An AI agent becomes more useful when it can reach the right context at the right time. Model Context Protocol, or MCP, is a standard way for an agent client to connect to servers that expose tools, resources, and context.
For a skills directory, the important part is discovery. An agent should be able to search capabilities, inspect their metadata, and read a source file before anyone decides to install or run code.
Think of MCP as a contract
The client knows how to ask. The server describes what it can provide. A good MCP server makes those boundaries visible instead of hiding a large collection of side effects behind one vague command.
Read-only is a useful default
Search, metadata, provenance, and source inspection are low-risk starting points. Installation, file changes, payments, messages, and external actions deserve an explicit decision. EveryAI’s MCP gateway follows that metadata-first approach: an agent can find and inspect a skill, but it does not silently install or execute third-party code.
Local and shared connections
Local stdio is a simple fit for a desktop or coding agent. A shared Streamable HTTP server can work for a team, but it needs TLS, authentication, rate limits, logs, and a clear owner. If a server is reachable from the public internet without those basics, the convenience is not worth the risk.
What to look for in an MCP server
- Tool names and descriptions that explain the actual boundary.
- Input schemas that reject ambiguous or unsafe requests.
- Resources that are explicit about their origin and freshness.
- Authentication and auditability for anything beyond read-only discovery.