"Onboard MCP (Model Context Protocol) Tool or Server" matching MCP servers:

Provides a standardized way to interact with MinIO object storage, allowing access to text files, binary files, and bucket contents while supporting operations like listing buckets/objects, retrieving objects, and uploading files.

A secure, production-grade MCP server that provides filesystem operations, AST math evaluation, and system diagnostics for LLM agents.

A TypeScript prototype for an MCP-style registry providing local utility tools for shell command execution, filesystem access, and search functionality. It features Zod input validation and project manifest resource exposure for embedding into larger MCP implementations.

A custom MCP server with 6 utility tools (file search, file reading, math calculation, JSON formatting, time query, system info) that demonstrates MCP protocol workflow and integrates with LangChain agents.

Encoding-aware, indentation-smart file tools for AI coding agents. 20+ tools including read/edit with automatic encoding detection, smart indentation conversion, SSH, SFTP, process management, and system utilities.

A unified context layer that connects your local data — repositories, documents, remote machines, and notes — to LLM interfaces through the Model Context Protocol (MCP).

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.

A Model Context Protocol (MCP) server that provides VSCode context and filesystem operations for AI assistants.

MCP server that gives AI chat real vision by reading images, files, logs, and zip archives via local OCR in any language, with privacy-first processing.

MCP server that helps AI coding agents understand a repository by providing lightweight tree/map, code search, and token-budgeted context packing tools without dumping the entire monorepo into the prompt.

Enables AI coding agents to persist structured long-term memory (gotchas, architecture, API notes) in a .context folder and sync across devices and agents via Git.

MCP server providing lossless file reads with deduplication, per-repo token metering, and HMAC-signed context receipts. Enables auditable, vendor-neutral measurement of what an AI agent saw.

Enables local OCR transcription of images using tesseract.js, with optional low-token AI-generated descriptions, folder batch processing, and compatibility with MCP clients like ChatGPT, Claude, opencode, and Cursor.

Provides LLMs with secure, read-only access to local documentation by scanning directories, extracting content from PDF, DOCX, Markdown, and text files, and performing keyword searches.

Enables managing personal information with dynamic topic-based organization (tasks, meetings, contacts, etc.), supporting optional OTP authentication and AES-256 encryption for sensitive data with automatic backups.

A simple MCP server for local documentation with RAG capabilities, enabling AI assistants to access and search local documents.

Local MCP server enabling Codex and ChatGPT to read/write files, execute commands, manage processes, use Git, and inspect images on the user's machine with full privileges.

A local MCP server that runs Llama models entirely on your machine. No API keys, no cloud costs, 100% private and offline-capable.

Enables local, bounded routing and evidence judgments for Codex tasks, with tools for reading file context, checking outputs, and selecting registered workflows while keeping execution and review with the client.

Provides a bulk_read_files tool that reads specified files and forwards them to a remote backend for summarization, so agents receive concise summaries instead of raw file contents.