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. It explicitly states that it scrapes team/about pages and that email addresses are 'probable' and generated using 'common patterns', honestly conveying the heuristic nature of the output. It does not cover rate limits or blocking, but the core behavioral traits are transparent.

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 two efficient sentences: a front-loaded purpose statement followed by a detail sentence about method and output. Every phrase earns its place, with no redundant filler or background.

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 single-parameter tool with no output schema, the description adequately conveys the essential return content (names, titles, LinkedIn URLs, probable email addresses) and the method. It lacks explicit notes on edge cases like pagination or data freshness, but the high-level behavior is sufficiently specified for an agent to call 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 coverage is 100% because the query parameter description already explains the domain/URL format. The tool description adds no new parameter-specific semantics, so it does not elevate above the baseline of 3.

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 states a specific verb 'Find' and resource 'key people at a company', then elaborates with method ('Scrapes team/about pages') and concrete outputs (names, titles, LinkedIn URLs, probable emails). This clearly distinguishes it from siblings like lookup_tech_stack, which focuses on technology rather than people.

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: use this tool when you need key people or contact details for a company. However, it does not explicitly compare against siblings research_company or lookup_tech_stack, nor state when not to use it, so it falls short of full 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?

With no annotations available, the description carries the burden of behavioral disclosure. It usefully explains the detection sources ('from HTML, headers, and meta tags') and the output categories, but it does not mention limitations such as potential fetch failures, rate limits, or how it behaves for unreachable or JavaScript-heavy sites.

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 sentences, no filler. The core purpose comes first and the second sentence adds valuable detail about categories and detection sources without repetition or 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?

The tool has only one parameter and a clear, well-scoped purpose, so the relative description is strong. A minor gap is that there is no output schema and no explanation of the return format, but for a simple detection tool this is unlikely to prevent correct invocation.

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% for the single required parameter, so the schema already documents that 'query' accepts a domain or URL. The tool description adds no semantic meaning beyond this, but none is really needed given the thorough schema coverage.

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 uses a specific verb ('Detect') and a clear resource ('technologies used on a website'), then expands with the categories it can identify. It does not explicitly differentiate from the siblings, but the listed categories distinguish it from research_company and find_contacts without ambiguity.

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 intended use case is implied: the agent should call this tool when it needs to identify the technology stack of a website. However, there is no explicit guidance about when not to use it or when a sibling like research_company would be a better fit.

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?

With no annotations, the description carries the behavioral burden. It discloses that the tool aggregates data from website scraping, DNS, SSL certificates, GitHub, and DuckDuckGo, which signals network-bound, multi-source research and helps an agent anticipate latency or variability. It does not mention rate limits or failure modes, but the source list is meaningful context beyond a simple 'research company' claim.

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 two sentences with no filler. The first sentence front-loads the action and expected outputs; the second adds the data-source context. Every clause earns its place.

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 single-parameter tool with no output schema and no annotations, the description covers input forms, return categories, and data sources. It stops short of explaining the result structure and the vague 'and more', but an agent has enough to invoke it correctly and interpret the broad result.

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%, and the schema already specifies that the query can be a domain, URL, or company name. The description only echoes 'by domain or name' and adds no new semantic detail, so it meets the baseline for a fully documented parameter.

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 uses a specific verb ('Research') and resource ('a company by domain or name') and lists concrete return categories: company info, tech stack, social links, hosting, and email provider. This clearly distinguishes it from broader or narrower sibling tools like lookup_tech_stack and find_contacts, even without naming them 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 intended use is implied: an agent should call this when it needs a broad company profile rather than just contacts or tech stack. However, the description never references sibling tools or states when to prefer a more focused alternative, leaving routing partially to inference.

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