
Agent Team MCP
- Updated April 20, 2026
- RichardLemmon/AgentTeam
Agent Team is a MCP server that runs 13 specialized agents with 44 tools over SQLite via a one-command npm stdio install.
About
Agent Team MCP packages a full miniature agency as a local Model Context Protocol server: thirteen specialized agents, forty-four tools, and SQLite as the shared handoff layer so roles can pass context without you duct-taping separate prompts. developers install the npm package once, wire stdio into the MCP client, and treat the server as agent tooling that can also support Ship review passes and Operate iteration when you re-run specialist agents against the same task store. Complexity is intermediate to advanced—you are orchestrating many tools, not calling one API. Compared to AgentRamp or SocialCrawl, this is an on-machine multi-agent runtime, not URL-generated remotes or external data feeds. Best when your product workflow genuinely splits across research, implementation, and QA-style roles and you want that structure callable as MCP tools rather than ad-hoc subagents in chat.
- 13 specialized AI agents collaborating through a shared SQLite coordination layer
- 44 MCP tools exposed via one-command npm install (agent-team-mcp, stdio transport)
- Version 1.5.1; repository AgentTeam on GitHub
- Local stdio MCP—no hosted remote URL in the published server manifest
- Useful when you want persistent multi-role workflows inside Claude Code or Cursor instead of one monolithic assistant
Agent Team MCP by the numbers
- Data as of Aug 10, 2026 (Skillselion catalog sync)
claude mcp add agent-team-mcp -- npx -y agent-team-mcpAdd your badge
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| Package | agent-team-mcp |
|---|---|
| Transport | STDIO |
| Auth | None |
| Last updated | April 20, 2026 |
| Repository | RichardLemmon/AgentTeam ↗ |
What it does
Run a local multi-agent crew with 44 MCP tools and SQLite-backed coordination from a single npm install when one chat thread is not enough structure.
Who is it for?
Best when you want a structured local agent crew with many MCP tools for complex build-and-review loops.
Skip if: Simple single-tool integrations, fully managed cloud agent fleets, or beginners who only need one HTTP API bridge.
What you get
After install, your MCP client can invoke a coordinated agent team with dozens of tools and SQLite-backed collaboration instead of reinventing multi-agent wiring each sprint.
- Running stdio MCP server with 13 agent roles and 44 tools
- SQLite-backed collaboration surface for multi-step agent workflows
By the numbers
- 13 specialized AI agents
- 44 MCP tools
- SQLite collaboration backend
README.md
AgentTeam
AgentTeam is a reusable AI software development team built on the Model Context Protocol (MCP). Thirteen specialized agents — Product Manager, Project Manager, UX Researcher, UX/UI Designer, Frontend, Backend, Full-Stack, Mobile, DevOps, QA, Security, Data Engineer, and Data Scientist — collaborate on software projects through a shared SQLite database, each constrained strictly to their role.
The Project Manager orchestrates: it creates the project, recruits the specialists it needs, breaks work into tasks, and returns a dispatch manifest — a JSON array that the calling session uses to spawn each specialist as an independent parallel agent. Specialists read the project summary on joining, log their work and decisions as they go, and share structured research artifacts so no agent re-researches what another has already found.
All project state is persisted in SQLite (44 MCP tools across 12 domains: projects, summaries, team members, tasks, work entries, task comments, discussions, decisions, artifacts, and a user journal). Projects are UUID-scoped and lifecycle-managed (active → paused → archived → closed), so teams can pause and resume work across sessions without losing context.
Designed to be called from any Claude Code project via MCP — point your claude_desktop_config.json at the server and any project can spin up a full team.
User Journal
As the team works, the Project Manager captures your decisions, preferences, and reasoning from the conversation into a persistent user journal — things like devices considered and rejected, cost constraints, form factor preferences, and next-step intentions. These are stored as structured entries scoped to the project (or globally, for cross-project preferences) and reviewed at close-out so nothing important is lost between sessions. The journal is queryable via list_journal_entries so future agents can read what past conversations established before starting new work.
Project Structure
AgentTeam/
├── agents/ # Agent prompt files — one per role
│ ├── _base-protocol.md # Shared team protocol, constraints, efficiency rules
│ ├── project-manager.md
│ ├── product-manager.md
│ ├── backend-developer.md
│ └── ...
├── mcp-server/ # TypeScript MCP server
│ └── src/
│ ├── index.ts # Server entry — all 44 tools registered
│ ├── db/
│ │ ├── schema.ts # Table definitions and migrations
│ │ └── connection.ts
│ └── tools/ # One file per domain
└── docs/ # Design specs and reference guides
MCP Tool Domains
| Domain | Tools |
|---|---|
| Projects | create_project, get_project, update_project_status, list_projects, delete_project |
| Summaries | update_project_summary, get_project_summary, get_summary_version, list_summary_history |
| Team Members | add_team_member, remove_team_member, list_team_members |
| Tasks | create_task, update_task, get_task, list_tasks |
| Work Entries | log_work, get_my_work, get_work_history |
| Task Comments | add_task_comment, list_task_comments, list_my_comments |
| Discussions | create_discussion, add_discussion_participant, add_discussion_message, update_discussion_summary, get_discussion, list_discussions |
| Decisions | log_decision, list_decisions, get_decision |
| Artifacts | share_artifact, update_artifact, list_artifacts, get_artifact |
| Team Protocol | get_team_protocol |
| User Journal | log_journal_entry, list_journal_entries |
| User Questions | ask_user_question, list_user_questions, answer_user_question |
| Expansion Requests | request_team_expansion, list_expansion_requests, resolve_expansion_request |
Getting Started
1. Install the MCP server
One command (recommended):
claude mcp add agent-team -- npx agent-team-mcp
That's it. Claude Code will launch the server automatically, and the /team skill is installed globally on first run.
Or manually edit your MCP config (~/.claude/settings.json or project .claude/settings.json):
{
"mcpServers": {
"agent-team": {
"command": "npx",
"args": ["agent-team-mcp"]
}
}
}
Or from a local clone:
git clone https://github.com/RichardLemmon/AgentTeam.git
cd AgentTeam/mcp-server
npm install
npm run build
claude mcp add agent-team -- node /path/to/AgentTeam/mcp-server/dist/index.js
Token-Efficient Architecture
Agent prompt files contain only the role-specific Identity section (~100 words each). Shared team protocol, constraints, and efficiency rules live in a single agents/_base-protocol.md file, served on demand via the get_team_protocol MCP tool. This lazy-loading approach saves ~6,000 words of context when spawning a full team compared to duplicating the protocol in every agent file. The artifact JSON schema is embedded in the share_artifact tool description so agents discover it from the tool itself.
2. Use it
The /team skill is automatically installed to ~/.claude/skills/agent-team/ on first server startup. Just type:
/team build me a REST API for task management
Or use /team with no arguments to see your existing projects and pick one to work on.
Quick Start
With the /team skill (Claude Code):
/team build me a REST API for task management
Without the skill:
"Spin up the Project Manager and ask them to investigate [subject]"
Manage projects:
/team --projects # list all projects
/team --projects active # filter by status
/team --projects delete <name> # delete a project
How It Works
- PM sets up the project — creates the project record, recruits the specialists it needs, creates tasks, writes the project summary, and returns a dispatch manifest.
- Calling session spawns specialists — each specialist in the manifest is launched as an independent agent with its
project_idandmember_id. - Specialists work in parallel — each reads the project summary, logs work entries, shares artifacts, and communicates via task comments and discussions.
- State persists across sessions — any agent can rejoin a project by reading the current summary and picking up where the team left off.
- PM closes out — on completion, the PM writes a close-out summary and logs key user decisions and preferences to the journal for future reference.
Recommended MCP Servers
How it compares
Local multi-agent MCP runtime with 44 tools, not a hosted SaaS MCP generator or a social data API.
FAQ
Who is Agent Team MCP for?
Developers and small teams using MCP clients who need multiple specialized agents and shared state without building orchestration from scratch.
When should I use Agent Team MCP?
Use it during Build for agent-tooling workflows, and optionally in Ship review or Operate iterate when you want the same crew to re-run structured tasks.
How do I add Agent Team MCP to my agent?
Install the npm package agent-team-mcp (v1.5.1), add it as a stdio MCP server in Claude Code, Cursor, or Codex, and start the process from your client’s MCP configuration.