Parkour MCP

parkour

"an activity in which people move quickly around buildings and objects in a city while performing jumps and other skilful movements, usually trying to move between points as quickly, smoothly, and safely as possible"

-- Cambridge Dictionary

Parkour is a content exploration toolkit that helps LLMs surface high signal, unsummarized web content. It makes extensive use of clean APIs and Markdown conversion to enable targeted content extraction and knowledge synthesis. A rolling 2Q page cache keeps recently visited pages in memory so that follow-up requests (section extraction, BM25 search, slice retrieval, comparison pivots) are served quickly and without additional round-trips. While primarily designed for Claude Code and Claude Desktop, it should be adaptable to most agentic toolchain needs.

API integrations:

• Kagi Search
• Kagi Summarize
• Semantic Scholar
• arXiv
• IETF
• deps.dev (library package lookups)
• GitHub
• MediaWiki
• Reddit (old.reddit.com JSON API)
• Discourse (header-detected, raw markdown API)

Why Parkour?

What sets Parkour apart from the standard approaches are three principles:

Tool calls should participate in steering the LLM.

We design our tool outputs with the LLM in mind. The LLM is our immediate user, and if our user has a good experience the humans behind them have an even better experience.

The standout feature of Parkour is a frontmatter tool envelope that intelligently advises the LLM and steers its decisionmaking. This is a fancy way of saying "our tool payloads are prefaced with instructional YAML frontmatter". It's a technique that is simple on its face but deceptively powerful.

• Frontmatter leverages the LLM's existing document training to prime the next tool decision. Envelope fields are relevant to the activity at hand, and are positioned for actionability.
• The key:value pairs of YAML are self-documenting for both humans and LLMs, giving us a free out of band channel that doesn't require a new MCP standard.
• The tool outputs proactively steer the LLM toward sources of high signal and away from dead ends. This adds a small amount of tool latency for the background web calls we perform, but every unnecessary tool call we avoid pays dividends on that investment.
• We maintain a design document to ensure that the frontmatter envelope is used in a consistent fashion across tools. You can read more about it here.

Parkour also intercepts requests for content from websites with robust first-party APIs. When the LLM asks to fetch a URL that belongs to a known source, the server skips the generic HTTP-fetch-and-convert path and calls the source's structured API directly. Faster, richer metadata, no scraping:

For example, asking Parkour to fetch https://arxiv.org/abs/1706.03762 doesn't scrape the landing page. It returns structured metadata via the Atom API, with frontmatter hints pointing to the HTML full text and a Semantic Scholar cross-reference for citation counts:

>>> web_fetch_exact("https://arxiv.org/abs/1706.03762")
---
title: Attention Is All You Need
source

... [View full README on GitHub](https://github.com/blightbow/parkour-mcp#readme)