GitHub Issues MCP Server

A custom MCP (Model Context Protocol) server that lets an AI model securely read and manage GitHub issues, with:

• Authenticated access to a private/personal GitHub account via a token

• Confirmation prompts in front of every write/delete-equivalent action

• Metrics tracking on tool-call success/failure so you can measure how reliably the model picks the right tool and uses it correctly

1. Setup

cd mcp-github-server
python -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\activate
pip install -r requirements.txt

cp .env.example .env
# edit .env: paste in a GitHub token (Settings -> Developer settings ->
# Personal access tokens -> Fine-grained tokens, with Issues read/write
# permission on the repo(s) you want to use), and optionally restrict
# ALLOWED_REPOS.

Related MCP server: GitHub MCP Agent Server

2. Try it locally with the MCP Inspector

mcp dev server.py

This opens a browser UI where you can call list_issues, get_issue, create_issue, add_comment, and close_issue directly and see the confirmation prompt fire for the write tools.

3. Connect it to Claude Desktop (or another MCP client)

mcp install server.py --name "GitHub Issues"

Or add manually to your client's MCP config:

{
  "mcpServers": {
    "github-issues": {
      "command": "python",
      "args": ["/absolute/path/to/mcp-github-server/server.py"]
    }
  }
}

4. Tools exposed

Write tools use MCP elicitation (ctx.elicit(...)) to pause the tool call and ask a human to accept/decline before anything touches GitHub. If the human declines or cancels, nothing is written and that's logged as a "declined" outcome in metrics.

GitHub's API doesn't support deleting issues (only GitHub Apps with special permissions can, and it's rarely allowed) — close_issue is the closest "destructive" action, and it's gated the same way creates/comments are.

5. Metrics: measuring tool-selection quality

Every tool call — success, failure, and confirmation outcome — is logged to a local SQLite DB (metrics.db by default). View a summary with:

python metrics_dashboard.py

This reports, per tool:

• Success rate — low success rate on one tool suggests its description or argument schema is confusing the model relative to the others

• Top error category — validation errors mean the model picked the right tool but passed bad arguments (usually fixable by tightening the docstring); auth/not_found are config issues, not model issues

• Confirmation decline rate — how often a human rejected a write action the model proposed; a high rate suggests the model is reaching for create/comment/close in situations where it shouldn't

You can also query metrics.db directly with any SQLite tool for deeper analysis (e.g. plotting trends over time as you iterate on tool descriptions).

6. Production hardening ideas (next steps)

• Swap the raw PAT for a GitHub App installation token (shorter-lived, scoped per-installation, better audit trail)

• Add retry/backoff on rate_limit errors instead of failing immediately

• Add structured logging (e.g. to a real DB or observability platform) instead of SQLite once you're running this for more than one user

• Add per-tool rate limiting so a misbehaving model can't spam GitHub

• Write a small eval set of prompts with known "correct tool" labels so you can measure true tool-selection accuracy, not just success/failure proxies