Structural codebase indexer exposing 17 query tools (functions, classes, imports, dependency graphs, change impact) via MCP. Zero dependencies.
Local MCP server that shows AI agents which patterns your team actually uses, what files a change will affect, and when there is not enough context to trust an edit. 30+ languages, fully local.
Index and search codebases using structured schemas for deep code analysis. Audit specific domains or security-related functions to ensure code quality and safety. Explore complex codebases with hig
Analyzes your codebase identifying important files based on dependency relationships. Generates diagrams and importance scores, helping AI assistants understand the codebase.
Persistent codebase knowledge layer for AI coding agents. Pre-digests codebases into structured knowledge (symbols, dependency graphs, co-change patterns, architectural decisions) via tree-sitter nati
Scan files and directories to map code structure and navigate large codebases faster. See a compact overview of key elements to decide what to read next. Search for specific structures—like tests, a
A MCP server that uses [gitingest](https://github.com/cyclotruc/gitingest) to convert any Git repository into a simple text digest of its codebase.
Enforce consistent C++ style and best practices across your codebase. Analyze naming, memory safety, and const correctness, and get actionable modernization suggestions up to C++23. Accelerate reviews
Stop stitching context for Copilot and Cursor. With Sequa MCP, your AI tools know your entire codebase and docs out of the box.
Extracts essential code structure from large codebases into AI-digestible format, helping AI agents write code that correctly uses existing APIs on the first attempt.
Search the web and codebases to get precise, up-to-date context for programming and research. Find examples, API usage, and documentation from real repositories and sites to ship faster with fewer mis
MCP Language Server helps MCP enabled clients navigate codebases more easily by giving them access to semantic tools like get definition, references, rename, and diagnostics.
Scan files and directories to map code structure and navigate large codebases faster. See a compact overview of key elements to decide what to read next. Search for specific structures—like tests, asy
Search codebases for patterns and references with fast, precise results. Open files on demand to inspect context without leaving your workflow. Browse available collections to quickly target the right
EU AI Act compliance scanner that detects regulatory violations in AI codebases with risk classification and remediation guidance.
lets your preferred AI agent create & run fully managed [Octomind](https://www.octomind.dev/) end-to-end tests from your codebase or other data sources like Jira, Slack or TestRail.
Here's an enhanced README with more technical details based on the codebase:
Hosted MCP server that parses TypeScript and Python codebases into Neo4j knowledge graphs for blast radius analysis, dead code detection, dependency tracing, and architectural context.
Analyzes C++ source code to build and explore comprehensive internal dependency graphs. Maps function relationships to identify upstream callers, downstream callees, and circular dependencies. Improve
**Giving AI coding assistants a memory that actually persists.**
**Give your AI the ability to understand meaning, not just match keywords.**
[🇨🇳 中文文档](docs/README-zh.md) | [🇺🇸 English](README.md)