call_qtmodel_api

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

The description reveals important behavioral traits beyond the annotations: arguments are validated against the real signature before dispatch, causing wrong parameter names to fail fast rather than corrupting the model. It also discloses that destructive/long-running methods are blocked, adding safety context beyond the generic annotations. This is substantial behavioral transparency with no contradiction to the annotations.

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 well-organized with a clear header, guidance, blocklist note, Args section, and example. It is somewhat long due to bilingual text, but every section contributes to correct invocation. The front-loaded purpose and usage guidance help an agent quickly decide and proceed.

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?

Given the open-ended nature of a generic API call tool, the description provides the necessary context: how to discover signatures via list_qtmodel_api, what parameter formats to use, which methods are blocked, and a concrete example. It does not describe return values, but for an arbitrary API call that is impractical and not expected, and no output schema exists. The description is complete for its purpose.

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?

With 0% schema description coverage, the description fully compensates by documenting each parameter: api_object has allowed values ('mdb', 'odb', 'cdb'), method is an exact name with an example, and kwargs is a dict matching the real signature. This adds meaning far beyond the bare schema titles.

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 states a clear verb and resource: 'Call a qtmodel API method not covered by a curated tool'. It explicitly positions itself as an escape hatch, distinguishing it from the curated siblings and making its scope unambiguous. The example further clarifies the intended usage.

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 gives explicit guidance: 'prefer a dedicated tool when one exists', 'Discover the real signature with list_qtmodel_api first', and identifies blocked methods with alternatives ('use initialize_model / run_analysis instead'). This clearly tells an agent when to use this tool and when not to, including named alternatives.

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