MCP Servers
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A context processing service based on large language models for handling and generating transactions and query requests related to the PumpFun protocol. It serves as an intelligent middleware layer that understands user intent and converts it to corresponding on-chain operations
pydantic-ai-researcher is a research system that orchestrates an asynchronous loop between two specialized agents: a research agent and an evaluator agent. The research agent answers complex queries using external MCP servers, while the evaluator agent assesses and refines these
This is a robust Python-based MCP Filesystem Server. It enables AI models and applications to securely interact with the host system's file directories through a defined set of tools, allowing for operations like reading, writing, moving, and listing files and directories.
QuerySculptor is a GraphQL query builder designed for AI agents that provides 26 structured tools via the Model Context Protocol (MCP). Instead of generating complex GraphQL queries from scratch, AI agents can build queries step-by-step with real-time validation and schema introspection.
Quorel turns any public website into a versioned, queryable API. Define a schema once in plain English, and Quorel extracts, structures, and refreshes the data nightly, full version history, diffs, and rollback included. The MCP server lets Claude or any agent browse datasets.
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From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
