"Semantic Scholar" matching MCP servers:

Semantic React/TSX analysis MCP server that saves 70-90% tokens by using AST to retrieve precise component usages, prop flow, and state update information for AI coding assistants.

Builds a semantic graph of codebases across 38 languages and exposes 42 MCP tools for structured code understanding, search, and impact analysis.

Enables AI agents to query a repository's verified semantic model, including capabilities, ownership, contracts, constraints, impact, health, and verification status, without re-deriving project facts. It exposes read-only tools for inspecting, reasoning about, and verifying the provenance-tracked surface.

A semantic map of your .NET solution for AI coding agents. Analyzes a solution with Roslyn into a queryable code graph exposing 11 read-only tools (find_symbol, impact_analysis, find_implementations, etc.) over MCP. 100% local, no telemetry, MIT licensed.

Semantic code index and gatekeeper that exposes 14 read-only MCP tools for AI agents, enabling symbol search, definition lookup, reference finding, and impact analysis via static analysis of codebases.

Provides agents with read-only access to local code architecture graphs (structure from tree-sitter, semantic summaries from LLMs), including per-workspace graph/config/status/rebuild tools, while enforcing that topology comes only from static analysis.

Semantic concept graph MCP server for codebases — what your code means, not just what it does.

Enables AI agents to deterministically control Mermaid diagram layout through semantic operations like alignment, distribution, resizing, and history management via MCP.

A local-first whiteboard MCP server that enables AI agents to create, inspect, and update canvas diagrams and shapes collaboratively via 13 semantic tools.

Booster MCP turns complex codebases into understandable systems with semantic search, 3D visualization, and debugging tools.

Provides a semantic understanding of your codebase by parsing with tree-sitter and building a graph of symbols and dependencies. Enables AI assistants to navigate code, analyze changes, and discover architecture using 18 tools with minimal context overhead.

Enables AI coding assistants to turn live Miro architecture diagrams into semantic, queryable graph context, allowing them to inspect, search, and apply board topology directly to code.

CodeGraph MCP is a powerful standalone tool that parses your entire C/C++ codebase into a semantic knowledge graph and seamlessly exposes it to AI coding assistants via the Model Context Protocol (MCP). By providing AI (like Claude Desktop, Cursor, or Google Antigravity) with a structural map of your project—including caller/callee relationships, file dependencies, and dynamic function definition

Provides a standalone MCP server for tldraw diagrams, enabling clients to create, edit, and persist multiple canvases over Streamable HTTP with semantic protocol v2.