Llm ContextBLOCK
Share code with LLMs via Model Context Protocol or clipboard. Rule-based customization enables easy switching between different tasks (like code review and documentation). Includes smart code outlining.
Overview
From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.
[](https://opensource.org/licenses/Apache-2.0) [](https://pypi.org/project/llm-context/) [](https://pepy.tech/project/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
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 — this README is a landing page, not a manual.
Find them at .claude/skills/lc-curate-context/ after lc-init. In Claude Code the skill loads automatically; elsewhere, read the files directly.
Installation
uv tool install "llm-context>=0.6.0" cd lc-init # creates .llm-con
dca6ffae3418OBSERVED · 2026-10-03Connect
Built from this server's own package name, version and transport as found in its source — not copied from anyone's documentation, so it cannot drift against a page we do not control.
claude mcp add llm-context -- uvx llm-context
{
"mcpServers": {
"llm-context": {
"command": "uvx",
"args": [
"llm-context"
]
}
}
}Exposed tools (5)
5 read · 0 write · 0 destructive.
| Tool | Risk | Description |
|---|---|---|
lc_changed | read | Returns list of files modified since given timestamp. |
lc_missing | read | Unified tool for retrieving missing context (files, implementations, or excluded sections). |
lc_outlines | read | Returns excerpted content highlighting important sections in all supported files. |
lc_preview | read | Preview what files a rule selects and their sizes. |
lc_rule_instructions | read | Provides step-by-step instructions for creating custom rules. |
Trust audit
BLOCKgrade D · trust 69/100 Do not install this without reading the findings. The audit found something that could harm you or your machine.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | FAIL |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- declared (2 observation(s))
- Network
- none-observed
- Shell
- declared (4 observation(s))
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (5)
else ToolConstants.from_dict(Yaml.load(project_layout.state_path))
config = Yaml.load(self.project_layout.config_path)
return ToolConstants(**Yaml.load(path))
data = Yaml.load(self.storage_path)
raw_config = Yaml.load(project_layout.config_path)
Gates applied: no_behavioural_pass.
dca6ffae3418full audit observations/trust-audit/mcp-server/cyberchitta__llm-context.json · Report an issue / request a re-scanAudit history
Every audit this server has had. A grade with a past is a grade somebody is still checking.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-03 | dca6ffae3418 | BLOCK | D | 69 | first audit |
Questions
What is the Llm Context MCP server?
Share code with LLMs via Model Context Protocol or clipboard. Rule-based customization enables easy switching between different tasks (like code review and documentation). Includes smart code outlining.
What tools does Llm Context expose?
5 in total: 5 read-only, 0 that write, and 0 that can delete or overwrite. Every one is listed on this page with its risk.
Is Llm Context safe to connect to an agent?
No — not without reading the findings first. The audit graded it D (69/100) and found 4 critical or high issues in the source. Each one is listed on this page with the file and line it is on.
What credentials does Llm Context need?
No credential environment variables were found in its source, so it appears to need none.
How does Llm Context run?
It speaks stdio, so it runs as a local process your client starts. It is published on PyPI as llm-context.
How current is this page?
The grade is for one exact copy of the source (dca6ffae3418), read on 2026-10-03. The repository is watched and re-audited when it changes.