"Using Solana RPC and gRPC Functions for Advanced Operations" matching MCP servers:

Enables easy gRPC requests and Protocol Buffer file information retrieval through natural language commands. Supports unary RPCs with SSL, timeout configuration, and response time statistics.

Enables AI agents to access and search up-to-date Solana documentation, get canonical spec references, and fix Anchor/Pinocchio Solana programs via MCP tools.

Provides intelligent summarization capabilities through a clean, extensible architecture. Mainly built for solving AI agents issues on big repositories, where large files can eat up the context window.

A Model Context Protocol server that enables AI models to interact with the Solana blockchain, providing RPC methods, wallet management, DeFi trading capabilities, and Helius API integration for enhanced Solana development.

A multi-agent operations assistant that answers business questions by searching local documents and order records.

An MCP server exposing internal business operations as tools — task management (create, list, update status) and RAG-style semantic search over an internal knowledge base (leave, expense, and onboarding policies) that any MCP-compatible AI agent can call directly for grounded, non-hallucinated answers.

MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.

A proof of concept MCP server for Apache OFBiz

Enables asking questions and performing semantic searches against an Inkeep knowledge base. Deployable to Vercel with Next.js.

MCP server for searching an archive of Solana developer Telegram chats. Enables hybrid search across ~94K messages from Divers and Solana Dev RU chats, with tools for search, stats, and history.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

A TypeScript-based server to interact with ArangoDB using the Model Context Protocol, enabling database operations and integration with tools like Claude and VSCode extensions for streamlined data management.

WordPress plugin that runs an MCP server on your own site: search and read published posts and pages, look up WooCommerce products, describe the site as schema.org types and query site figures, with drafts only when the site enables them. Signed-in access by default; anonymous access and OAuth are optional and off.

This MCP server provides a cost-optimized personal-knowledge brain, enabling AI agents to query imported documents via hybrid search and synthesis at minimal monthly spend.

Provides structured local memory for Claude Desktop, reducing token usage by over 95% through intelligent retrieval and layered storage.

Turns any static website into an MCP-searchable knowledge base by deploying a Cloudflare Worker that provides full-text search tools, enabling AI assistants to search and retrieve content from your site.

An integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.

Enables cloud and platform engineers to investigate AWS incidents by grounding responses in approved runbooks and operational evidence, with authenticated access, citations, observability, and human approval before any write action.

Provides AI agents and RAG pipelines with production-grade web search, deep research, news, academic, extraction, crawling, and source verification tools. Enables citation-anchored, deduplicated, fresh, and security-hardened evidence gathering from across the web.

A meta-MCP server that acts as a universal gateway, allowing users to discover and execute tools from thousands of other MCP servers through semantic search. It dynamically loads servers on demand and provides standardized functions for searching, discovering, and running tools across the entire MCP ecosystem.