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If your agent cannot reach an external service or retrieve its data, check its MCP connection. If it can reach the service but does not follow a reliable procedure for using it, check its skill. Some workflows need both: MCP provides access, while a skill explains when and how to use that access. This is a diagnostic rule of thumb, not a guarantee—agent hosts support different features and configuration options.
What’s missing: access, instructions, or setup?
| What the agent lacks | Likely cause | What to check |
|---|---|---|
| Access to live or external data, a service, a tool call, or a controlled action | An MCP connection or server | Whether the host supports MCP; server reachability; transport; credentials; initialization; and permitted tools. See the MCP overview and OpenAI MCP connections. |
| A repeatable procedure, domain workflow, conventions, or reference material | A skill | Whether the host supports the skill format and installation method; whether it discovers the skill; and whether its manifest and supporting files are in place. See OpenAI Skills and Anthropic’s Agent Skills overview. |
| Both access and a consistent procedure for using it | An MCP connection and a skill | First confirm the connection works; then give the agent instructions for when and how to use it. Anthropic describes this pattern with a Notion connection and a meeting-preparation skill in Extending Claude’s capabilities with skills and MCP. |
| A capability that should already be available but isn’t | A configuration or runtime problem | Check host support, initialization, credentials, dependencies, reachability, tool restrictions, and skill discovery before adding another capability. OpenAI’s MCP connection documentation describes relevant checks for its Agents API. |
What MCP provides—and what it doesn’t
Model Context Protocol (MCP) standardizes how an AI application connects to external systems, including data sources and tools. An MCP server publishes tool definitions and handles tool calls. The connection can also involve a transport method, authentication, and controls over which tools are available.
That makes MCP a likely fit when the agent needs a capability it cannot reach through its existing tools. It does not, by itself, guarantee that the agent will use an available tool correctly or follow your organization’s preferred process. For those needs, provide clear procedural guidance as well.
What a skill provides—and what it doesn’t
A skill packages reusable procedural guidance, usually in a directory with a SKILL.md manifest. It may include scripts, reference documents, or other supporting files. A skill can explain a domain workflow, conventions, and how to approach a task; it is not automatically a connection to an external service.
Discovery matters. In OpenAI’s documented flow, the agent sees a skill’s name and description during discovery, then reads the full instructions when it invokes the skill. Write the description to say what the skill does and when it should be used. Anthropic also documents progressive disclosure: metadata is loaded first, with instructions and supporting resources accessed as needed. These behaviors describe the respective platforms, not a universal implementation shared by every host.
When the workflow needs both
Suppose an agent must retrieve information from a connected workspace and prepare a meeting brief in a consistent format. The MCP connection gives it a route to the workspace; a skill can specify which information to gather and how to turn it into the brief. Anthropic uses a Notion connection and meeting-preparation skill to illustrate this complementary pattern.
Build and diagnose the layers separately: verify that the connection exposes usable tools, then verify that the instructions tell the agent when and how to use them. A skill cannot compensate for a missing connection, and a connection alone does not supply a dependable procedure.
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Check configuration before adding a capability
When something that should work is unavailable, adding another server or skill may hide the actual fault. The following implementation checks are documented for OpenAI’s Agents API; do not assume other hosts use identical settings or requirements.
- Confirm the host supports the feature. MCP support does not imply support for every skill format, and support for skills does not imply MCP support. Check the target runtime’s current documentation.
- Check server reachability and transport. OpenAI’s documentation says HTTP servers must be reachable from OpenAI. For a stdio server, the executable and its dependencies must be installed.
- Check the working directory. OpenAI’s documented inline server configuration requires an absolute working directory.
- Verify authentication. Confirm credentials match the connection method and server URL.
- Check initialization and tool access. Review whether the server initialized and whether
allowed_toolsrestricts the needed tool. OpenAI documents that settingrequired: truemakes initialization failure fail the turn. - Check skill discovery and files. Confirm the skill directory contains its
SKILL.mdmanifest and any referenced supporting files, and that its name and description make its purpose clear to the host.
If skills arrive through an MCP server
The MCP Skills extension specification defines a skill’s identity using its originating server plus its skill URI. It requires resource manifests to enumerate the skill files or declare them dynamic. Hosts should not treat skills with the same name or URI from different origins as the same skill. These are extension-specific requirements; confirm that the host and server support the extension before relying on them. See the MCP Skills Extension Specification.
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