Qveris Official
- 431 installs
- 20 repo stars
- Updated August 4, 2026
- qverisai/open-qveris-skills
qveris-official is a Claude Code skill from qverisai/open-qveris-skills that assists developers with AI and agent building tasks within the Qveris agent workflow ecosystem.
About
qveris-official is an agent skill published in the qverisai/open-qveris-skills repository for AI and agent building work. Public metadata is sparse—the listed description centers on helping with agent construction tasks rather than a single API or CLI—but the skill sits in an open skills pack aimed at Qveris-aligned development. Developers may reach for qveris-official when working inside Qveris agent projects and needing repository-backed guidance for assembling or extending agent capabilities. Because documentation in the catalog excerpt is thin, pair this skill with repository README files for concrete commands, file paths, and integration steps before relying on it in production pipelines.
- qveris-official
- AI & Agent Building
- AI-coding skill
Qveris Official by the numbers
- 431 all-time installs (skills.sh)
- Ranked #1,912 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/qverisai/open-qveris-skills --skill qveris-officialAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 431 |
|---|---|
| repo stars | ★ 20 |
| Last updated | August 4, 2026 |
| Repository | qverisai/open-qveris-skills ↗ |
How do you build AI agents with Qveris official skills?
Helps with ai & agent building tasks.
Who is it for?
Developers already in the Qveris open-qveris-skills ecosystem who need repository-backed agent-building guidance when catalog metadata alone is limited.
Skip if: Teams needing fully documented integration playbooks with verified commands and APIs when the Qveris repository README has not been reviewed for specifics.
When should I use this skill?
A developer works on Qveris agent building tasks and references qveris-official or the open-qveris-skills repository for AI agent workflows.
What you get
Agent-building guidance and workflow steps aligned with the Qveris open skills repository for AI agent tasks.
- Agent workflow guidance
Files
QVeris — Capability Discovery & Tool Calling for AI Agents
QVeris is a tool-finding and tool-calling engine, not an information search engine. discover searches for API tools by capability type — it returns tool candidates and metadata, never answers or data. call then runs the selected tool to get actual data.
discover answers "which API tool can do X?" — it cannot answer "what is the value of Y?" To look up facts, answers, or general information, use web_search instead.
Setup: Requires QVERIS_API_KEY from https://qveris.ai.
Credential: Only QVERIS_API_KEY is used. All requests go to https://qveris.ai/api/v1 over HTTPS.
---
Invocation Tiers
Check availability in order and use the first working tier:
Tier 1 — Native tools (most reliable): If qveris_discover and qveris_call tools are available in your environment, use them directly — skip all other tiers.
Tier 2 — `http_request` tool (universal fallback): Call the QVeris HTTP API directly using the http_request tool (see QVeris API Reference below). Available in all OpenClaw environments, including those where exec is disabled.
Tier 3 — Script execution: Run node {baseDir}/scripts/qveris_tool.mjs discover/call/inspect — only when {baseDir}/scripts/ directory is present and the exec tool with node are available.
Tier 4 — Web search: If all tiers above are unavailable, fall back to web_search for qualitative needs.
---
When and How to Use QVeris
Choosing the Right Tool
| Task type | Preferred approach | Reasoning |
|---|---|---|
| Computation, code, text manipulation, stable facts | Local / native | No external call needed |
| Structured/quantitative data (prices, rates, rankings, financials, time series, scientific data) | QVeris first | Returns structured JSON from professional APIs — more accurate than web pages |
| Historical data, reports, or sequences (earnings history, economic series, research datasets) | QVeris first | Professional APIs provide complete structured datasets; web pages give fragments |
| Non-native capability (image/video gen, OCR, TTS, translation, geocoding, web extraction, PDF) | QVeris first | These capabilities require external APIs; web search cannot perform them |
| Any task that local tools or other configured tools cannot fulfill | Discover via QVeris | QVeris aggregates thousands of tools — it may have what you need |
| No web search tool available in this environment | Discover web search tools via QVeris | Run discover "web search API" to find one, then call it — this is a two-step substitute, not a reason to send information queries to discover |
| Factual questions ("Is X listed?", "What is Y's stock symbol?", "Who founded Z?") | Web search | QVeris discover finds API tools, not answers — factual lookups need web_search |
| Qualitative information (opinions, documentation, tutorials, editorial content) | Web search first | Better served by browsing real pages and reading text |
| QVeris returned no useful results after a retry | Fall back to web search | Acceptable fallback for data tasks; mandatory for qualitative tasks |
Key distinction: QVeris discover finds API tools by capability type (e.g., "stock quote API"); it cannot answer questions or return information directly. For factual questions → web_search. For structured data → discover the right tool first, then call it. When in doubt, ask: "Am I looking for a tool or for information?"
Usage Flow
1. Discover: Find tool candidates for the capability you need. Write the query as an English tool type description (e.g., "stock quote real-time API"). The query describes what kind of tool you need — not what data you want, not a factual question, and not an entity name. 2. Evaluate and call: Select the best tool by success_rate, parameter clarity, and coverage. Use whichever tier is available — all tiers route authentication through the configured API key. 3. Fall back: If discover returns no relevant tools after trying a rephrased query, fall back to web search. Be transparent about the source. 4. When everything fails: Report which tools were tried and what errors occurred. Training-data values are not live results.
---
Tool Discovery Best Practices
Discovery Query Formulation
1. Describe the tool type, not the information you want — the query must describe an API capability, not a factual question or entity name:
- GOOD:
"China A-share real-time stock market data API"— describes a tool type - BAD:
"Zhipu AI stock symbol listing NASDAQ"— this is a factual question, use web_search - BAD:
"智谱AI 是否上市 股票代码"— this is a factual question in Chinese, use web_search - GOOD:
"company stock information lookup API"— describes a tool type - BAD:
"get AAPL price today"— this is a data request, not a tool description - GOOD:
"stock quote real-time API"— describes a tool type
2. Try multiple phrasings if the first discovery yields poor results — use synonyms, different domain terms, or adjusted specificity:
- First try:
"map routing directions"→ Retry:"walking navigation turn-by-turn API"
3. Convert non-English requests to English capability queries — user requests in any language must be converted to English tool type descriptions, not translated literally:
| User request | BAD discover query | GOOD discover query |
|---|---|---|
| "智谱AI是否上市" / "Is Zhipu AI listed?" | ~~"Zhipu AI stock symbol listing"~~ (factual question → use web_search) | "company stock information lookup API" |
| "腾讯最新股价" / "latest Tencent stock price" | ~~"Tencent latest stock price"~~ (data request) | "stock quote real-time API" |
| "港股涨幅榜" / "HK stock top gainers" | ~~"HK stock top gainers today"~~ (data request) | "hong kong stock market top gainers API" |
| "英伟达最新财报" / "Nvidia latest earnings" | ~~"Nvidia quarterly earnings data"~~ (data request) | "company earnings report API" |
| "文字生成图片" / "generate image from text" | ~~"generate a cat picture"~~ (task, not tool type) | "text to image generation API" |
| "今天北京天气" / "Beijing weather today" | ~~"Beijing weather today"~~ (data request) | "weather forecast API" |
Domains with Strong QVeris Coverage
Discover tools in these domains first — QVeris provides structured data or capabilities that web search cannot match:
- Financial/Company:
"stock price API","crypto market","forex rate","earnings report","financial statement" - Economics:
"GDP data","inflation statistics" - News/Social:
"news headlines","social media trending" - Blockchain:
"DeFi TVL","on-chain analytics" - Scientific/Medical:
"paper search API","clinical trials" - Weather/Location:
"weather forecast","air quality","geocoding","navigation" - Generation/Processing:
"text to image","TTS","OCR","video generation","PDF extraction" - Web extraction/Search:
"web content extraction","web scraping","web search API"
Known Tools Cache
After a successful discovery and call, note the tool_id and working parameters in session memory. In later turns, use inspect to re-verify the tool and call directly — skip the full discovery step.
---
Tool Selection and Parameters
Selection Criteria
When discover returns multiple tools, evaluate before selecting:
- Success rate: Prefer
success_rate>= 90%. Treat 70–89% as acceptable. Avoid < 70% unless no alternative exists. - Execution time: Prefer
avg_execution_time_ms< 5000 for interactive use. Compute-heavy tasks (image/video generation) may take longer. - Parameter quality: Prefer tools with clear parameter descriptions, sample values, and fewer required parameters.
- Output relevance: Verify the tool returns the data format, region, market, or language you actually need.
Before Calling a Tool
1. Read all parameter descriptions from the discovery results — note type, format, constraints, and defaults 2. Fill all required parameters and use the tool's sample parameters as a template for value structure 3. Validate types and formats: strings quoted ("London"), numbers unquoted (42), booleans (true/false); check date format (ISO 8601 vs timestamp), identifier format (ticker symbol vs full name), geo format (lat/lng vs city name) 4. Extract structured values from the user's request — do not pass natural language as a parameter value
---
Error Recovery
Failures are almost always caused by incorrect parameters, wrong types, or selecting the wrong tool — not by platform instability. Diagnose your inputs before concluding a tool is broken.
Attempt 1 — Fix parameters: Read the error message. Check types and formats. Fix and retry.
Attempt 2 — Simplify: Drop optional parameters. Try standard values (e.g., well-known ticker). Retry.
Attempt 3 — Switch tool: Select the next-best tool from discovery results. Call with appropriate parameters.
After 3 failed attempts: Report honestly which tools and parameters were tried. Fall back to web search for data needs (mark the source).
---
Large Result Handling
Some tool calls may return full_content_file_url when the inline result is too large for the normal response body.
- Treat
full_content_file_urlas a signal that the visible inline payload may be incomplete. - Conclusions drawn from
truncated_contentalone when a full-content URL is present may be incomplete. - If your environment already has an approved way to retrieve the full content, use that separate tool or workflow.
- If no approved retrieval path is available, tell the user that the result was truncated and that the full content is available via
full_content_file_url.
---
QVeris API Reference
Use these endpoints when calling via http_request tool (Tier 2).
Base URL: https://qveris.ai/api/v1
Required headers (on every request):
Authorization: Bearer ${QVERIS_API_KEY}
Content-Type: application/jsonDiscover tools
POST /search
Body: {"query": "stock quote real-time API", "limit": 10}Response contains search_id (required for the subsequent call) and a results array — each item has tool_id, success_rate, avg_execution_time_ms, and parameters.
Call a tool
POST /tools/execute?tool_id=<tool_id>
Body: {"search_id": "<from discover>", "parameters": {"symbol": "AAPL"}, "max_response_size": 20480}Response contains result, success, error_message, elapsed_time_ms.
Inspect tool details
POST /tools/by-ids
Body: {"tool_ids": ["<tool_id>"], "search_id": "<optional>"}---
Quick Start
Tier 1 — Native tools (if available)
Use qveris_discover and qveris_call directly when present in your tool list.
Tier 2 — http_request tool
Step 1 — Discover:
{
"method": "POST",
"url": "https://qveris.ai/api/v1/search",
"headers": {"Authorization": "Bearer ${QVERIS_API_KEY}", "Content-Type": "application/json"},
"body": {"query": "weather forecast API", "limit": 10}
}Step 2 — Call (use tool_id and search_id from step 1):
{
"method": "POST",
"url": "https://qveris.ai/api/v1/tools/execute?tool_id=openweathermap.weather.execute.v1",
"headers": {"Authorization": "Bearer ${QVERIS_API_KEY}", "Content-Type": "application/json"},
"body": {"search_id": "<from step 1>", "parameters": {"city": "London", "units": "metric"}, "max_response_size": 20480}
}Tier 3 — Script execution (if {baseDir}/scripts/ is present)
node {baseDir}/scripts/qveris_tool.mjs discover "weather forecast API"
node {baseDir}/scripts/qveris_tool.mjs call openweathermap.weather.execute.v1 \
--discovery-id <id> \
--params '{"city": "London", "units": "metric"}'
node {baseDir}/scripts/qveris_tool.mjs inspect openweathermap.weather.execute.v1---
Quick Reference
Self-Check (before responding)
- Is my discover query a tool type description or a factual question / entity name? → If it contains specific company names, "is X listed?", or "what is Y?" — use web_search instead. Discover finds tools, not information.
- Am I about to state a live number or need an external capability? → Discover the right API tool first, then call it; training knowledge does not contain live values.
- Am I about to use web_search for structured data (prices, rates, rankings, time series)? → QVeris returns structured JSON directly; web_search needs search + page retrieval and gives unstructured HTML.
- Am I about to give up or skip QVeris because it failed earlier? → Re-engage. Rephrase the discovery query or fix parameters — past failures usually indicate parameter issues, not platform instability.
- Did the call result include
full_content_file_url? → Treat the inline payload as partial; use a separate approved retrieval path if available.
Common Mistakes
| Mistake | Example | Fix |
|---|---|---|
| Passing factual questions to discover | "Zhipu AI stock symbol listing NASDAQ" or "智谱AI 是否上市" | Discover finds tools, not answers. Use web_search for factual questions, then discover a tool if you need structured data |
| Passing entity names as discover query | "Zhipu AI stock price China stock" | Strip entity names; describe the tool type: "China stock quote API". Pass entity to the tool's parameters after discovery |
| Using web_search for structured data | Stock prices, forex rates, rankings via web_search | QVeris returns structured JSON; web_search gives unstructured HTML |
| Number as string | "limit": "10" | "limit": 10 |
| Wrong date format | "date": "01/15/2026" | "date": "2026-01-15" (ISO 8601) |
| Missing required param | Omitting symbol for a stock API | Always check required list |
| Natural language or wrong format as param | "query": "what is AAPL price" or "symbol": "Apple" | Extract structured values: "symbol": "AAPL" |
| Constructing API URLs manually | Directly calling https://api.qveris.com/... | Use the API reference above or the script |
| Giving up after one failure | "I don't have real-time data" / abandoning after error | Discover first; follow Error Recovery on failure |
| Not trying http_request when exec fails | Abandoning when node/exec is unavailable | Use http_request tool (Tier 2) — it works without exec |
Install Qveris Official Skill
Prerequisites
A valid QVERIS_API_KEY is required. If the user has not provided one, stop and ask:
Please provide your QVERIS_API_KEY. You can obtain one from https://qveris.ai
- Confirm Node.js 18+ is available.
- Confirm the user has provided a valid
QVERIS_API_KEY.
Steps
Install Flow
1. Remove existing qveris skills
Before installing, remove any existing skills whose directory name starts with qveris under the skills folder.
Bash (Linux / macOS / WSL):
rm -rf ~/.openclaw/skills/qveris*PowerShell (Windows):
Remove-Item -Recurse -Force "$env:USERPROFILE\.openclaw\skills\qveris*" -ErrorAction SilentlyContinue2. Download the full skill directory
Download the qveris-official skill definition and scripts from https://qveris.ai/skill so that all helper modules remain together.
Bash (Linux / macOS / WSL):
mkdir -p ~/.openclaw/skills/qveris-official/scripts
curl -fSL https://qveris.ai/skill/SKILL.md -o ~/.openclaw/skills/qveris-official/SKILL.md
curl -fSL https://qveris.ai/skill/scripts/qveris_tool.mjs -o ~/.openclaw/skills/qveris-official/scripts/qveris_tool.mjs
curl -fSL https://qveris.ai/skill/scripts/qveris_client.mjs -o ~/.openclaw/skills/qveris-official/scripts/qveris_client.mjs
curl -fSL https://qveris.ai/skill/scripts/qveris_env.mjs -o ~/.openclaw/skills/qveris-official/scripts/qveris_env.mjsPowerShell (Windows):
New-Item -ItemType Directory -Force -Path "$env:USERPROFILE\.openclaw\skills\qveris-official\scripts"
Invoke-WebRequest -Uri "https://qveris.ai/skill/SKILL.md" -OutFile "$env:USERPROFILE\.openclaw\skills\qveris-official\SKILL.md"
Invoke-WebRequest -Uri "https://qveris.ai/skill/scripts/qveris_tool.mjs" -OutFile "$env:USERPROFILE\.openclaw\skills\qveris-official\scripts\qveris_tool.mjs"
Invoke-WebRequest -Uri "https://qveris.ai/skill/scripts/qveris_client.mjs" -OutFile "$env:USERPROFILE\.openclaw\skills\qveris-official\scripts\qveris_client.mjs"
Invoke-WebRequest -Uri "https://qveris.ai/skill/scripts/qveris_env.mjs" -OutFile "$env:USERPROFILE\.openclaw\skills\qveris-official\scripts\qveris_env.mjs"3. Set the key for the current session
Use a session-scoped variable first. Do not modify shell startup files (~/.bashrc, ~/.zshrc, or PowerShell $PROFILE) unless the user explicitly asks for persistent setup.
Bash (Linux / macOS / WSL):
curl -fSL https://qveris.ai/skills/qveris-official/SKILL.md -o ~/.openclaw/skills/qveris-official/SKILL.mdPowerShell (Windows):
$env:QVERIS_API_KEY = "<USER_PROVIDED_KEY>"4. Verify the installed files
All of these files should exist after the download.
Bash (Linux / macOS / WSL):
curl -fSL https://qveris.ai/skills/qveris-official/scripts/qveris_env.mjs -o ~/.openclaw/skills/qveris-official/scripts/qveris_env.mjs
curl -fSL https://qveris.ai/skills/qveris-official/scripts/qveris_client.mjs -o ~/.openclaw/skills/qveris-offical/scripts/qveris_client.mjs
curl -fSL https://qveris.ai/skills/qveris-official/scripts/qveris_tool.mjs -o ~/.openclaw/skills/qveris-official/scripts/qveris_tool.mjsPowerShell (Windows):
@("SKILL.md", "scripts\qveris_tool.mjs", "scripts\qveris_client.mjs", "scripts\qveris_env.mjs") | ForEach-Object {
$file = Split-Path $_ -Leaf
if (Test-Path "$env:USERPROFILE\.openclaw\skills\qveris-official\$_") { Write-Host "$file OK" } else { Write-Host "$file MISSING" }
}
if ($env:QVERIS_API_KEY) { Write-Host "QVERIS_API_KEY is set" } else { Write-Host "QVERIS_API_KEY is NOT set" }5. Verify the runtime entrypoint
Run the local CLI help and one discovery command.
Bash (Linux / macOS / WSL):
echo 'export QVERIS_API_KEY="<USER_PROVIDED_KEY>"' >> ~/.bashrc
source ~/.bashrcPowerShell (Windows):
node "$env:USERPROFILE\.openclaw\skills\qveris-official\scripts\qveris_tool.mjs" --help
node "$env:USERPROFILE\.openclaw\skills\qveris-official\scripts\qveris_tool.mjs" discover "stock price API" --limit 3The help output should mention discover, call, and inspect. The discovery command should return tool candidates and a discovery ID.
Behavior Rules
test -f ~/.openclaw/skills/qveris-official/SKILL.md && echo "SKILL.md OK" || echo "SKILL.md MISSING"
test -f ~/.openclaw/skills/qveris-official/scripts/qveris_env.mjs && echo "qveris_env.mjs OK" || echo "qveris_env.mjs MISSING"
test -f ~/.openclaw/skills/qveris-official/scripts/qveris_client.mjs && echo "qveris_client.mjs OK" || echo "qveris_client.mjs MISSING"
test -f ~/.openclaw/skills/qveris-official/scripts/qveris_tool.mjs && echo "qveris_tool.mjs OK" || echo "qveris_tool.mjs MISSING"
echo "QVERIS_API_KEY=${QVERIS_API_KEY:+is set}"
node ~/.openclaw/skills/qveris-official/scripts/qveris_tool.mjs discover "stock price" --limit 5For Bash / Zsh, the target file is typically ~/.bashrc or ~/.zshrc. For PowerShell, the target file is $PROFILE and the line would be:
$env:QVERIS_API_KEY = "<USER_PROVIDED_KEY>"Default behavior is non-persistent session setup.
QVeris Official
Official QVeris skill documentation for OpenClaw and other AI agents.
QVeris is a capability discovery and tool calling engine. Use discover to find specialized external API tools, then use call to run the selected tool through QVeris.
What It Does
- Discover tools for real-time data, historical sequences, structured reports, web extraction, PDF workflows, OCR, TTS, translation, image/video generation, and more
- Route all requests through the QVeris API instead of constructing provider-specific endpoints
- Return structured JSON results suitable for agent workflows
- Use only built-in Node.js web APIs plus local helper modules
Requirements
- Node.js 18+
QVERIS_API_KEY
Set the API key in your current shell session:
export QVERIS_API_KEY="your-api-key-here"Install
Option 1: Via ClawHub (recommended)
npx clawhub install qveris-officialOption 2: Manual
Copy the full qveris-official/ directory into your OpenClaw skills directory so the helper modules remain next to qveris_tool.mjs:
cp -R qveris-official ~/.openclaw/skills/Usage
Manual usage follows a two-step flow: discover first, then call.
discover returns tool candidates and metadata, not final data results. call returns the execution result.
# Discover tools for a capability
node scripts/qveris_tool.mjs discover "stock price API"
node scripts/qveris_tool.mjs discover "text to image generation API" --limit 5
# Call the selected tool
node scripts/qveris_tool.mjs call <tool_id> --discovery-id <discovery_id> --params '{"symbol": "AAPL"}'
# Inspect a known tool by ID
node scripts/qveris_tool.mjs inspect <tool_id>
# Output raw JSON
node scripts/qveris_tool.mjs discover "weather forecast API" --json
node scripts/qveris_tool.mjs call <tool_id> --discovery-id <discovery_id> --params '{"city": "London"}' --jsonNotes
- Write discovery queries as English capability descriptions for best results
- Do not construct API URLs manually; use the provided script
- Keep sensitive credentials and PII out of discovery queries and tool parameters
License
MIT
const BASE_URL = "https://qveris.ai/api/v1";
async function requestJson(path, { method = "POST", query = {}, body, timeoutMs = 30000, apiKey }) {
const controller = new AbortController();
const timeout = setTimeout(() => controller.abort(), timeoutMs);
try {
const url = new URL(`${BASE_URL}${path}`);
for (const [key, value] of Object.entries(query)) {
if (value !== undefined && value !== null) {
url.searchParams.set(key, String(value));
}
}
const response = await fetch(url.toString(), {
method,
headers: {
Authorization: `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
...(body === undefined ? {} : { body: JSON.stringify(body) }),
signal: controller.signal,
});
if (!response.ok) {
const text = await response.text();
throw new Error(`HTTP ${response.status}: ${text}`);
}
return await response.json();
} finally {
clearTimeout(timeout);
}
}
export function getBaseUrl() {
return BASE_URL;
}
export async function discoverTools({ apiKey, query, limit = 10, timeoutMs = 30000 }) {
return requestJson("/search", {
apiKey,
body: { query, limit },
timeoutMs,
});
}
export async function inspectToolsByIds({ apiKey, toolIds, discoveryId, timeoutMs = 30000 }) {
const body = { tool_ids: toolIds };
if (discoveryId) {
body.search_id = discoveryId;
}
return requestJson("/tools/by-ids", {
apiKey,
body,
timeoutMs,
});
}
export async function callTool({
apiKey,
toolId,
discoveryId,
parameters,
maxResponseSize = 20480,
timeoutMs = 120000,
}) {
return requestJson("/tools/execute", {
apiKey,
query: { tool_id: toolId },
body: {
search_id: discoveryId,
parameters,
max_response_size: maxResponseSize,
},
timeoutMs,
});
}
export function readQverisApiKey() {
const apiKey = process.env.QVERIS_API_KEY;
if (!apiKey) {
console.error("Error: QVERIS_API_KEY environment variable not set");
console.error("Get your API key at https://qveris.ai");
process.exit(1);
}
return apiKey;
}
#!/usr/bin/env node
/**
* QVeris Capability Discovery & Tool Calling CLI
*
* Discover tools by capability and call them through QVeris.
* Uses local modules and built-in Node.js web APIs only.
*
* SECURITY MANIFEST:
* Credential used: QVERIS_API_KEY (only)
* External endpoint: https://qveris.ai/api/v1 (only)
* Local file reads: none
* Local file writes: none
*
* Usage:
* node scripts/qveris_tool.mjs discover "weather forecast"
* node scripts/qveris_tool.mjs call <tool_id> --discovery-id <id> --params '{"city": "London"}'
* node scripts/qveris_tool.mjs inspect <tool_id1> [tool_id2 ...]
*/
import { readQverisApiKey } from "./qveris_env.mjs";
import { callTool, discoverTools, getBaseUrl, inspectToolsByIds } from "./qveris_client.mjs";
function normalizeLegacyArgs(rawArgs) {
const args = [...rawArgs];
const warnings = new Set();
const commandAliases = {
search: "discover",
execute: "call",
invoke: "call",
"get-by-ids": "inspect",
};
if (args.length > 0 && commandAliases[args[0]]) {
warnings.add(`'${args[0]}' is deprecated; use '${commandAliases[args[0]]}' instead.`);
args[0] = commandAliases[args[0]];
}
for (let i = 0; i < args.length; i++) {
if (args[i] === "--search-id") {
warnings.add("'--search-id' is deprecated; use '--discovery-id' instead.");
args[i] = "--discovery-id";
}
}
return { args, warnings: [...warnings] };
}
function displayDiscoveryResults(result) {
const discoveryId = result.search_id ?? "N/A";
const tools = result.results ?? [];
const total = result.total ?? tools.length;
console.log(`\nDiscovery ID: ${discoveryId}`);
console.log(`Found ${total} tools\n`);
if (tools.length === 0) {
console.log("No tools found.");
return;
}
for (let i = 0; i < tools.length; i++) {
const tool = tools[i];
const toolId = tool.tool_id ?? "N/A";
const name = tool.name ?? "N/A";
const desc = tool.description ?? "N/A";
const stats = tool.stats ?? {};
let successRate = stats.success_rate ?? "N/A";
let avgTime = stats.avg_execution_time_ms ?? "N/A";
if (typeof successRate === "number") {
successRate = `${Math.round(successRate * 100)}%`;
}
if (typeof avgTime === "number") {
avgTime = `${avgTime.toFixed(1)}ms`;
}
console.log(`[${i + 1}] ${name}`);
console.log(` ID: ${toolId}`);
console.log(` ${desc.length > 100 ? desc.slice(0, 100) + "..." : desc}`);
console.log(` Success: ${successRate} | Avg Time: ${avgTime}`);
const params = tool.params ?? [];
if (params.length > 0) {
const required = params.filter((p) => p.required).map((p) => p.name);
const optional = params.filter((p) => !p.required).map((p) => p.name);
if (required.length > 0) {
console.log(` Required: ${required.join(", ")}`);
}
if (optional.length > 0) {
const shown = optional.slice(0, 5).join(", ");
console.log(` Optional: ${shown}${optional.length > 5 ? "..." : ""}`);
}
}
const examples = tool.examples ?? {};
if (examples.sample_parameters) {
console.log(` Example: ${JSON.stringify(examples.sample_parameters)}`);
}
console.log();
}
}
function displayCallResult(result) {
const success = result.success ?? false;
const execTime = result.elapsed_time_ms ?? "N/A";
const cost = result.cost ?? 0;
console.log(`\n${success ? "Success" : "Failed"}`);
console.log(`Time: ${execTime}ms | Cost: ${cost}`);
if (!success) {
const error = result.error_message ?? "Unknown error";
console.log(`Error: ${error}`);
}
const data = result.result ?? {};
const fullContentUrl = typeof data.full_content_file_url === "string" ? data.full_content_file_url : null;
if (fullContentUrl) {
console.log("\nLarge result notice:");
console.log(" The inline payload may be incomplete.");
console.log(` Full content URL: ${fullContentUrl}`);
console.log(" Use a separate approved retrieval path if your environment has one.");
const { truncated_content, full_content_file_url, ...displayData } = data;
if (Object.keys(displayData).length > 0) {
console.log("\nResult (truncated_content omitted — use the URL above for complete data):");
console.log(JSON.stringify(displayData, null, 2));
}
} else if (Object.keys(data).length > 0) {
console.log("\nResult:");
console.log(JSON.stringify(data, null, 2));
}
}
function printUsage() {
const baseUrl = getBaseUrl();
console.log(`QVeris Capability Discovery & Tool Calling CLI
Usage:
node scripts/qveris_tool.mjs discover <query> [options]
node scripts/qveris_tool.mjs call <tool_id> --discovery-id <id> [options]
node scripts/qveris_tool.mjs inspect <tool_id> [tool_id2 ...] [options]
Commands:
discover <query> Discover tool candidates for a capability description
call <tool_id> Call the selected tool through QVeris
inspect <id> [id2 ...] Inspect tool details before reuse or calling
Notes:
discover returns tool candidates and metadata, not final data results
call returns the execution result
all requests are routed to ${baseUrl}
Options:
--limit N Max results for discover (default: 10)
--discovery-id ID Discovery ID from previous discover (required for call, optional for inspect)
--params JSON Tool parameters as JSON string (default: "{}")
--max-size N Max response size in bytes (default: 20480)
--timeout N Request timeout in seconds (default: 30 for discover/inspect, 60 for call)
--json Output raw JSON instead of formatted display
--help Show this help message
Examples:
node scripts/qveris_tool.mjs discover "weather forecast API"
node scripts/qveris_tool.mjs call openweathermap.weather.execute.v1 --discovery-id abc123 --params '{"city": "London"}'
node scripts/qveris_tool.mjs inspect openweathermap.weather.execute.v1`);
}
function parseArgs(argv) {
const normalized = normalizeLegacyArgs(argv.slice(2));
const args = normalized.args;
if (args.length === 0 || args.includes("--help") || args.includes("-h")) {
printUsage();
process.exit(0);
}
for (const warning of normalized.warnings) {
console.error(`Deprecated: ${warning}`);
}
const command = args[0];
const parsed = { command, json: false };
if (command === "discover") {
if (args.length < 2) {
console.error("Error: discover command requires a query argument");
process.exit(1);
}
parsed.query = args[1];
parsed.limit = 10;
parsed.timeout = 30;
for (let i = 2; i < args.length; i++) {
if (args[i] === "--limit" && i + 1 < args.length) {
parsed.limit = parseInt(args[++i], 10);
} else if (args[i] === "--timeout" && i + 1 < args.length) {
parsed.timeout = parseInt(args[++i], 10);
} else if (args[i] === "--json") {
parsed.json = true;
}
}
} else if (command === "call") {
if (args.length < 2) {
console.error("Error: call command requires a tool_id argument");
process.exit(1);
}
parsed.toolId = args[1];
parsed.discoveryId = null;
parsed.params = "{}";
parsed.maxSize = 20480;
parsed.timeout = 60;
for (let i = 2; i < args.length; i++) {
if (args[i] === "--discovery-id" && i + 1 < args.length) {
parsed.discoveryId = args[++i];
} else if (args[i] === "--params" && i + 1 < args.length) {
parsed.params = args[++i];
} else if (args[i] === "--max-size" && i + 1 < args.length) {
parsed.maxSize = parseInt(args[++i], 10);
} else if (args[i] === "--timeout" && i + 1 < args.length) {
parsed.timeout = parseInt(args[++i], 10);
} else if (args[i] === "--json") {
parsed.json = true;
}
}
if (!parsed.discoveryId) {
console.error("Error: --discovery-id is required for call command");
process.exit(1);
}
} else if (command === "inspect") {
if (args.length < 2) {
console.error("Error: inspect command requires at least one tool_id argument");
process.exit(1);
}
parsed.toolIds = [];
parsed.discoveryId = null;
parsed.timeout = 30;
for (let i = 1; i < args.length; i++) {
if (args[i] === "--discovery-id" && i + 1 < args.length) {
parsed.discoveryId = args[++i];
} else if (args[i] === "--timeout" && i + 1 < args.length) {
parsed.timeout = parseInt(args[++i], 10);
} else if (args[i] === "--json") {
parsed.json = true;
} else if (!args[i].startsWith("--")) {
parsed.toolIds.push(args[i]);
}
}
if (parsed.toolIds.length === 0) {
console.error("Error: inspect command requires at least one tool_id argument");
process.exit(1);
}
} else {
console.error(`Error: unknown command '${command}'. Use 'discover', 'call', or 'inspect'.`);
process.exit(1);
}
return parsed;
}
async function main() {
const args = parseArgs(process.argv);
const apiKey = readQverisApiKey();
try {
if (args.command === "discover") {
const result = await discoverTools({
apiKey,
query: args.query,
limit: args.limit,
timeoutMs: args.timeout * 1000,
});
if (args.json) {
console.log(JSON.stringify(result, null, 2));
} else {
displayDiscoveryResults(result);
}
} else if (args.command === "call") {
let params;
try {
params = JSON.parse(args.params);
} catch (e) {
console.error(`Invalid JSON in --params: ${e.message}`);
process.exit(1);
}
const result = await callTool({
apiKey,
toolId: args.toolId,
discoveryId: args.discoveryId,
parameters: params,
maxResponseSize: args.maxSize,
timeoutMs: args.timeout * 1000,
});
if (args.json) {
console.log(JSON.stringify(result, null, 2));
} else {
displayCallResult(result);
}
} else if (args.command === "inspect") {
const result = await inspectToolsByIds({
apiKey,
toolIds: args.toolIds,
discoveryId: args.discoveryId,
timeoutMs: args.timeout * 1000,
});
if (args.json) {
console.log(JSON.stringify(result, null, 2));
} else {
displayDiscoveryResults(result);
}
}
} catch (e) {
if (e.name === "AbortError") {
console.error("Error: Request timed out");
} else {
console.error(`Error: ${e.message}`);
}
process.exit(1);
}
}
main();
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Treat qveris-official as a Qveris ecosystem entry point; prefer domain-specific skills when the task has detailed docs outside the thin official catalog description.
FAQ
What is qveris-official for?
qveris-official is a skill in qverisai/open-qveris-skills that helps with AI and agent building tasks. Catalog text is brief; developers should read the repository README for concrete setup, files, and commands.
Where is qveris-official published?
qveris-official lives in the qverisai/open-qveris-skills GitHub repository as part of the open Qveris skills collection aimed at agent developers.
Is qveris-official a rendering or CRM skill?
qveris-official is not a video or CRM integration skill. Its stated scope is AI and agent building within the Qveris skills ecosystem rather than media pipelines or sales data sync.