Skim MCP Server

Lossless skim-then-expand reading of big files, repos, and command output with far fewer tokens. That is what the skim mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.

The short version

Token-efficient skim-then-expand I/O for agentic models (Claude Code / desktop Claude).

• Files — — Python (ast) + ~17 languages (tree-sitter); bodies folded, one expand away
• Whole repos — — skim_repo builds a ranked, token-budgeted map; expand exact code from any file
• Command output — — skim_run compresses verbose test / build / log output, fully recoverable
• Data & logs — — a generic path with a retention guarantee (ids, numbers, error codes, negations

Getting it running

Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.

The tools it exposes

The server publishes 2 tools. What each one is for:

• Files — Python (ast) + ~17 languages (tree-sitter); bodies folded, one expand away
• Tools — The Tools tool exposed by this server

Things to watch

• It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
• Keep per-call confirmation enabled while you learn its behaviour; it is the cheapest safeguard you have.

How it compares

This sits in the developer tooling group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Skim's toolset — Files, Tools — is a fair guide to whether it matches your workflow. It is maintained by helloderekg; worth a glance at recent repository activity before you build anything load-bearing on it.

We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.