"Understanding and Building Probability Density Functions (PDF) Models" matching MCP servers:

A basic example of developing and running serverless Model Context Protocol (MCP) using Netlify Functions, demonstrating how to deploy and access serverless functions with customized URLs.

Compiles LaTeX resumes into PDFs via MCP tools, including validation, preview, template compilation, and LaTeX export, with security defaults like path isolation and no shell escapes.

Acts as an AI Gateway for MCP servers using Azure API Management, implementing the latest MCP Authorization specification.

A template for deploying MCP servers on Vercel with serverless functions. Includes example tools for rolling dice and getting weather data to demonstrate basic functionality and API integration patterns.

A cloud-based custom MCP server using Azure Functions that enables saving and retrieving code snippets with secure communication through keys, HTTPS, OAuth, and network isolation options.

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.

A deployable MCP server for Cloudflare Workers or Azure Functions that provides example tools (time, echo, math), prompt templates for code assistance, and configuration resources. Enables AI assistants to interact with edge-deployed services through the Model Context Protocol.

A production-ready foundation template for building Model Context Protocol (MCP) servers with FastAPI, featuring modular tools, comprehensive testing, and OpenShift deployment configurations. Includes automated transformation scripts to create custom domain-specific MCP servers.

Enables AI assistants to create and modify Google Apps Script projects, manage versions and deployments, run functions, and inspect execution history through natural language.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

Enables AI coding assistants to browse Unreal Python API documentation, search classes and functions, and execute Python code in Unreal Editor.

Provides workspace-scoped file operations and command execution tools for building Cursor-style agents, along with system prompts and tool definitions for LLM integration.

Enables interaction with Google's Gemini AI models including file uploads, conversation management, and batch API processing for large-scale tasks at reduced costs. Supports multiple Gemini models with advanced features like embeddings generation and automated workflow processing.

Provides a set of read-only Kubernetes functions via an MCP server, enabling interaction with Kubernetes clusters through agents or coding assistants like GitHub Copilot.

Manages a Neo4j knowledge graph of reusable Python functions, documentation, and environment variables, enabling dynamic script composition and execution with automatic dependency management and secret protection.

A lightweight framework for building and running Model Context Protocol (MCP) servers using FastMCP, providing tools for development, debugging, and server management.

Provides WebAssembly-based, sandboxed tools for JavaScript execution, binary disassembly, file recovery, steganography, image optimization, data processing, PDF redaction, secret scanning, and semantic search.

A TypeScript MCP server template with Zod validation, dual transport (stdio/HTTP), and modular architecture for building MCP-compatible tools, resources, and prompts.

Enables interaction with Groq models, including compound models, through tools for asking questions with real-time information and code execution.

Enables dynamic creation and execution of custom tools/functions in multiple programming languages at runtime, exposing them to MCP clients like Claude.