inspect_model

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

With no annotations, the description carries the full burden. It usefully discloses the execution model ('fresh native Python instance') and the returned contents, which is genuine behavioral context. It does not state whether the operation is read-only, what file path/permissions are required, or any performance characteristics of spawning a new instance.

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?

A single, front-loaded sentence with no filler; the verb and resources lead. Slightly dense via the semicolon clause, but nothing is wasted.

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 2-parameter tool with no annotations, no output schema, and 0% schema coverage, the description should compensate by clarifying parameters and preconditions. It names the return values (helpful absent an output schema) but leaves the input parameters and file requirements unexplained.

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 0%, so neither 'model' nor 'file_type' is documented beyond a title and a default. The description's 'keyword/d3plot' implies file_type values, but neither the meaning of 'model' (e.g., a path) nor the full set of file_type options is explained, leaving the parameters largely opaque.

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?

States a specific verb ('Open') plus the resources ('keyword/d3plot') and enumerates the returned data (counts, user part IDs, state times), so the agent knows what the tool does. However, it does not distinguish itself from siblings like inspect_keyword_deck or inspect_d3plot_database, which appear to cover overlapping ground.

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 phrase 'in a fresh native Python instance' hints at an isolation scenario, but there is no explicit when-to-use guidance and no named alternative among the many sibling inspect_* tools. The agent is left to infer when this is preferable to inspect_keyword_deck or the d3plot inspectors.

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