Conduit
Provides read-only access to GitHub repositories and issues through standardized MCP tools, with caching to handle API rate limits.
README
Conduit
An MCP (Model Context Protocol) server that exposes real GitHub repository and issue data to any MCP client — Claude Desktop, or any other tool that speaks the protocol — through five standardized tools instead of a one-off integration per client.
Why MCP instead of a custom API wrapper
Without MCP, "let an LLM query GitHub" means writing a bespoke integration for every client that wants it. MCP standardizes that: build the server once, and Claude Desktop, a custom agent, or any other MCP-speaking client can call it the same way. This server is the data side of that — read-only access to public repos and issues, with a caching layer in front of it.
Tools exposed
| Tool | What it does |
|---|---|
list_repos(username, limit) |
A user's public repos, most recently updated first |
get_repo(owner, repo) |
Description, language, stars, open issue count |
list_issues(owner, repo, state, limit) |
Issues only — pull requests filtered out |
get_issue(owner, repo, number) |
Full issue detail, including body text |
get_readme(owner, repo) |
Raw README content |
Every tool response is slimmed down to the fields that matter
(conduit/github_client.py) — GitHub's raw API payloads run 60+ keys
deep, and handing an LLM all of it burns context for no reason.
Caching, and why it's not optional here
GitHub's unauthenticated REST API allows 60 requests/hour per IP. An
LLM calling tools in a loop can burn through that in minutes. Every tool
response is cached in SQLite (conduit/cache.py) with a configurable
TTL (CACHE_TTL_SECONDS, default 300s) — a real engineering constraint
this project actually has to handle, not a hypothetical one.
Two real bugs this caught while being built
- A run stuck silently on a closed event loop. The first working
version created its
httpx.AsyncClientonce at import time and reused it for every tool call. On Windows, that client's connection pool would occasionally try to close a pooled connection whose cleanup got scheduled against an event loop that had already moved on —RuntimeError: Event loop is closed, with no indication of why. Fixed two ways: the client is now built lazily on first real use instead of at import time, and connection keep-alive pooling is disabled (httpx.Limits(max_keepalive_connections=0)) so there's no pooled connection left around to race on. - Tests failing on GitHub's rate limit looked like code failures.
Unauthenticated testing hit the 60/hour cap partway through
development, and those failures were indistinguishable from real bugs
in the output.
tests/conftest.pynow catches a 403 rate-limit response and skips with a clear reason instead of failing red for a constraint that has nothing to do with correctness.
Using it with Claude Desktop
Add to your Claude Desktop MCP config (claude_desktop_config.json):
{
"mcpServers": {
"conduit": {
"command": "python",
"args": ["-m", "conduit"],
"cwd": "/path/to/conduit",
"env": { "GITHUB_TOKEN": "your-token-here" }
}
}
}
GITHUB_TOKEN is optional — public data works without one, just at the
lower rate limit.
Running it
pip install -r requirements.txt
python -m conduit
Runs over stdio, which is what Claude Desktop (and most MCP clients) expect — there's no web dashboard for this one, since an MCP server's actual interface is the protocol itself, not a UI.
Running the tests
pytest -q
12 tests. test_cache.py is fully offline. test_github_client.py and
test_server.py hit the real GitHub API on purpose — a mocked response
would never have caught either bug above — and skip cleanly if the rate
limit is already exhausted rather than failing.
Known limitations
- Read-only by design. A version that could open issues or comment would be a legitimately different, higher-risk project — this one doesn't attempt it.
- No auth beyond an optional personal access token — fine for public repos, not built for private repo access control.
- The SQLite cache is unbounded — nothing evicts old entries beyond TTL expiry. Fine at the scale one developer's tool calls generate; not designed for high-volume multi-user deployment.
License
MIT — see LICENSE.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。