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

With no annotations provided, the description carries the full burden and does well: it explains the computation source (live MCP search telemetry), the output fields, the meaning of cosine similarity, and honestly cautions that it returns few gaps while query volume is low. It stops short of noting side effects or read-only guarantees, but the behavior is clearly observational.

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 information-dense and well-structured: behavior first, then outputs, then honest caveat, then parameter note. Every sentence carries substantive detail, though the phrase 'No arguments needed ({}); `limit` caps the list' creates a slight tension with the three optional schema parameters.

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?

With no output schema and no annotations, the description compensates by listing the key returned fields and their interpretations. It also gives the low-volume behavior caveat and mentions live telemetry, giving the agent enough context to use the tool. It could more explicitly differentiate from the sibling demand_signals and market_report tools, but the core completeness is strong.

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?

All three parameters are fully described in the input schema, so the description adds only marginal value. It mentions that no arguments are required and that 'limit' caps the list, but rank and sector semantics are already covered by the schema. This meets the baseline but does not go beyond it.

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 identifies the tool's purpose: finding unmet demand by computing gaps between user-request clusters and paid providers. It is specific about the method (raw text-embedding space, no fixed categories) and distinguishes itself from market-report or provider-ranking siblings by defining gaps explicitly.

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 provides a clear usage context: use when you need to discover demand clusters that are underserved by paid providers. It does not explicitly name alternative tools or say when not to use it, but the distinctive gap/under-served framing makes the intended scenario unambiguous.

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