"Understanding and Building Probability Density Functions (PDF) Models" matching MCP servers:

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

Converts vector PDF circuit schematics into evidence-preserving SchematicIR JSON with tools to inspect, convert, and validate schematic PDFs.

Enables AI agents to analyze and diff binaries by driving Ghidra headless or inside its GUI, exposing native reverse-engineering tools such as listing functions and segments, searching strings, decompiling, disassembling, and following cross-references. Ships with a devcontainer and supports local stdio or remote HTTP deployment.

Enables MCP clients to load eight on-demand coding skills and inspect project repositories through bounded, read-only map, search, read, diff, checks, and style tools. It keeps context lean by loading skill bodies only when needed and limiting repository scan and output sizes.

A field-level semantic change impact analysis MCP server for Claude Code, enabling natural language queries to find affected code when modifying fields, strings, or functions across multiple languages.

Cross-repository code intelligence server that enables AST-based search for functions, classes, types, and API routes across local JavaScript/TypeScript and PHP repositories.

A deterministic structural code map server for AI agents, giving them the shape of a codebase (imports, exports, classes, functions, signatures, comments, TODO-markers) without reading whole files into context. Powered by tree-sitter WASM grammars, it runs anywhere Node 18+ works.

Codebase intelligence and context engine for AI coding agents: search, symbol lookup, impact analysis, and context building via MCP.

An MCP server that helps AI agents comprehend a codebase by providing tools for navigating, searching, and understanding code structure and history.

A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP).

Provides the real exported API surface and type signatures of any npm package, extracted from its published TypeScript declarations, enabling AI coding agents to use functions accurately and avoid inventing nonexistent APIs.

An MCP server that gives Claude structural understanding of a codebase—dependency graph, entry points, complexity ranking, and import cycles—without reading every file into context.

Local MCP tool for understanding JSON Schema files while planning tests, summarizing types, required fields, constraints, and generating placeholder paths without copying data.

Assigns personality types to functions, rates variable name energy, and suggests aesthetic alternatives for code, providing an unscientific yet entertaining vibe assessment.

Provides AI coding agents with five intelligence layers (dependency graph, git history, documentation, architectural decisions, code health) via nine MCP tools, enabling deep codebase understanding and reducing exploration cost.

Stop pasting your file tree into Claude. Give any AI assistant real architectural understanding of a codebase — local, private, zero‑config.

Enables building MCP servers in TypeScript where tool schemas, descriptions, and validation are automatically generated from class methods, their TypeScript types, and JSDoc comments at compile time.

An MCP server that helps AI coding agents navigate codebases, discover reusable functions, and inspect callers before making changes.

Integrates Google Gemini 3.1 with Claude Code to provide web search, large codebase analysis, multimodal image understanding, and creative brainstorming, leveraging Gemini's 1M token context and Google Search grounding.

Enables AI-assisted FlutterFlow development by reading, exploring, and understanding FlutterFlow projects through Claude and other MCP-compatible clients.

Enables binary comparison capabilities by leveraging IDA Pro and BinDiff to analyze similarities and differences between files. Users can perform automated function analysis to identify changed functions and compare original binaries against patched versions.

Show an AI coding assistant only the code that matters for a change — callers, callees, and related functions packed into a token budget, with self-grading retrieval.

High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.

Enables coding agents to rank repository files, functions, and web search results using Jev-based probabilistic scoring, returning locations and confidence scores for the agent to review.

A fully local RAG MCP server for semantic code search and code intelligence, using AST-level chunking and hybrid search to pinpoint functions, classes, and APIs. No cloud, no API keys, zero setup.

locidx

Enables agents to explore a codebase as a graph via MCP, searching for relevant functions and their connections, and retrieving cited code chunks with callers, callees, and neighbours.

Provides structural, queryable understanding of a Python codebase via MCP tools, enabling direct lookups for callers, dependencies, and class hierarchies without repeated grep/read cycles.

A local semantic memory and code-indexer that uses AST parsing for structural understanding and persists architectural decisions to help AI assistants bypass context window limits.

Test coverage intelligence MCP server for TypeScript and JavaScript projects. Analyzes coverage reports to find untested functions, computes cyclomatic complexity, and suggests test cases locally.