"A server implementing NFS (Network File System) with MCP (Model Context Protocol)" matching MCP servers:

A Python-based server for managing and analyzing code context for AI-assisted development. Provides real-time code analysis, project structure analysis, dependency tracking, and secure API endpoints.

Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.

Enables querying Odoo's model registry (fields, inheritance, methods) without a database connection by loading the registry in-memory.

Read-only MCP tools for coding agents to audit deployment targets, detect fabricated code, review backlog, database schema, analytics, ML models, architecture docs, and decision lenses.

Reduces Claude's context window costs by automatically summarizing inactive files to their public interfaces using AST parsing, keeping only the full contents of the currently active file.

Enables intelligent handling of large files through smart chunking, search with regex support, line navigation, and streaming capabilities without loading entire files into memory.

Provides persistent memory and a codebase knowledge graph for AI coding assistants, enabling shared context across multiple tools like Claude, Cursor, and ChatGPT, with significant token reduction.

Enables AI agents to explore, analyze, and search file systems, with tools for directory trees, file statistics, duplicate detection, code counting, and multi-criteria file search.

Provides deep git file-level forensics tools to analyze detailed file histories, changes, and patterns within git repositories, focusing on individual file analysis rather than repository-wide operations.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

An MCP server that provides a flexible lens into directory structures and files, enabling LLM clients to efficiently navigate and understand codebases with minimal noise. It offers secure, gitignore-aware file access with tools like directory listing, file reading, and grep-like search.

Enables AI agents to create, validate, and export UML system models using structured MSON format. Supports generating PlantUML and Mermaid diagrams with direct export to System Designer macOS application.

Enables precise file patching using SEARCH/REPLACE blocks, with mandatory directory sandboxing, automatic Python QA (Ruff/Black/MyPy), and git versioning for rollback.

Enables comprehensive project analysis, intelligent search with regex, multi-file editing with automatic backups, and dependency mapping, all via natural language.

Enables Claude Web to securely inspect, search, and modify local project files through a sandboxed MCP server with atomic writes, path traversal protection, and sensitive file blocking.

Provides AI agents with secure, read-only file system access to analyze and understand project codebases, enabling multi-repository context aggregation and cross-project code tracing.

Enables repository context packing, file/symbol ranking, task-relevant selection, and applying structured patches via MCP.

An intelligent MCP server that acts as an AI tech lead for coding agents, providing expert validation, impact analysis, and strategic guidance before code changes are made.

Provides real structural understanding of a codebase via tree-sitter AST parsing before starting a task, reducing token waste and grounding acceptance criteria in code structure.

Fetches, summarizes, and reviews GitHub code diffs with asynchronous processing for large diffs.