"Understanding the Concept of LLM Context" matching MCP servers:

An MCP server that manages your conversation context automatically, pruning dead branches and keeping an audit trail of every cut.

Share code context with LLMs via MCP or clipboard

A zero-dependency Node.js playground for debugging how MCP clients authenticate to an MCP server over HTTP.

An MCP server that analyzes local or remote GitHub repositories, providing intelligent code context and structure to AI coding assistants.

Enables coding agents to publish HTML pages and small sites over MCP, returning a stable link and keeping every version, with access limited to specific people, an organization, or anyone with the link.

MCP server for managing i18n translation files — gives your AI agent full control over your app's translations without dumping entire locale files into context.

Supply chain risk scoring for npm, PyPI, and GitHub repos

Enables a primary agent to offload bounded, high-volume analysis of explicitly selected workspace files to an auxiliary LLM through a single read-only tool, returning text, Markdown, JSON, or unified-diff output with token usage while retaining all planning, verification, and edits.

Enables coding agents to inspect Python applications, research source and target models, and produce reviewable migration plans with evidence gates and evaluation before production code changes.

A local-first memory cartridge for MCP-compatible AI clients, providing durable, inspectable, and auditable memory storage with a read-only-by-default MCP server.

This MCP server enables MCP clients such as Claude Desktop to run LLM-generated code in a secure, isolated sandbox across multiple container backends and programming languages.

An open-source memory layer that provides persistent project context and architectural history for AI development tools across multiple platforms and sessions. It enables AI assistants to maintain a shared understanding of codebases while integrating directly with services like Notion for documentation management.

Wraps Tool and ToolPackage instances from the llm-chat ecosystem behind the Model Context Protocol, enabling stdio and Streamable HTTP transports for MCP servers.

Token cost math for LLM API calls: current per-million-token rates for 69 models across 17 providers, with local arithmetic for estimates, comparisons and monthly budgets. Rates are verified and date-stamped.

A meta-server that aggregates multiple MCP servers into a single interface, reducing token usage by 98%+ through progressive tool discovery and direct code execution that processes data between tools without consuming context window space.

Enables local, cross-provider preflight validation of LLM integration changes—model, prompt, structured-output, and provider-call modifications—with no-spend checks, evidence on speed, tokens, and cost, and explicit human approval before paid runs.

Local-first documentation for AI agents

Loads entire codebase into LLM context for AI-assisted code understanding and editing.

Provides real structural understanding of a codebase via tree-sitter AST parsing before starting a task, reducing token waste and grounding acceptance criteria in code structure.

Provides real-time LLM pricing and availability data as an MCP server, enabling AI agents to make optimal model routing decisions at inference time with cited pricing sources.