"EarningsPrism MCP (Model Context Protocol) Configuration" matching MCP servers:

A production-ready MCP server template that connects LLMs and AI agents to external data, tools, and services with built-in OAuth 2.1 authentication, Redis-backed session management, and a modular tools engine.

A basic Python implementation of a Model Context Protocol server for educational purposes, using FastAPI and WebSockets.

MCP server for exploring and querying Supabase databases with interactive React widgets for schema browsing, table viewing, and SQL execution.

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.

A tool that helps manage and synchronize MCP server configurations across different AI assistant clients (Cline, Roo Code, WindSurf, Claude), automating the process of retrieving, listing, adding, and removing server configurations from client configuration files.

An MCP server for integrating AI models with client applications, allowing for structured interactions with APIs using command-line arguments for authentication and configuration.

Enables AI assistants to interact with Square's Connect API through the Model Context Protocol standard, allowing for operations like managing customers, processing payments, and handling inventory.

Enables creating Spotify playlists based on text descriptions by connecting Cursor editor to Spotify's API through OAuth authentication.

This server facilitates the invocation of AI models from providers like Anthropic, OpenAI, and Groq, enabling users to manage and configure large language model interactions seamlessly.

A server built with mcp-framework that allows users to extend Claude's capabilities by adding custom tools that can be used through the Claude Desktop client.

Serves MCP tools and resources over a Streamable HTTP endpoint, letting clients invoke methods such as echo through JSON-RPC 2.0 requests. Its methods are generated from an OpenAPI Specification file and can be customized via environment variables for port and API URL.

A TypeScript wrapper library for the Model Context Protocol SDK that provides a simplified interface for creating MCP servers with tools, resources, and prompts without needing to work directly with the protocol.

A server that allows AI models to interact with PostgreSQL databases through a standardized protocol, providing database schema information and SQL query execution capabilities.

A Python implementation of the MCP server that enables AI models to connect with external tools and data sources through a standardized protocol, supporting tool invocation and resource access via JSON-RPC.

Discovers local model runtimes such as Ollama, LM Studio, and LocalAI, and exposes their endpoint metadata to MCP clients like Copilot.

Enables AI coding agents to offload large file reads and boilerplate code generation to cheaper or local worker models through MCP, reducing token consumption and keeping context clean.

AIFeed Open Standard. AIFeed Protocol to the building tools section. AIFeed enables web publishers to provide verified, token-efficient content feeds for AI agents.

Provides time intelligence, persistent memory and complete traceability for AI coding agents. Enables sophisticated activity tracking, timezone management, smart reminders, and cross-session continuity with support for both per-project and centralized storage modes.

A Model Context Protocol server that provides tools to manage Keycloak users and realms, allowing you to create and delete users, list available realms, and view users in specific realms.

A simple MCP server implementation that provides AI models with basic Solana RPC methods and helpful development prompts.

Demonstrates an MCP server with calculate and text_stats tools over Streamable HTTP, integrating with LangChain/LangGraph and n8n agents.

Configurable MCP server that lets you define LLM-powered tools via JSON, enabling easy integration of multiple models (GPT, Gemini, Claude, etc.) as MCP tools without writing Python code.

Enables inspecting ONNX models, probing the deployment environment, and benchmarking inference latency and FPS either locally or on remote SSH targets such as Linux workstations and Jetson devices. Benchmark results are validated against FPS, P95 latency and model-size constraints to produce PASS/FAIL deployment gates.

A server that enables AI assistants to interact with LinkedIn programmatically for job searching, resume/cover letter generation, and managing job applications through standardized JSON-RPC requests.

Routes subagent tasks across Claude and OpenAI lanes by task kind and quota state, deciding whether to spawn a single model, run a duel of both vendors, or run both and ship the merge. It records duels with attested proof-of-run session ids, grades sides blind with one judge per vendor, and tracks per-kind standings and lane pacing.

A plug-and-play solution that enhances AI assistants by enabling them to create projects, manage configurations, and interact with Openfort's wallet infrastructure through 42 specialized tools.

Local-first documentation for AI agents

Enables multi-agent coordination with file locking, shared registry, async messaging, and architectural decision logging via MCP tools.

A comprehensive demonstration server that provides tools for calculations, weather, and note management alongside an interactive web interface. It showcases how AI assistants can seamlessly interact with external data sources and functions using the Model Context Protocol.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.