site-context-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@site-context-mcpSearch the cached site corpus for 'LangGraph' and return snippets with source URLs."
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.
site-context-mcp
Problem
Agents invent project and site claims without citations. When you ask what a public site says about a product page, a topic note, or an essay, you often get fluent prose and no way to verify it against the live corpus.
Related MCP server: DuckDuckGo MCP Server
Solution
A stdio MCP server that:
Fetches public pages starting from
llms.txt, then follows same-host links to core pagesCaches them in-process (~1-hour TTL)
Exposes tools that return structured answers with a top-level
sources[]of public URLs on every response
No secrets, no LinkedIn scraping, no private admin / WOD-APP links — only HTTPS to the wired public site host.
This package is for site/project context. It does not duplicate resume identity or resume-summary tools (see sibling resume-mcp for that).
The default corpus is YongBo Yu’s public site. The same fetch/cache/source-URL shape can be pointed at any public llms.txt-indexed site corpus.
Requirements
Python 3.11+
Network access to the wired public host (default:
yongbo-yu.vercel.app) when running the live server
Install
# from this repo
pip install -e .
# or with uv
uv pip install -e .Dev / smoke tests (mocked HTTP; no live network required):
pip install -e ".[dev]"
pytest -qRun (stdio)
# module entrypoint
python -m site_context_mcp
# console script (after install)
site-context-mcp
# with uv
uv run python -m site_context_mcpThe process speaks MCP over stdin/stdout. Do not pipe unrelated stdout into the same process.
Cursor mcp.json
Add a server entry (Cursor: Settings → MCP, or edit ~/.cursor/mcp.json / project .cursor/mcp.json):
{
"mcpServers": {
"site-context-mcp": {
"command": "python3",
"args": ["-m", "site_context_mcp"],
"cwd": "/absolute/path/to/site-context-mcp"
}
}
}If the package is installed into a venv, point command at that interpreter (or use the site-context-mcp console script).
With uv:
{
"mcpServers": {
"site-context-mcp": {
"command": "uv",
"args": ["run", "python", "-m", "site_context_mcp"],
"cwd": "/absolute/path/to/site-context-mcp"
}
}
}After saving, reload MCP servers in Cursor. The tools below should appear as site-context-mcp tools.
Tools
Tool | Purpose |
| Catalog of cached public pages from |
| One page by path ( |
| Keyword search over the cached corpus (snippets + source URLs) |
| Project evidence page (default: |
Every tool response is JSON text that includes a top-level sources array of public URLs. Partial fetch failures are reported in cache_notes when present.
Example prompts (in Cursor / Claude)
“List the public pages this MCP has cached and cite the source URLs.”
“Get
/projects/kilodockand quote the engineering metrics with sources.”“Search the site corpus for ‘LangGraph’ / ‘Codex’ and return snippets with URLs.”
“Use
get_projectfor KiloDock evidence — do not invent stack claims.”
Data sources (public only)
On startup (and on first tool use if needed), the server fetches:
https://yongbo-yu.vercel.app/llms.txtSame-host pages linked from
llms.txt(home, about, project pages, topic pages, essays)Optionally
resume.jsonwhen linked — used only for light project cross-links, not as a resume clone
Binaries and off-host / private targets are skipped. No API keys.
Author
YongBo Yu (also Yong Yu) — Toronto, Canada · GitHub YongBoYu1
KiloDock evidence: https://yongbo-yu.vercel.app/projects/kilodock
Site index: https://yongbo-yu.vercel.app/llms.txt
License
MIT — see LICENSE.
This server cannot be deployed
Maintenance
Related MCP Connectors
LLM-ready web search + instant answers + URL-to-clean-text fetch for agents and RAG.
Collaborative, cache-first web search for agents — cited answers from a shared live-web pool.
Shared copies of public web pages for AI agents. Search stored pages or fetch a URL.
Real-time fact-check, citation verification, and source-freshness for AI agents.
Related MCP Servers
- FlicenseNot gradedqualityNot gradedmaintenanceEnables querying and retrieving content from webpages by parsing sitemap.xml files and fetching HTML content from specified URLs. Includes rate limiting for abuse protection.-
- AlicenseBqualityDmaintenanceEnables web search through DuckDuckGo and webpage content fetching with intelligent text extraction. Features built-in rate limiting and LLM-optimized result formatting for seamless integration with language models.2MIT
- AlicenseNot gradedqualityNot gradedmaintenanceProvides controlled access to llms.txt documentation files through MCP tools, allowing AI assistants to fetch and read documentation from user-approved domains with full audit visibility of tool calls and context retrieval.MIT
- AlicenseNot gradedqualityDmaintenanceConverts URLs into clean, LLM-ready markdown, respecting robots.txt and never bypassing anti-bot measures or paywalls.MIT