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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context beyond that: it emphasizes the result universe, metrics coverage, and that output is factual only — no recommendations, price targets, or grey-market premium. This sets correct expectations without contradicting 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?

Three sentences, all information-dense, with the primary action front-loaded, then use case, then the factual-data constraint. No filler or repetition of schema details.

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 description is complete enough for an agent to understand the tool's purpose, main input pattern, and constraints. It does not explain pagination or sort defaults, and there is no output schema, but the essential filter-construction guidance and factual scope are present. A small gap remains around result format and pagination behavior.

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 only 50% schema description coverage, the description compensates for the most important ambiguity: the `filters.field` property is a plain string, and the description directs agents to `screener_fields` for valid field names. It also conveys the breadth of available metrics (108 across named categories). It adds less for page/limit/sort parameters, but those are secondary to the core filtering workflow.

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 ('Filter') and names the exact resource (MarketPing's universe of 5,400+ NSE/BSE companies) and scope (108 fundamental metrics). The 'find companies where…' phrasing makes its screening role unmistakable and distinguishes it from complementary siblings like `company_snapshot` or `screener_fields`.

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?

It clearly states the intended use case: 'Use for find companies where… questions.' It does not explicitly name sibling tools to avoid, but the context is specific enough that an agent can route screening questions here. The factual-data caveat also helps prevent misuse for recommendation-style queries.

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