How do I use lead-agent-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@lead-agent-mcp ask lead: Should the auth service use JWT or session tokens?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

lead-agent-mcp

by JiHungLin

Overview Schema Related Servers Score Discussions

Python

Remote

Lead Agent MCP Server

A lightweight MCP server that lets distributed AI agent teams query a designated project lead before making design decisions — preventing divergence before it happens.

Problem

When multiple developers each use their own AI coding agent, every agent builds its own understanding of the project. Without a shared source of truth, agents silently make incompatible design decisions. By the time a code review catches it, the divergence is already expensive to fix.

Related MCP server: MCP Plus

How it works

Developer's Claude Code (any machine)
  │
  │  call ask_lead("Should bt-engine be a separate container?", ...)
  ▼
Lead Agent MCP Server  ──────────────────────────────────────────┐
  │                                                               │
  ├── Matching standing decision found → answer immediately       │
  │                                                               │
  └── No match → queue for human review                          │
        │                                                         │
        │  Developer's agent polls check_answer(question_id)      │
        │  and blocks progress until answered                     │
        ▼                                                         │
Lead's Web UI  (http://localhost:8080)  ◄────────────────────────┘
  │
  ├── Review pending questions
  ├── Write answer → agent unblocks
  └── Save as standing decision → future similar questions answered automatically

Installation

git clone <repo>
cd lead-agent-mcp
pip install -e .

Running the server

lead-agent \
  --workspace /path/to/design-docs \
  --name     "my-project-lead" \
  --mcp-port  8765 \
  --ui-port   8080

Flag	Default	Description
`--workspace`	(required)	Primary folder — design docs, specs, any files agents should be able to read
`--memory`	—	Extra path to include, namespaced by directory name. Repeatable. See Syncing Claude memory
`--name`	`lead-agent`	MCP server name visible to connecting agents
`--description`	`Design alignment oracle`	Short description shown in MCP tool discovery
`--mcp-port`	`8765`	Port for MCP SSE — agents connect here
`--ui-port`	`8080`	Port for Web UI — lead reviews questions here (localhost only)
`--host`	`0.0.0.0`	Host to bind the MCP server
`--data`	`.data/`	Folder to store the SQLite database

On startup the server prints the exact config string team members need:

────────────────────────────────────────────────────────
  my-project-lead
────────────────────────────────────────────────────────
  Workspace : /Users/you/design-docs
  MCP (SSE) : http://0.0.0.0:8765/sse
  Web UI    : http://localhost:8080/
────────────────────────────────────────────────────────

Claude Code config for team members:
  "url": "http://<your-ip>:8765/sse"

Team member setup

Each developer adds one entry to their ~/.claude/settings.json:

{
  "mcpServers": {
    "lead-agent": {
      "url": "http://<lead-ip>:8765/sse"
    }
  }
}

The server is then available as an MCP tool in every Claude Code session.

MCP tools (available to agents)

Tool	Description
`ask_lead(question, context, from_agent)`	Ask a design question. Returns an immediate answer if a matching decision exists, otherwise returns `{status: "pending", question_id}`
`check_answer(question_id)`	Poll until the lead has answered. Returns `{status: "answered", answer}` or `{status: "pending"}`
`list_workspace_docs()`	List all files in the workspace
`read_workspace_doc(path)`	Read a workspace file by relative path
`search_workspace(query)`	Keyword search across workspace files — returns snippets
`list_decisions()`	List all standing decisions (useful for agents to understand existing policy before asking)

Recommended agent behavior

1. Call list_decisions() and search_workspace(topic) first
   → often the answer is already in a design doc

2. If still uncertain, call ask_lead(...)
   → if status == "answered": proceed with the answer
   → if status == "pending": stop work on this decision,
     record the question_id, poll check_answer() periodically

3. Never guess on decisions that cross component boundaries

Web UI

Open http://localhost:8080 on the lead's machine.

Queue — pending questions from agents, sorted by arrival time. Each shows the asking agent's ID, the question, and any context they provided. Click Reply to answer.

When answering, you can optionally Save as standing decision with a topic label. The next agent asking a similar question will receive the answer automatically without waiting for human review.

Decisions — browse and manage all standing decisions. Add new ones manually to pre-answer known policy without waiting for a question to arrive.

Syncing Claude Code memory

Claude Code stores its cross-session memory in ~/.claude/projects/<encoded-path>/memory/. These files capture project direction, design decisions, and context built up through your conversations — exactly what team agents should align with.

Pass this path via --memory and it becomes available under the memory/ namespace:

lead-agent \
  --workspace ~/projects/design-docs \
  --memory ~/.claude/projects/-home-you-projects-myproject/memory \
  --name "my-project-lead"

Find your encoded path:

ls ~/.claude/projects/

With this flag active, list_workspace_docs() includes memory/user-preferences.md, memory/project-decisions.md, etc., and search_workspace("bt-engine") searches them alongside your design docs.

You can mount multiple extra paths:

lead-agent \
  --workspace ~/design-docs \
  --memory ~/.claude/projects/.../memory \
  --memory ~/personal-notes/project

Note: Memory files contain your personal conversation history with Claude. Only mount paths you're comfortable sharing with the team.

Architecture

lead-agent-mcp/
├── lead_agent/
│   ├── store.py        # SQLite — questions & decisions
│   ├── workspace.py    # file reader / keyword search
│   ├── mcp_server.py   # FastMCP tools (factory pattern, no hardcoded names)
│   ├── web_ui.py       # FastAPI web UI
│   ├── main.py         # CLI entry point, runs both servers via asyncio
│   └── templates/      # Jinja2 + Bootstrap UI
└── pyproject.toml

Both servers (MCP SSE + Web UI) run in the same process via asyncio.gather. The MCP server binds to all interfaces by default; the Web UI binds to 127.0.0.1 only.

Requirements

Python 3.11+
Dependencies: mcp[cli], fastapi, uvicorn[standard], jinja2, python-multipart

This server cannot be installed

license - not found

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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