List the well-known MeshKore skill vocabulary. Each entry is an { id, title, description, input_modes, output_modes } record that agents advertise in their A2A card. Use the returned ids as the skill argument to call_agent.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional substring filter matched against skill id, title, and description. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It states what the tool lists, the exact record structure, and how the results fit into A2A cards. The read-only nature is implied by 'List' and the vocabulary framing; missing pagination or ordering details are minor for this low-complexity tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two compact sentences carry the essential purpose, record structure, and usage instruction with no redundancy. The most important information is front-loaded and every clause adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only list tool with one optional, fully documented parameter and no output schema, the description is complete. It explains what the output contains and how to use it, so an agent can invoke it correctly without missing information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the query parameter is already fully documented. The description adds no extra parameter-level detail beyond the downstream usage note, matching the baseline expected when the schema handles parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and a clear resource ('well-known MeshKore skill vocabulary'), and it defines the record shape. This clearly distinguishes it from sibling tools like call_agent and search_agents, which operate on different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent to use the returned ids as the skill argument to call_agent, providing concrete downstream usage. It does not explicitly contrast with search_agents, but the context is clear enough for a simple vocabulary-listing tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool has a clearly distinct role: search_agents finds agents, list_skills returns vocabulary for skill IDs, and call_agent performs the actual invocation. There is no overlap or ambiguity between searching, listing, and calling.
All tool names follow a consistent verb_noun snake_case pattern: call_agent, list_skills, search_agents. The naming is predictable and uniform.
Three tools is minimal but well-scoped for a narrow purpose: discover agents, understand available skills, and invoke an agent skill. Each tool is necessary and earns its place in the set.
The set covers the full core workflow of finding agents, checking their skills, and calling them. A minor gap is the lack of a direct get_agent_by_id tool, but search_agents with flexible queries largely compensates.