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  3. Llm Context.py
Llm Context.py logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 3:48:17 PM

Llm Context.py

User RatingsBe the first to rate and review this MCP server!
View Repository306 GitHub StarsTotal stargazers on GitHub for the source repository (306 stars).Visit Website
file-systemscontext-managementmcpllmrules

Manage and share focused project file context with LLMs via composable rules, clipboard, or MCP integration.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
We couldn’t automatically confirm this listing starts correctly

The install command below didn't complete successfully in our automated test.

uvx --from llm-context lc-mcp

Downloading cryptography (4.5MiB) Downloading tree-sitter-language-pack (2.2MiB) Downloading pydantic-core (2.0MiB) Downloaded pydantic-core Downloaded tree-sitter-language-pack Downloaded cryptography Installed 37 packages in 17ms Traceback (most recent call last): File "/home/user/.cache/uv/archive-v0/7XR2JHRa-LGKITXr/bin/lc-mcp", line 6, in <module> from llm_context.mcp import run_server File "/home/user/.cache/uv/archive-v0/7XR2JHRa-LGKITXr/lib/python3.11/site-packages/llm_context

This is an experimental automated check and can have false negatives β€” missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "cyberchitta-llm-context-py": {
      "command": "uvx",
      "args": [
        "--from",
        "llm-context",
        "lc-mcp"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸ“‚ More in File Systems

Overview

llm-context.py enables intelligent selection and rule-based filtering of project files to provide relevant context for large language models. It supports human workflows via clipboard commands and AI agent workflows via CLI or MCP tools. Use it to automate context gathering, validate context rules, and fetch additional files on demand during development or AI interactions.

Use cases

β€’Select relevant project files for LLM conversations
β€’Generate focused context snippets for AI agents
β€’Validate and preview context selection rules
β€’Fetch missing files or implementations on demand
β€’Integrate with MCP servers for seamless AI access to code

Key features

β€’Composable YAML+Markdown rules for task-specific context
β€’Commands for file selection, context generation, and rule validation
β€’Clipboard support for human workflows
β€’MCP server tools for AI chat integration
β€’Agent CLI commands for rule-driven context management
β€’Interactive Claude skill for AI-assisted rule creation

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Llm Context.py.

Extracted Tool Capabilities
Composable YAML+Markdown rules for task-specific context
Commands for file selection, context generation, and rule validation
Clipboard support for human workflows
MCP server tools for AI chat integration
Agent CLI commands for rule-driven context management
Interactive Claude skill for AI-assisted rule creation

Documentation Overview

LLM Context

License PyPI version Downloads

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 β€” 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 β€” the tooling handles packing, follow-up fetches, and change tracking.

Documentation lives in the skill

The full documentation is the lc-curate-context skill, installed into your project by lc-init. It is written to be read by an agent, and it is the only copy β€” this README is a landing page, not a manual.

FileContents
SKILL.mdWriting a rule; the workflow; packing for a sub-agent
COMMANDS.mdCLI and MCP reference, including output routing
SYNTAX.mdRule file schema and every field
PATTERNS.mdReusable rule shapes
EXAMPLES.mdWorked examples
TROUBLESHOOTING.mdFailure cases

Find them at .claude/skills/lc-curate-context/ after lc-init. In Claude Code the skill loads automatically; elsewhere, read the files directly.

Installation

bash
uv tool install "llm-context>=0.6.0"
cd <project-root>
lc-init          # creates .llm-context/, installs the skill into .claude/skills/

Upgrading: uv tool upgrade llm-context, then any lc-* command refreshes the skill, rules and templates in place.

Three ways to use it

Your coding agent, curating for itself β€” this is the primary path. After lc-init the agent has the lc-curate-context skill, so "get me focused context for the auth refactor" becomes a rule it writes and verifies without you naming files.

bash
lc-preview -r tmp-prm-auth    # what does this rule actually select?

lc-preview reports the exact file lists, the size, and β€” via the code graph β€” files defining symbols the selection uses but does not include. That last section is how the agent catches the module it forgot, before spending a turn on a wrong answer.

A sub-agent, via a pipe β€” a dispatcher writes the prompt, llm-context supplies the files.

bash
lc-context -r tmp-prm-auth -a | claude -p 'Your task here'

-r routes output to stdout; without it lc-context copies to the clipboard, so a pipe or redirect gets nothing but log lines. These commands take no bare positional rule name β€” always -r. -a renders the pack's fetch instructions as shell commands the child runs itself, so from the repo root it can call lc-missing against the pack's timestamp for anything left out. See SKILL.md "Packing for a Sub-Agent".

A chat, via MCP or the clipboard β€” for models that aren't driving a terminal.

JSON Config
{
  "mcpServers": {
    "llm-context": {
      "command": "uvx",
      "args": ["--from", "llm-context", "lc-mcp"]
    }
  }
}

With MCP the model pulls files it wasn't given and notices ones that changed underneath it, through lc_missing, lc_changed, lc_outlines and lc_preview. Without it, lc-select then lc-context puts the pack on your clipboard to paste anywhere.

Rules in one minute

A rule is YAML frontmatter plus optional markdown:

yaml
---
description: "Debug API authentication"
compose:
  filters: [lc/flt-no-files]
  excerpters: [lc/exc-base]
also-include:
  full-files: ["/src/auth/**", "/tests/auth/**"]
---
Focus on the authentication system and its tests.

You rarely write one by hand β€” the skill does, and lc-preview is how it checks its work. Rules compose, and are named by category: prm- produces a context, flt- controls file inclusion, ins- supplies guidelines, sty- enforces coding standards, exc- configures excerpting. Files you expect to edit go in full-files; supporting code goes in excerpted-files, where it is reduced to signatures and definitions. See SYNTAX.md and PATTERNS.md.

What a generated context contains

Complete contents for full files, structural excerpts for the rest, a filtered file listing marking what is and isn't included, and a timestamp that lc-missing and lc-changed resolve against.

A pack is always partial β€” the listing is filtered by your .gitignore files and by the rule before anything is marked excluded, so files can exist that it never mentions. The header reports how many files are full, outlined and excerpted, so a consumer can check what it actually received rather than trusting the rule.

Learn More

  • Design Philosophy β€” why llm-context exists
  • Real-world Examples β€” using full context effectively

License

Apache License, Version 2.0. See LICENSE for details.


Developed in collaboration with several Claude models and Groks, using LLM Context itself to share code during development. All code is heavily human-curated by @restlessronin.

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

GitHub stars
306
Stargazers on the source repository.
Last commit
16d ago
Most recent push to the default branch.
Install check
Inconclusive
Install command did not finish in our automated test.
Directory activity
2 views
Config copies, upvotes, and views on AllMCPs.

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Frequently Asked Questions about Llm Context.py

Run 'lc-init' in the project root to create configuration and install the Claude skill if desired.

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Technical Specs & Signals

CategoryπŸ“‚File Systems
PricingFree
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimePython
AuthNo auth required
LicenseApache-2.0
ClientsClaude Desktop
Last updatedAug 26, 2026
10/10 checks healthy over the last 31d
Views2
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars306
GitHub Star CountTotal stargazers on GitHub representing community popularity (306 stars).
Last commit16d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Aug 26, 2026
47Quality signal: Fair Β· 47/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools16/30
Adoption & activity9/15
Community engagement0/10

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Scanned 23d ago via OSV.dev Β· lc-mcp (PyPI)

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