get_startup_scaleup_counts

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

With no annotations, the description carries the full burden and does well: it discloses the HARMONIC_API_KEY requirement, states that 'Returns exact counts', explains the currency caveat ('Harmonic funding is ~USD; EUR 30M ~= USD 32-33M'), and defines the startup/scaleup classification logic. This goes beyond typical transparency, though it omits error/limit behaviors.

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 front-loaded with a one-sentence purpose, followed by a compact Args list and essential notes. Every sentence adds value—no fluff or repetition of schema defaults. The structure makes it easy to scan and extract key points.

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?

Given the tool has an output schema (so return format need not be described) and no annotations, the description is complete: it covers authentication, definitions, parameter semantics, a data-quality caveat ('tags are coarser... use clean-tag segments precisely'), and the connection to the TAM sheet. It leaves no major gap for an agent to invoke the tool correctly.

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 0%, so the description must fully explain parameters. It does: each of the five parameters gets a clear description, examples (industry_tags, technology_tags, exclude_business_tags), defaults (region, split_at), and the crucial EUR/USD note for split_at. This exceeds the schema's bare types.

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: 'Count Startups and Scaleups for one value-chain segment via Harmonic, split on funding raised.' This clearly distinguishes it from siblings like get_enterprise_counts, get_segment_counts, and get_company_count, which target different count types.

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 clear context for when the tool is appropriate ('for one value-chain segment', 'TAM sheet columns D and E') and offers a tip on handling Harmonic's coarse tags. However, it does not explicitly compare against sibling tools or state when to use this instead of another, so the guidance is implied rather than explicit.

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