LLM Code Context
This LLM Context MCP server, developed by restlessronin, streamlines the process of sharing code context with Large Language Models. Built with Python using the Model Context Protocol SDK, it offers features like profile-based customization, code outlining, and integration with both MCP and clipboard for easy switching between tasks such as code review and documentation. The server implements smart file selection using .gitignore patterns and supports multiple programming languages. By abstracting the complexities of context generation, it enables AI systems to easily analyze and work with codebases. This implementation is particularly valuable for developers and teams requiring efficient code-related interactions with LLMs, facilitating use cases such as code reviews, documentation generation, and codebase analysis across various programming languages and project structures.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-28.
Scan details: Circle-IR · 2026-09-28 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- ai-ml
- Source
- PulseMCP
- Repository
- github.com/cyberchitta/llm-context.py
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve LLM Code Context from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.