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lan-club-live

startup-gtm-skill

search_evidence

Query narrative evidence across 350 Indian startups to find what worked or failed. Ideal for 'when did X fail' and 'who used Y for Z' queries, returning brief snippets with company names.

Instructions

Keyword search across the narrative evidence (what worked / what failed / signature moves / ignition). Use for 'when did X fail', 'who used Y for Z'. Returns short snippets with company names — not full rows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
query_textYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the output behavior (returns short snippets with company names) and the scope of evidence searched. However, it does not explicitly state that the operation is read-only, nor does it mention rate limits, pagination, or side effects. For a search tool, the transparency is adequate but incomplete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is exactly two sentences, front-loaded with the primary action, and contains no extraneous information. It includes usage examples and output format without editorializing, making it concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains the return value (short snippets with company names) despite lacking an output schema. It also gives a sense of the evidence scope. However, missing parameter details and lack of annotations leave gaps for a tool with no output schema and sparse input schema, so it is only partially complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description does not compensate. It implies query_text is the search term but does not explain the limit parameter or its default behavior. There is no parameter-specific guidance beyond the broad notion of 'keyword search'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs a keyword search across narrative evidence, listing specific content areas (what worked/failed, signature moves, ignition). It also clarifies the return format (short snippets with company names, not full rows), which distinguishes it from tools like get_company that return full rows.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides specific example use cases ('when did X fail', 'who used Y for Z'), giving clear context for when to use the tool. However, it does not mention when not to use it or explicitly compare to sibling tools, so it lacks exclusions or alternatives.

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

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