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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, fully covering the safety profile. The description adds valuable context beyond annotations: it mentions the AI classification, clarifies that the data is factual only, and explicitly lists exclusions (no recommendations, no price targets, no IPO grey-market premium). This helps the agent set user expectations.

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 two sentences with no waste. The first sentence front-loads the core concept and returned fields; the second sentence gives usage guidance and boundaries. Every clause 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 read-only query tool with no output schema, the description lists the output dimensions (subject, category, document type, importance, sentiment), explains both company-specific and market-wide usage, and states data constraints. An agent has enough to decide when to call it and what to expect in return.

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 100%, so both parameters (days and company) are already well-documented. The description reinforces that omitting company gives market-wide results, but it does not add new parametric semantics beyond the schema, so the baseline 3 is appropriate.

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 names a specific resource ('Recent NSE/BSE corporate filings') and the AI-classified fields (subject, category, document type, importance, sentiment), making the tool's purpose unmistakable. It also distinguishes itself from siblings by calling itself 'MarketPing's core dataset' and giving concrete query 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 gives explicit use cases: 'what has X announced lately', 'any news on X', and 'what is happening in the market today' for the no-company case. It also sets boundaries by stating 'Factual data only' and listing what the tool does not provide (buy/sell recommendations, price targets, IPO grey-market premium). It does not name alternative tools, but the context is clear.

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