"Proof-of-Concept Examples for Multi-Component Processes (Model Context Protocol)" matching MCP servers:

MCP server that prepares Proof of Invention (POI) transaction requests for submission

Enables AI agents to understand, inspect, create, validate, build, and document Jekyll component frameworks through Jekyll-aware tools, resources, prompts, sandboxed filesystem access, and configurable write modes.

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 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.

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.

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.

MCP server for managing multiple SSH servers via AI assistants, offering tools for remote command execution, file operations, and system monitoring.

50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.

Enables atomic batch find-and-replace edits across single or multiple files, reducing tool calls and token usage for efficient code refactoring.

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 an AI agent to host a shared folder or join an invite link as a participant in a collaborative editing session, reading, editing, and writing the files the room shares. Runs the same sync engine and wire protocol as editor clients, so an agent and a person or another agent converge on the same documents and see each other's changes.

A self-hosted MCP server that gives an AI client read, search, triage, drafting, attachment/export and label-management access to several Gmail or Google Workspace accounts at once, with each call naming an explicit account. Sending is disabled unless armed twice, mail deletion is impossible, and tokens, exports and audit data stay local.

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.

A collection of small MCP servers for macOS automation that expose Apple Mail, MoneyMoney, and Spotlight functionality as tools via AppleScript and command-line, enabling AI applications to search mail, manage contacts and transactions, and perform system-wide file searches through natural language.

Enables ChatGPT to inspect a selected local project on demand, retrieving project overviews, code search results, file excerpts, and Git context without pasting entire repositories into the conversation.

A thin, local, read-only MCP gateway that exposes governed context documents and producer manifests as logical resources, enabling AI clients to access them without arbitrary filesystem access.

Enables AI agents to read, search, create, and append Markdown notes from a local vault (e.g., Obsidian) via MCP, allowing natural-language note management through compatible clients.

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

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.