query

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

With no annotations, the description carries the full burden and delivers useful behavioral traits: `format=json` is added automatically, the one-calendar-day-per-query constraint is mentioned, and `raw=True` returns untouched JSON. It does not cover error behavior or authorization requirements, but for a generic query tool this is substantial context.

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 compact and well-structured, starting with the core purpose, then usage guidance, parameter details, and a final note. Every sentence adds value; there is no fluff or repetition. Appropriate for a tool that needs this level of context.

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?

The tool is complex and arbitrary, but the description gives enough context for a knowledgeable agent: examples, constraints, and the raw option. It also references `list_query_types` for discovering types. It falls slightly short by not elaborating on the `app` parameter and the one-calendar-day constraint, but overall it is quite complete given the output schema exists.

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 the description must compensate. It does so effectively for the tricky parameters: `params` is explained as a dict of hyphenated wire names with a concrete example, and `raw` is explained. However, the `app` parameter is left undocumented, and `query_type` is only implied by name. This is a strong but not complete compensation.

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: 'Call an arbitrary mPulse query-type with arbitrary parameters.' It uses a specific verb ('call') and resource ('query-type'), and distinguishes itself from sibling tools by explicitly targeting query-types 'not covered by the explicit tools' with concrete examples.

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 when to use this tool: 'Use this for query-types not covered by the explicit tools (see `list_query_types`)'. It also points to the alternative (explicit tools) and provides examples, giving both a clear when-to-use and an implied exclusion for covered types.

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