mcp-server copilot-instructions.md

A guide for an OpenAPI MCP gateway, a server that exposes API operations as tools for MCP clients and forwards clients' bearer tokens to downstream services. It is built with FastAPI and FastMCP and can handle asynchronous callback results.

In plain words
What is it for?
Use it when adding or changing MCP tools, API calls, callback endpoints, token handling, or the in-memory and Memcached result store. It identifies the main application, core helper, storage, and API-module locations.
Why use it?
It explains where tools are registered, how authorization is forwarded, and how callback results are stored. This reduces the risk of changing a central import, request helper, or callback path incorrectly.

Instructions file for GitHub Copilot

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/openapi/mcp-server/copilot-instructions
Clone the repo
git clone --depth 1 https://github.com/openapi/mcp-server

Made for: GitHub Copilot.

Per session 1,021 This file is loaded in full into every session.
When invoked 1,021 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.01021 $0.01021
Opus 5 $0.00511 $0.00511
Sonnet 5 $0.00204 $0.00204
Haiku 4.5 $0.00102 $0.00102

Measured yesterday against content hash e9fc3494e34b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

mcp-server copilot-instructions.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.github/copilot-instructions.md · 50 lines

How it starts

The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Quick context for AI coding agents

This repository implements the Openapi.com MCP gateway (FastAPI + FastMCP). The server acts as a proxy that forwards a client's Bearer token to downstream Openapi services and exposes MCP tools implemented under src/openapi_mcp_sdk/apis/.

Key files

  • src/openapi_mcp_sdk/main.py — application entry point. Mounts the MCP app and contains HTTP endpoints /callbacks and /status/{request_id}. Shows how token query params are converted into an Authorization header.
  • src/openapi_mcp_sdk/mcp_core.py — central FastMCP instance (mcp), helper make_api_call(ctx, method, url, ...), and processPolling for handling async callbacks.
  • src/openapi_mcp_sdk/memory_store.py — in-memory + Memcached-backed callback result store. Exposes get_callback_result and set_callback_result and defines BASE_URL, callbackUrl and memcached config from env vars.
  • src/openapi_mcp_sdk/apis/ — each module defines one or more tools decorated with @mcp.tool. Importing these modules triggers tool registration.

What matters for code changes

  • Tools are registered when the apis modules are imported by main.py. Don't remove those imports unless you know how to re-register tools.
  • make_api_call relies on extracting the Authorization header from FastMCP internals or ctx.request_context.request.headers. Many tools assume the client supplied Authorization: Bearer <token> (or ?token= query param).
  • Async APIs use a callback workflow: downstream APIs accept a callback object with url and custom (see company.py). Callbacks are saved with set_callback_result and read with get_callback_result.

Common patterns and code examples

  • Registering a tool (example from company.py):
    • Add @mcp.tool above an async function with ctx: Context as last arg.
    • Use make_api_call(ctx, "GET", url) to proxy authenticated requests.
  • Creating an async callback request (company full profile):
    • Build custom_context = {"request_id": getSessionHash(ctx), ...}
    • POST to the downstream .../callback endpoint with callback.url = memory store callbackUrl and callback.custom = custom_context.
    • Use processPolling(ctx, request_id, ...) to wait/poll stored results.

Environment and runtime

  • Run locally: python src/openapi_mcp_sdk/main.py (README shows python main.py from project root; in this layout use the module path). The server binds to 0.0.0.0:PORT (reads PORT env var, default 80).
  • Virtualenv: repository uses uv in README but pyproject.toml shows requires-python = ">=3.13". Note: README states Python 3.9+. If you modify runtime or CI, confirm the correct Python target.

Developer workflows (concrete)

  • Start server (local):
    • Create venv and install requirements (README): uv venv && source .venv/bin/activate && uv pip install -r requirements.txt
    • Run: python src/openapi_mcp_sdk/main.py (or use uvicorn as in if __name__ == '__main__').
  • Docker: see Dockerfile and README.
  • Debugging tips:
    • Check printed logs: main.py and mcp_core.make_api_call print request details and errors.
    • Use /status/{request_id} to inspect async request state set by callbacks.
    • If memcached calls fail, memory fallbacks provide predictable behavior.

Conventions and gotchas for agents

  • Do not add or hardcode secrets (tokens/keys) into code — this project explicitly expects the client to supply Bearer tokens. Keep token handling in middleware or test fixtures.
  • When adding a new API/tool module:
    • Follow the @mcp.tool decorator pattern and ensure the module is imported from main.py or otherwise registered during startup.
    • Prefer to call make_api_call for HTTP interactions so the common auth/header extraction is reused.
  • The code expects ctx to contain request_context.request.headers in some places; use defensive checks when reading headers if you add new code paths.

What I couldn't verify automatically

  • Exact developer commands for uv usage and environment variants are described in README; confirm whether you prefer python -m venv flows or uv for reproducibility. Also confirm the Python version (pyproject vs README).

Read the full file on GitHub · 50 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 50 lines · 1,021 tokens per session scan A e9fc3494e34b

Subscribe to this mod's changes

mcp-server copilot-instructions.md is an instructions file published in the GitHub repository openapi/mcp-server (19 stars, last pushed 25d ago), licensed MIT. It adds 1,021 tokens to every session, about $0.0051 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.