Legion Demo
Enables interaction with the Agent Legion platform through MCP. Provides tools such as tool_say_hi to demo the server's capabilities.
README
legion-demo
Live demo agent for the 图灵星球 Agent Legion platform — connect over MCP and ask it about the platform.
A member agent of 图灵星球 Agent 军团, generated from agent-template with Copier. Run copier update to pull future template changes (your code is preserved; conflicts come out as markers to resolve).
Setup checklist
- Install & run locally → Run the MCP server (
poetry install, connect Claude). - Push to GitHub as its own repo — run from inside this folder so the repo root is the agent:
(If your deploy later says "root only contains subdirectories", you pushed a parent folder — redo this from inside the agent folder.)git init && git add -A && git commit -m "Scaffold from agent-template" gh repo create legion-demo --private --source . --push - Fleet auto-sync (optional but recommended) → grant the bot access.
- Deploy (optional) → Deploy remotely.
Layout
agent.manifest.yaml— the instruction card: toolchain, paths, and commands.config.py— THE one config file: every runtime knob (transport, port, model) plus the checklist of env vars/secrets a deployment needs. Changing model or platform later = read this one file.api/— your business logic (replace the placeholderrun();say_hi()is a working example).mcp_server/— one process, two surfaces over/api: an MCP server at/mcp(for Claude) and a REST API at/api(FastAPI, for humans/other services). Local runs use stdio (MCP only); deployed runs serve both over HTTP.tests/— smoke tests..github/workflows/review.yml— thin pointer to the central review flow.
Run the MCP server & connect Claude
poetry install # once
# register with Claude (run from the repo root; stores absolute paths):
claude mcp add legion-demo -- poetry -C "$(pwd)" run python "$(pwd)/mcp_server/server.py"
Then in Claude, ask it to call the tool_say_hi tool — it replies with this server's timezone and current time:
hello from PDT 2026-07-03 15:04:05: hi
Add your own tools by writing functions in api/ and exposing them with @mcp.tool() in mcp_server/server.py.
Deploy remotely (connect from anywhere)
The same server switches to HTTP mode automatically when the platform injects a PORT (Railway, Render, Fly.io — any always-on host; serverless platforms like Vercel don't fit this Python server). No code change needed:
- Make sure
poetry.lockis committed (created at scaffold time; builders detect a Poetry project by it). - Push this repo to GitHub and create a project on your platform (e.g. Railway → Deploy from GitHub repo). The start command ships in
railpack.json— Railway picks it up with zero configuration; the injectedPORTflips the server to HTTP, serving MCP at/mcp. - Your deployed app serves both surfaces (replace
<your-app-url>with your real deployment URL):- MCP at
https://<your-app-url>/mcp— connect Claude from any machine. The-cloudsuffix keeps this remote registration separate from your local stdio one (same server name would clash):
Then in a new Claude session:claude mcp add --transport http --scope user legion-demo-cloud https://<your-app-url>/mcp/mcpshowslegion-demo-cloudconnected → ask it to calltool_say_hi→ the time comes back in the server's timezone (e.g. UTC on Railway), proof it's the remote one. - REST API at
https://<your-app-url>/api/...— for humans, scripts, or other services:Add more endpoints incurl https://<your-app-url>/api/say_hi # {"message":"hello from UTC …: hi"}mcp_server/server.py(build_http_app), reusing your/apilogic.
- MCP at
Everything configurable about the deployment (transport, port, model, which secrets to set) is documented in config.py — that's the only file to read when you change platform or model.
⚠️ A deployed server is public: anyone with the URL can call your tools. Fine for the harmless starter tools; add auth before exposing tools that touch real data.
Fleet auto-sync (keep this repo on the latest template)
This agent can be tracked by the fleet migration bot: when a new agent-template version ships, the bot opens a PR here bumping you to it (you review + merge — never auto-merged). Two things must be true:
- You're listed in the fleet's
members.yaml. The scaffold offers to open that PR for you (the first Copier question). If you skipped it or lacked registry access, ask the platform admin to add:- name: legion-demo repo: <owner>/legion-demo - The bot's GitHub App can access this repo. ⚠️ Registration alone is NOT enough — a GitHub App can't grant itself access; the owner of this repo's account grants it once:
- GitHub → Settings → Applications → Installed GitHub Apps →
fleet-migration-bot→ Configure - Under Repository access: add this repo, or choose All repositories (simplest for a personal account — the bot only ever touches repos in
members.yaml). - On an org you don't administer, ask the platform admin to grant it.
- GitHub → Settings → Applications → Installed GitHub Apps →
If a sync run fails with "Not Found" on your repo, it's always #2 — the App hasn't been granted access yet.
How review works
Open a pull request → the review flow from policies reads the manifest, installs, runs the tests, lints, scans for security issues, lets the AI reviewer advise — and the gate (the hard checks) decides pass/fail. See the platform overview for the full picture.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.