"Proof-of-Concept Examples for Multi-Component Processes (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.

Local MCP server that lets Claude Code delegate bounded, read-only tasks to external AI models (Codex, Grok, local OpenAI-compatible servers) via CLIProxyAPI.

MCP server providing virtual memory for AI context with durable SQLite storage, retrieval, and bounded prompt assembly to manage context windows.

Enables LLMs to perform conceptual search over local PDF/EPUB documents using a RAG pipeline with corpus-driven concept extraction and WordNet enrichment.

Provides two MCP servers: Variant Authority for managing component variant manifests and Radix Primitives for accessibility composition using Radix UI. Together they serve as the source of truth for design-to-code workflows.

Connects Codex to local and cloud language models from MoE4All, GLM, DeepSeek, and OpenAI-compatible endpoints through a controlled MCP worker boundary, enabling read-only worker analysis, provider configuration, and VS Code model setup while keeping write authority with Codex.

Concept-test your product with synthetic consumers, straight from Claude Code. It turns free-text reactions from roleplayed personas into purchase-intent reports using Semantic Similarity Rating.

Summarizes Slack activity and GitLab merge requests with OpenAI to help generate daily standups, weekly reports, or quarterly check-ins.

Verifies blockchain-anchored timestamp proofs entirely on the user's machine, with five tools: hash a file, verify a file against a proof, verify a known hash against inline proof data, explain what a proof asserts, and create a new proof through ProofLedger. Verification recomputes the SHA-256 and walks the Merkle path locally instead of asking the issuer; only create uses the network.

An MCP server that queries multiple Ollama models and combines their responses, providing diverse AI perspectives on a single question for more comprehensive answers.

An MCP server that integrates AI retrievals with NASA's Common Metadata Repository (CMR), allowing users to search NASA's catalog of Earth science datasets through natural language queries.

A collection of Model Context Protocol implementations ranging from a minimal hello-world to an autonomous AI agent for software development.

Enables access to a local Markdown knowledge base via MCP, currently only providing a ping tool.

A conversational application server that integrates LLM capabilities via Ollama with vector memory context, supporting multiple users, sessions, automatic history summarization, and a plugin system for executing real actions.

Exposes design system component documentation to LLM clients via MCP, allowing listing and retrieving component contexts from Markdown files with YAML frontmatter.

Use multiple Cerebras models from Claude Desktop & Claude Code with automatic model selection.

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.

MCP server that provides six verification tools (Lean proof checking, axiom audit, bound, gridlock check, certificate verification, residency check) with honest status reporting (ok/failed/unavailable) to prevent agents from claiming unchecked proofs passed.

An MCP server that scans React and Vue projects, extracts component metadata (props, slots, events, imports, usage), and exposes it to AI coding agents via structured tools.

Enables AI to precisely use local Vue components by extracting their Props, Events, and Slots via AST analysis.