"Resources for Integrating Python, Data Science, MySQL, and Flask API Development" matching MCP servers:

A Model Context Protocol server that enables seamless integration between Claude AI and development tools like VSCode, Augment, Vercel, Airtable, and Square.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Paybond Kit for Python is the PyPI package for tenant-bound Paybond integrations and delegated agent spend controls. It opens hosted Gateway sessions, verifies capability tokens, authorizes tool-call spend, signs intent and evidence payloads, uses Stripe Connect, Stripe ACH Direct Debit, or x402 / USDC-on-Base settlement rails, reads tenant-scoped Signal, fraud, ledger, protocol, and A2A data, and

A Model Context Protocol server that allows AI agents to execute SQL queries against a MySQL database, supporting operations like reading data, creating tables, inserting, updating, and deleting records.

MCP Server for Google Cloud Healthcare API enables Agentic AI for a variety of FHIR-based digital health solutions, from smarter clinical workflows for Health Systems to Pre-Auth frameworks for Payers!

A Machine Comprehension Protocol server that enables AI assistants to interact with Kestra workflows through natural language, supporting operations like flow management, executions, backfills, and other Kestra features.

Enables ZCode to control a real Chrome browser, including tabs, navigation, page content, clicks, form filling, keyboard input, scrolling, screenshots, and JavaScript evaluation through a local MCP bridge.

Enables developers to build and deploy secure, typed Model Context Protocol servers with tools, resources, prompts, bearer-token authentication, per-tool scopes, rate limiting, and health probes over Streamable HTTP.

A middleware API that connects AI assistants like ChatGPT to Captain Data tools for extracting information from LinkedIn company and profile pages.

Provides pre-fetch routing intelligence for AI agents by recommending the cheapest reliable route (HTTP, browser, machine endpoint, or avoid) before visiting a URL.

A demonstration server implementing the Model Context Protocol with Server-Sent Events for streaming content production and management.

Provides full Devin API coverage via MCP servers, enabling programmatic management of sessions, knowledge, playbooks, secrets, schedules, and attachments, plus a documentation-only DeepWiki proxy.

Enables AI agents to drive real Google Chrome with stealth and captcha handling, using Patchright to avoid bot detection.

A meta API Gateway server that works with the Model Context Protocol (MCP), enabling AI assistants to connect to any API and access real-world data sources through standardized MCP tools.

An MCP server providing risk-aware API governance tools for AI agents. Agents call preflight checks before tool calls or merges to verify API contracts are safe. Returns risk score, blast radius, agent impact, economic cost, and a deterministic ALLOW/WARN/REQUIRE_APPROVAL/BLOCK decision. MCP endpoint: https://app.coderifts.com/mcp

An MCP server that exposes any REST API to LLMs through runtime discovery, providing tools to browse endpoints, fetch schemas, and apply business rules without hardcoding or schema duplication.

Converts any REST API into MCP-compatible tools instantly by providing an OpenAPI/Swagger spec, enabling seamless integration with AI agents.

Acts as a bridge between standard REST/OpenAPI web APIs and the Model Context Protocol, dynamically converting REST endpoints into MCP tools for LLM clients like Cursor, Claude Desktop, and n8n.

Enables ChatGPT to delegate writing, analysis, second opinions, and Japanese text polishing to Gemini through a single ask_gemini tool, without copying text to Gemini's web interface. Self-hosted on Cloudflare with your own Gemini API key and Google login.

A Python implementation of the Model Context Protocol that allows applications to provide standardized context for LLMs, enabling creation of servers that expose data and functionality to LLM applications through resources, tools, and prompts.