tcai_capability_model

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'query' and 'list', implying read-only behavior, but does not explicitly state side effects, safety, permissions, or error handling (e.g., what happens if action is not found). This lack of detail is a significant gap.

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?

The description is concise: two sentences with no wasted words. It front-loads the key information (model type and purpose) followed by usage. Every sentence earns its place.

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 query tool with one optional parameter, the description covers the main functionality. However, it omits details about the return format or data structure (e.g., what 'expected-valence map' looks like). Since no output schema exists, the description could be more complete by describing the output, but it remains mostly sufficient.

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?

The input schema covers the single 'action' parameter with a description. The tool description adds value by explaining the underlying 'action → expected-valence map' and the default behavior (listing if no action). This provides semantic context beyond the schema alone.

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 clearly states the tool's purpose: 'Query expected outcome of an action, or list the learned capability table.' It identifies the resource (capability model) and the action (query/list), distinguishing it from sibling tcai_* tools that handle different aspects like self-model or metacognition.

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 implies usage for querying expected valences or listing the table, but provides no explicit guidance on when to use this tool versus alternatives (e.g., tcai_self_model). No exclusions or alternative recommendations are given, leaving the agent to infer context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.