company-technographics

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

Annotations already declare readOnlyHint and idempotentHint, so the behavior is safe. The description adds valuable context about the response content (firmographics, hiring stats, tech stack with adoption context), going beyond annotations without contradicting them.

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 a single, well-structured sentence that front-loads the tool's purpose and streams the output details without unnecessary words.

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 single required parameter, no output schema, and annotations covering safety, the description is complete. It specifies what the tool returns and includes an example. Sibling tools are listed but not contrasted, which is acceptable.

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 coverage is 100%, so baseline is 3. The description adds an example ('walgreens.com') and explains what the domain resolves to, but does not significantly enhance the schema's own parameter description ('Company primary domain').

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 states the verb 'resolve' and the resource 'company domain to its full profile', including specific data categories (firmographics, hiring stats, tech stack with adoption context). It effectively distinguishes from siblings like search_companies_by_technologies and list_technologies.

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 implies usage when a company domain is known and a full profile is needed. It provides an example domain. However, it does not explicitly state when not to use or contrast with sibling tools beyond their names.

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

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

Annotations already declare readOnlyHint and idempotentHint true, so the description adds no extra behavioral context beyond the sort behavior. No mention of data freshness, rate limits, or other traits. The description does not contradict 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?

The description is exceptionally concise: two sentences with no filler. Every word adds value. It front-loads the purpose and immediately specifies the key parameter behavior.

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?

For a simple read-only list tool with 3 parameters and no output schema, the description covers the core behavior and sort options. However, it does not mention pagination limits or the returned data structure, which is a minor gap. Overall adequate but not thorough.

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 compensate. It explains the sort parameter (enum values) but omits page and page_size entirely. However, the schema has defaults and constraints for those, so an agent could infer. This is average coverage given the tool's simplicity.

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 states it lists all technologies with company counts, using the verb 'Catalog' and specifying the resource 'technologies'. It distinguishes from siblings like search_companies_by_technologies (which filters by tech) and get_company_by_domain (retrieves a single company).

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 sort options (count, name) but does not explicitly state when to use this tool versus the siblings. The context is implied as an overview of all technologies, but lacks explicit when-to-use or when-not-to-use guidance.

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

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

Annotations provide readOnlyHint and idempotentHint. Description adds valuable behavioral context: case-insensitive search, 10,000+ technologies tracked, ranking by usage, and match logic. No contradictions.

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 concise sentences front-load the main purpose. Every sentence adds value with no redundancy. Highly efficient.

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 4 parameters and no output schema, description covers main behavior, match modes, case-insensitivity, and references sibling tool. Lacks explanation of ranking method and pagination, but sufficient for most use cases.

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 low (25%), but description compensates by explaining case-insensitivity for 'technologies' array and behavior of 'match' parameter. Does not cover 'page' or 'page_size' beyond schema defaults.

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?

Description clearly states it finds companies using given technologies, ranked by usage, and specifies case-insensitivity. It explicitly references sibling tool list_technologies for exploration, distinguishing purpose.

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?

Explains when to use the tool (find companies by technologies) and details match parameter behavior ('any' needs at least one, 'all' needs every one). Mentions case-insensitivity and suggests list_technologies for exploration. Lacks explicit when-not-to-use guidance but is clear overall.

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