"Noun Project" matching MCP servers:

Enables searching and retrieving icons from The Noun Project API with advanced filters for style, color, and size customization.

Enables AI assistants to search, browse, and download professional icons from The Noun Project directly within MCP-compatible environments. It supports SVG and PNG formats with customizable styles and provides optimized modes for free and paid API tiers.

Enables AI project feasibility evaluation and solution planning through structured reasoning stages, using Gemini API and web search for up-to-date research.

A tool for querying and analyzing materials data from the Materials Project database using natural language prompts, enabling materials scientists to explore properties, structures, and compositions of materials through conversational interfaces.

Local workspace memory for Claude Desktop. Indexes documents into a vector store with hybrid search, cross-session memory, auto-learn, and knowledge graph.

Provides access to API Ninjas Random Word API to generate random words by type (noun, verb, adjective, adverb) for creative writing, games, and language learning applications.

Enables AI agents to operate an isolated local Chromium browser through MCP, with semantic snapshots, ref-based actions, search, research, crawling, and CDP access.

Enables hybrid retrieval-augmented generation with semantic and keyword search over documents, supporting multiple formats and providing MCP integration for Claude Desktop.

A standard, forkable MCP App server with a Streamable HTTP endpoint, providing a self-contained UI resource and native tools like search_projects for project discovery.

Routes coding agents to the most relevant project documentation (decisions, intent, constraints) with provenance and freshness, providing tools for task routing, knowledge search, and document context.

Enables Claude to perform hybrid search across local documents by combining semantic vector retrieval and BM25 keyword matching for optimal context recovery. It supports multiple file formats including PDF, CSV, and Markdown, leveraging local Ollama models for private and efficient document querying.

A project search MCP server based on Milvus vector database, supporting semantic search, hybrid search, and exact scalar queries.

Enables natural language queries over employee and project data by exposing database-backed business tools to a Gemini agent, allowing the agent to fetch live details from PostgreSQL and answer with readable responses.

Provides a local, evidence-first knowledge base over source snapshots and summaries stored in SQLite, with bounded search and exact readback while preserving provenance.

Enables semantic search over a software project's Markdown and text documentation by indexing document chunks in Qdrant and exposing retrieval through MCP tools.

Enables AI assistants to discover and interact with GitHub repositories through standardized MCP tools, allowing exploration and retrieval of project information without direct API calls.

Provides persistent, cross-project memory for Claude over the Model Context Protocol, enabling it to remember goals, decisions, and progress between sessions with deterministic briefs and supersession-aware recall.

Web search for AI agents across 6 engines (Serper, Brave, Exa, Tavily, Firecrawl, Perplexity) through one search tool. Routes each query to the cheapest engine that clears a quality bar and caches repeats. Hosted, streamable-HTTP, BYOK supported.

Snipara is the Project Brain for AI coding agents: a shared Project Intelligence layer for persistent context, reviewed memory, code impact, verification evidence, and handoffs through MCP.

Unified search across all Claude Code and Gemini CLI memories, enabling AI assistants to recall and save cross-project knowledge via an MCP server.