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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds context beyond that: the data source (NSDL), the update cadence ('as NSDL publishes them' implying daily), and what it deliberately excludes (no recommendations, price targets, grey-market premium). This gives an agent a richer behavioral model 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?

Two sentences total: the first front-loads the purpose and content, the second adds usage guidance and exclusions. There is zero filler, and every clause earns its place. The structure is ideal for an agent to parse quickly.

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 has 7 optional parameters, no required ones, and no output schema, but the description accurately summarizes the data scope and the intended use. It does not explain return format, but with no output schema the agent can infer it from the described content. The description is complete enough to understand what data will come back and how to frame a query, missing only detailed parameter-combination examples that the schema already covers.

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 86%, so the schema already documents each parameter reasonably well. The description adds a few conceptual hooks (e.g., 'by investment route', 'month-to-date totals', 'daily net equity and debt flows') that map to route, period, category, and granularity, but it does not go beyond what the schema descriptions already convey. Baseline 3 is appropriate because the schema does the heavy lifting.

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 specific verb and resource: 'Foreign portfolio investor (FPI/FII) flows into Indian markets as NSDL publishes them' and lists concrete content (daily net equity and debt flows, by investment route, month-to-date totals). It distinguishes the tool from siblings by giving it a unique, self-contained scope and even names the intended question ('are foreign investors buying'). No ambiguity remains about what the tool does.

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 says 'Use for are foreign investors buying questions', giving a clear trigger condition. It also adds a negative boundary: 'Factual data only — MarketPing publishes no buy/sell recommendations, no price targets and no IPO grey-market premium', which tells the agent when not to use this tool, though it does not name alternative tools. This is clear context with a partial exclusion, but not a full when-not/alternative matrix.

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