Agentic Research
Attach a self-verifying research engine to your coding agent so competitive, technical, and market questions get planned, retrieved, and checked before you commit to a build.
Overview
io.github.TheAiSingularity/agentic-research is an Idea-phase MCP server that runs a multi-step local research pipeline with a final verify node so agents return checked answers, optionally via Ollama at $0 per query.
What is this MCP server?
- Multi-node research pipeline: plan, classify, synthesize, critic, compress, and verify before answers surface to the age
- Runs against any OpenAI-compatible API or local Ollama (e.g. Gemma 3 4B) with API key sentinel `ollama` for $0/query loc
- PyPI stdio package `agentic-research-mcp` (v0.1.3) with separate MODEL_SYNTHESIZER and MODEL_PLANNER env controls
- Configurable EMBED_MODEL for retrieval and memory across research sessions
- stdio MCP transport fits Claude Code, Cursor, and other agents without a separate HTTP service
- MCP server version 0.1.3 on registry schema 2025-12-11
- stdio transport via PyPI identifier agentic-research-engine / runtimeHint agentic-research-mcp
- Documented pipeline roles: synthesize, plan, classify, critic, compress, and verify nodes
What problem does it solve?
Agent chat gives confident research summaries that are hard to trust and expensive to re-run when you are still deciding what to build.
Who is it for?
Indie builders doing competitor, stack, and audience research inside Claude Code or Cursor who want verification and optional fully local Ollama (Gemma 3 4B) runs.
Skip if: Teams that need managed web search APIs only, production observability, or code review—this is research orchestration, not CI or monitoring.
What do I get? / Deliverables
Your agent can execute a planned, retrieved, and verified research pass and hand you a compressed answer you can use for scope and positioning decisions.
- Agent-invokable research runs with plan-through-verify orchestration
- Verified, compressed research summaries in the agent session
- Retrieval-backed memory across related discovery questions when embeddings are configured
Recommended MCP Servers
Journey fit
Solo builders need trustworthy answers while still in discovery; this MCP server is a research-and-verification stack, which maps to the Idea phase before scope and prototype work. The server’s plan–classify–synthesize–critic–compress–verify loop is classic research workflow: gather context, stress-test claims, and return cited-style conclusions—not shipping or growth automation.
How it compares
MCP research engine with an explicit verify node—not a single prompt skill or a generic web-search plugin.
Common Questions / FAQ
Who is io.github.TheAiSingularity/agentic-research for?
Solo and indie builders using AI coding agents who want deeper, self-checked research during discovery without always paying per cloud query.
When should I use io.github.TheAiSingularity/agentic-research?
Use it in the Idea phase when you need market, technical, or competitive answers verified before you lock scope, pricing, or architecture.
How do I add io.github.TheAiSingularity/agentic-research to my agent?
Install the PyPI package `agentic-research-mcp` (v0.1.3), register the stdio MCP server in Claude Code or Cursor, and set OPENAI_BASE_URL, OPENAI_API_KEY, MODEL_SYNTHESIZER, MODEL_PLANNER, and EMBED_MODEL for cloud or Ollama.