The Tool Desk
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What is missing: access, instructions, or setup?
| What the agent lacks | Likely place to look | What to check |
|---|---|---|
| Access to live or external data, a service, a tool call, or a controlled action | MCP connection or server | Host support, server reachability, transport, credentials, initialization, and allowed tools |
| A repeatable procedure, domain workflow, conventions, or supporting reference material | Skill | Skill format and installation, its SKILL.md, discoverable name and description, supporting files, and whether the host loads it |
| Both access and a reliable way to use it | Both | Verify the connection first, then give the agent instructions for when and how to use it |
| A capability that should already be available but is not | Configuration or runtime | Host support, initialization, credentials, dependencies, working directory, and tool restrictions |
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; connection setup can also involve transport, authentication, and controls over which tools are available. See the Model Context Protocol introduction and OpenAI’s MCP connections documentation.
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A skill is reusable procedural guidance, commonly packaged with a SKILL.md manifest and optional scripts, references, or other supporting files. OpenAI’s Skills documentation describes discovery through metadata such as a skill’s name and description; in that documented flow, the full instructions are read when the skill is invoked. Anthropic describes a related progressive-disclosure approach in its Agent Skills overview, where metadata is loaded first and instructions and resources are accessed as needed. Formats and loading behavior are host-specific.
When should you add an MCP server?
Consider MCP when the missing capability is a route to an external system: the agent needs to retrieve current information, call a service, or perform an action through a tool. Before adding a server, confirm the target agent host supports the connection method and that the server exposes the tool the workflow needs. A configured connection is not proof that every tool is available to the agent; access may be limited by host settings or tool restrictions.
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For implementation details, consult the documentation for the host you are actually using. OpenAI’s MCP connections guide covers connection configuration, including transport and authentication. Those details should not be assumed to apply identically to another runtime.
When is a skill the better fit?
Look at a skill when the agent can already reach the necessary tools or information but needs consistent instructions about how to carry out the task. A skill can describe a procedure, conventions, decision rules, and relevant reference material. It does not, by itself, grant access to a service that the host or agent cannot reach.
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Check that the skill is installed in a format the host supports and that its manifest is discoverable. A clear name and description should tell the agent both what the skill does and when it applies. The OpenAI Skills guide documents its format and discovery flow; Anthropic’s overview explains its own approach. Do not assume a skill built for one host will be loaded by another without compatible support.
When do you need both?
Use both when a task requires external access and a dependable procedure for using it. The connection makes the external capability available; the skill can tell the agent which information to retrieve, what steps to follow, and how to apply the result. Anthropic illustrates this pattern with a Notion connection paired with a meeting-preparation skill in Extending Claude’s capabilities with skills and MCP.
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For example, an agent might have a connection to a workspace but still need instructions for preparing a meeting brief from relevant notes. If the connection is missing, fix access first. If the agent can retrieve the notes but handles them inconsistently, add or improve the workflow instructions.
How to tell whether the problem is configuration instead
Before adding another capability, check whether the one you expect is installed, reachable, initialized, and permitted. A broken connection or undiscovered skill can look like a missing feature.
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- The development kit includes AGX Orin 64GB module and can emulate all Orin modules. It utilizes the Ampere GPU architecture, next-generation deep learning and vision accelerators, high-speed I/O, and fast memory bandwidth. You can leverage the largest and most complex AI models to develop solutions for problems such as natural language understanding, 3D perception, and multi-sensor fusion.
- Jetson runs AI software and provides application frameworks for specific use cases, such as Isaac for robotics, DeepStream for visual AI, and Riva for conversational AI. Using Omniverse Replicator for Synthetic Data Generation (SDG) can save you significant time; while fine-tuning pre-trained AI models from the NGC catalog using the TAO toolkit can further enhance your results.
- Yahboom offers four kits for users to choose from. The AIlarge model voice module utilizes examples of AI large models and multimodal models; it provides 1TB/2TB SSDs with pre-flashed driver image files; and an 8MP USB industrial camera for image processing.
- It offers various online and offline mainstream AI large model development materials. The system is pre-configured with AI vision examples, ROS case studies, and AI large models. It supports offline/online deployment of large models for voice interaction, real-time video analysis, and visual positioning, helping you quickly get started with localized AI agent development.
- Confirm host support. Verify that the agent runtime supports the MCP transport or skill format you chose.
- Check server availability and startup. For OpenAI’s Agents API, an HTTP server must be reachable from OpenAI. A stdio server requires its executable and dependencies to be installed; the documented inline configuration also requires an absolute working directory.
- Verify authentication. Make sure credentials match the connection method and server URL.
- Inspect tool access. OpenAI’s configuration can restrict discovery and calls with
allowed_tools. Ifrequiredis set totrue, an initialization failure fails the turn rather than allowing it to proceed without that connection. - Check initialization and logs. A server that is configured but fails to initialize cannot provide its tools.
- Check skill discovery. Confirm the skill directory and
SKILL.mdare in the installation location recognized by the host, and that its name and description make its purpose clear.
These specific runtime checks come from OpenAI’s Agents API MCP documentation; equivalent controls and failure behavior may differ on other platforms.
If a server offers skills, check their origin too
An MCP server can also serve skills. The MCP Skills extension specification identifies a skill by its originating server plus its skill URI. It requires resource manifests either to enumerate skill files or declare them dynamic. That origin matters: hosts should not treat same-named or same-URI skills from different servers as necessarily identical.
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When debugging a skill obtained this way, check not only its name but also which server supplied it and whether the host can discover its files. This is separate from deciding whether the task needs external access, procedural guidance, or both.
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