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

With no annotations provided, the description carries the full burden of behavioral disclosure — and it excels. It reveals the ~20 s runtime, that data is LIVE from specific registries, that newcomers (<=24 months) calibrate the reach, and exactly what the response contains including the deciding number and links. This is rich, honest disclosure about process, timing, and output with no annotation to contradict.

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 core purpose is front-loaded in the first clause, and every subsequent sentence earns its place: the processing pipeline, the output contract, and the runtime estimate are all decision-relevant for an agent. It is on the longer side, but there is no filler — each element (sources, newcomer logic, return fields, timing) carries distinct information an agent needs to invoke and interpret the result.

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?

This is a complex tool (multi-source lookup, classification output) with no annotations and no output schema, so the description must carry the entire burden — and it does. It covers process, all seven data sources, the reach logic, the full return contract (KILL/PASS/UNKNOWN, deciding number, month-12 revenue ceiling, months to first dollar, comparables with links), and runtime. Nothing an agent needs to call it or interpret its result is missing.

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% — the 'idea' parameter is already fully documented (plain words, price, free tier, 8-300 chars). Per the baseline, the description need not add parameter details. The description does hint at how the input flows through the pipeline (turned into a market lookup), which is marginal value, but it adds no substantive parameter-level meaning beyond the schema. 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 opens with a specific verb and resource ('Score a product idea against LIVE market data') and then details the exact pipeline: market lookup, comparables across seven named directories, newcomer range, and sourced priors. It clearly distinguishes itself from the sibling data-fetch tools (get_dataset, github_trending, mcp_registry_census) — none of which evaluate a product idea. No tautology; full specificity.

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?

While it never explicitly names an alternative or a 'do not use when' condition, the description makes the tool's role unmistakable by spelling out the full scoring workflow and output (KILL/PASS/UNKNOWN, revenue ceiling, timeline). Given that all sibling tools serve clearly different functions (dataset retrieval, trend tracking), the context is enough for an agent to route correctly, though an explicit exclusion would have pushed this to a 5.

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