MCP Servers & Agent Skills – Find, Compare & Submit
The Model Context Protocol (MCP) and agent skills give AI assistants
standardized ways to interact with external tools and services. MCP servers
connect AI models to real-world data and actions, while agent skills add
reusable capabilities — all with security and user control.
Learn more about the protocol → Why MCP Servers & Agent Skills?
Enhanced Capabilities
MCP servers and agent skills let AI assistants interact with databases,
cloud services, and APIs — expanding their ability to help with real-world tasks.
Secure Architecture
Built with security-first design, ensuring controlled access and
protecting sensitive information across servers and skills.
Universal Standard
A unified protocol that works across different AI models and services,
with servers and skills sharing a consistent integration experience.
Developer Friendly
Easy to implement and extend, with a growing ecosystem of
community-contributed servers and skills for various services.
MCP Servers & Agent Skills
operant-mcp
Security testing MCP server with 51 tools for penetration testing, network forensics, memory analysis, and vulnerability assessment.
SheetsData
Gives AI agents instant access to electronic component
datasheets — specs, pinouts, package info, absolute max ratings, and
application circuits extracted from manufacturer PDFs on demand. Read any
section, compare parts, validate designs against datasheet limits, and find
alternatives — all without touching a PDF.
MisarMail — Email Marketing MCP Server
Full email marketing platform for AI assistants — 32 MCP tools for campaigns, contacts, automations, A/B testing, AI content, multi-channel messaging (email + WhatsApp + push), deliverability monitoring, DMARC health checks, ecommerce revenue attribution, and advanced analytics.
marketplace-search-mcp
Unified MCP server for searching 20+ online marketplaces (TCGPlayer, Reverb, Grailed, Poshmark, etc.), verifying professional licenses, and querying NYC building violations
Stockfilm. Authentic Vintage Footage
Search and license 217,000+ authentic vintage 8mm home movie clips from the 1930s-1980s. 6 MCP tools with x402 USDC licensing on Solana and Base.
Global Chat MCP Server
Cross-protocol agent discovery infrastructure. Search, validate, and register AI agents across MCP, A2A, and agents.txt protocols. Directory of 18K+ MCP servers across 6+ registries with cross-search capabilities.
MCP Markdown Tools
Markdown processing for AI agents: TOC generation, linting, formatting, word count and reading time, table generation from JSON data.
MCP JSON Tools
JSON and data tools for AI agents: validate against schema, diff objects, transform/flatten, CSV to JSON, YAML to JSON conversion.
MCP GitHub Tools
GitHub analytics for AI agents: repo analysis, PR summaries, issue triage, release notes generation, contributor statistics.
MCP SEO Tools
SEO analysis for AI agents: meta tags, heading hierarchy,
broken links, keyword density, page speed metrics, sitemap parser. No API keys needed.
Frequently Asked Questions
What is an MCP server?
An MCP server is a lightweight service that exposes tools
and resources to AI assistants via the Model Context Protocol.
How do I publish my MCP server or agent skill?
Click the Submit button, paste your GitHub URL, and fill in the details. We support both MCP servers and agent skills.
What are agent skills?
Agent skills are reusable instruction sets that give AI coding assistants
specialized capabilities — like code review, migration guides, or
deployment workflows. They’re typically distributed as SKILL.md files
hosted on GitHub.
Which AI assistants support MCP servers?
Claude Desktop, Cursor IDE, Windsurf, OpenAI Agent SDK, and others that
implement the protocol.
MCP vs. OpenAI “function‑calling” — what’s the difference?
Scope: OpenAI function‑calling is an API‑specific JSON protocol that lets ChatGPT call developer‑defined functions inside a single request cycle. MCP is an open, transport‑agnostic protocol that works with any LLM or IDE and supports persistent state, resource streaming and multi‑tool suites.
Transport: Function‑calling occurs over HTTPS. MCP supports STDIO for local processes and Server‑Sent Events for remote servers, enabling CLI‑level latency and bi‑directional progress events.
Security model: Function‑calling inherits the security context of the backend service. MCP adds tool‑level capability descriptors, allowing clients to review & approve each server before use.
Bottom line: choose MCP when you need an open ecosystem where any LLM, IDE or agent framework can reuse the same server; choose function‑calling for quick single‑model prototypes on OpenAI.
Are MCP servers safe to run locally?
Yes—each tool declares required environment variables and permissions
up front; clients prompt you to approve before execution. For extra
safety, run servers in Docker or point to hosted endpoints.
How does MCP compare to other AI interoperability protocols?
MCP is focused on context and data delivery between AI models and external
systems, while protocols like Google’s Agent2Agent (A2A) target
agent-to-agent communication. MCP is designed to complement, not replace,
other standards in the AI ecosystem.