
llm-context
llm-context provides intelligent context management for LLM development workflows through rule-based file selection and filtering. The server exposes three primary MCP tools—`lc_outlines` for generating excerpted context, `lc_preview` for validating rule effectiveness, and `lc_missing` for fetching specific files on demand—enabling AI agents to access relevant project files dynamically without manual copying. This solves the friction of manually gathering appropriate context, managing token limits, and handling incremental file requests during development sessions.
- GitHub stars
- 300
- tools
- 4
- last updated
- Mar 8, 2026
Tools
4 toolsPublic tool metadata for what this MCP can expose to an agent.
lc_missingLets the model pull files it wasn't given, through MCP.
Lets the model pull files it wasn't given, through MCP.
No parameter schema in public metadata yet.
lc_changedNotices files that changed underneath the model, through MCP.
Notices files that changed underneath the model, through MCP.
No parameter schema in public metadata yet.
lc_outlinesRetrieves structural outlines of files, through MCP.
Retrieves structural outlines of files, through MCP.
No parameter schema in public metadata yet.
lc_previewPreviews what a rule actually selects, through MCP.
Previews what a rule actually selects, through MCP.
No parameter schema in public metadata yet.
Sponsors
Readme
LLM Context
Context curation your coding agent does for itself. lc-init installs a skill that teaches the agent to work out which files a task actually needs, write that down as a composable rule, check the rule against the codebase, and pack the result — for its own context, for a chat you paste into, or for a sub-agent it dispatches.
Getting the right context into an LLM is friction-heavy: finding and copying files by hand wastes time, too much context hits token limits, too little misses what matters, and follow-up file requests mean more manual fetching. The usual answers are to send everything, or to have a person curate by hand. A rule describes the selection once, and it is a thing an agent can author, verify and reuse — the tooling handles packing, follow-up fetches, and change tracking.
Documentation lives in the skill
Details
- Repository
- github.com/cyberchitta/llm-context.py
- Category
- AI & LLM Tools