"Semantic Scholar" matching MCP servers:

Semantic code graph MCP server for coding agents

Enables Claude Code to perform fuzzy and semantic codebase search via a query_codebase tool.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.

Enables AI agents to perform semantic code search across entire codebases using natural language queries. Provides fast indexing and ranked search results with line numbers and file paths through the Seroost search engine.

Enables natural-language semantic search of a project's code through a local vector index, so questions that plain grep cannot match return ranked file:line references. It also indexes or refreshes projects on demand, reports index status, and optionally flags near-identical code across files.

Enables free indexing of repository files into a live semantic code search Worker via the index_code tool, while semantic search requires calling the paid x402-gated endpoint directly.

Bridges MCP and LSP to enable semantic code analysis, including go-to-definition, find references, hover info, and function call relationship checks.

Provides semantic code search over codebases using local embeddings with natural language queries. Supports hybrid search, file watching, and respects .gitignore.

A self-hosted semantic code retrieval MCP server that enables natural language queries to find relevant code snippets using Tree-sitter AST parsing, embedding, and hybrid search with reranking.

Semantic code search for Claude Code, enabling natural language codebase indexing and search using AI embeddings.

Enables semantic code search across local projects and Git repositories using AI embeddings with ChromaDB. Supports both OpenAI and local Ollama models for private, enterprise-ready code analysis and similar code discovery.

Enables coding agents to search locally indexed repositories with hybrid semantic and lexical retrieval, returning exact source citations with file paths and line ranges.

Combines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.

Enables AI agents to perform semantic search over codebases by converting natural language queries into efficient search patterns like grep and ripgrep. It utilizes LLMs to verify relevance and find code snippets that traditional keyword-based searches might miss.

Enables semantic code search for AI assistants by indexing codebases with embeddings and Tree-sitter, returning relevant snippets via natural language queries.

A production-grade MCP server providing advanced code intelligence capabilities such as semantic search, dependency analysis, and natural language Q&A across multiple programming languages.

Enables LLM agents to explore and search a 1C:Enterprise configuration codebase, including metadata objects, module procedures, call graphs, and semantic similarity search.

Enables AI coding agents to perform semantic code search locally, finding code by meaning rather than exact keywords.

Enables AI assistants to read and audit Tableau .twbx workbooks locally, providing tools for dependency tracing, field listing, SQL queries, filters, and semantic diffs.

Enables agents to perform local hybrid code search and code intelligence across a workspace, including semantic and full-text search, symbol lookup, file outlines, and caller analysis.