High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 159 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
Provides AI assistants with real-time visibility into your codebase's internal libraries, team patterns, naming conventions, and usage frequencies to generate code that matches your team's actual practices.
Provides a compact, task-oriented wrapper around the CDISC Library API for discovering, searching, and fetching Biomedical Concepts and SDTM Dataset Specializations.
Historical stock pattern intelligence for AI agents. Search 24M pre-computed chart pattern embeddings across 15K stocks and 10 years. 19 tools: pattern similarity search, forward returns, regime analysis, anomaly
detection, sector rotation, earnings reactions, correlation shifts, scenario analysis, and more. Returns what happened historically when charts looked like this — compliance-safe
Generates comprehensive documentation (architecture overview, dependency graph, API surface, and README) for any codebase locally without external APIs.
A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.
Renders source code as beautiful SVG or PNG images with syntax highlighting, line numbers, and diff support. Ideal for AI agents to present code visually to humans or for sharing snippets.
An MCP server that gives AI agents clean, token-efficient access to US civic & property data — geocoding, census tracts, Opportunity Zones, ACS demographics, and FEMA flood zones — sourced entirely from free federal open data.
Make any LLM a codebase expert instantly. Provides deep code intelligence through semantic search, architecture mapping, security analysis, and smart context that fits perfectly in token windows.
An enhanced sequential thinking tool optimized for programming tasks that helps break down complex coding problems into structured, self-auditing thought steps with branching and revision capabilities.