"Understanding Inference Models" matching MCP servers:

A Model Context Protocol server for exploring and analyzing CSV files, providing tools for inspection, sampling, schema inference, statistics, filtering, and more.

Provides tools for file system operations and text generation using OpenAI-compatible models via the Model Context Protocol, supporting both stdio and HTTP transports.

Enables continuing a local coding task from your own web ChatGPT account by exposing project-scoped read and apply tools over MCP, with no inference API dependency.

A proof-of-concept MCP server that enables reading local files and performing basic arithmetic operations. It provides a simple foundation for understanding how tools are exposed to MCP clients.

This server provides an API to query Large Language Models using context from local files, supporting various models and file types for context-aware responses.

MCP server that converts file contents into compact, line-numbered PNG images for vision models to read, reducing token usage by roughly 7x for large files.

Enables AI assistants to inspect, create, and report on IFC/BIM files locally, and to open models in a viewer with BCF viewpoint support.

An MCP server that gives AI models a persistent notebook backed by a folder of Markdown files, with tools to discover, read, write, edit, and delete notes.

Enables read-only scanning and text extraction from PDF, DOCX, PPTX, SVG, and PNG files in a local folder, providing the raw text to AI models for summarization or analysis without an external LLM API.

A plain-files brain template and MCP server that lets models read owned, portable working memory via list, read, and grep-style search tools.

Token-efficient skim-then-expand I/O for agentic models, enabling lossless reading of large files via skeletons and exact expansion with anchor IDs.

A local stdio MCP server for generating images using Google's Gemini image models, saving to disk as JPEG.

MCP server for TQS structural engineering CAD/analysis software, enabling Claude to read building models, extract drawing data, run structural processing, and script against the TQS API.

Enables rendering PDF pages to images and returning them directly as multimodal content for models, with controls for page ranges, resolution, and output dimensions.

Enables large language models to refine Office documents (Word, Excel, PowerPoint) via interactive visual selection of text and fine-grained prompt annotations in a browser window, with version history and precise location-based edits.

Provides secure filesystem access for AI models through the Model Context Protocol with strict path validation, file operations, directory management, and system command execution within predefined directories.

A universal resource server that enables AI models to mount local folders, scrape web pages into Markdown, and query SQLite databases without writing code. It features a real-time TUI dashboard and persistent configuration for managing diverse knowledge sources.

Acts as the 'Hands and Eyes' for an Autonomous AI Agent, bridging Large Language Models and your local development environment to enable safe file manipulation, context reading, command execution, and documentation verification.

A secure Model Context Protocol server that provides controlled filesystem access within predefined directories, enabling AI models to perform file and directory operations with strict path validation.

A comprehensive MCP server that enables AI models to perform local file operations, command execution, and task management across multiple platforms. It features advanced capabilities like row-level file editing, directory searching, and system monitoring with built-in security filters.