Lead Enrichment API

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

With no annotations, the description carries the full burden. It discloses key behavioral traits: it's a read-only query, scoped to the calling API key, and limited to the current month. It doesn't mention potential caveats like caching or rate limits, but the core behavior is transparent for a simple query.

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?

A single clean sentence that is front-loaded and provides all necessary information without extraneous 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 tool's simplicity (no params, no output schema), the description adequately specifies the return content (quota status, recent usage) and scope. It falls short of explaining the exact response structure, but that's acceptable for a basic usage query.

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?

There are no parameters, so baseline is 4. The description doesn't need to explain parameters since none exist, and the schema trivially covers 100%.

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 tool returns current-month quota status and recent usage for the calling API key, using a specific verb and resource. It distinguishes from siblings (search_leads, validate_lead) which are lead-related, leaving no 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 description implies usage context (checking quota/usage for the API key) but does not explicitly state when to choose this tool over alternatives or provide exclusions. Sibling tools are clearly unrelated, so intent is clear, but there's no explicit 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?

No annotations are provided, so the description carries the full burden. It does disclose a meaningful behavioral trait ('Each lead returned counts against the monthly quota') and indicates the return type (scored companies with firmographics, tech stack signals, buying signals). However, it does not mention permissions, rate limits, error cases, or whether the operation is read-only beyond the verb 'Search'.

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 efficient and well-structured: a one-line purpose, two sentences on returns and quota, then a compact Args list. Every sentence adds value, and the parameter documentation is organized and scannable.

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 tool with no output schema and 9 flexible filter parameters, the description covers the high-level return content and quota impact, plus detailed parameter semantics. However, it lacks specifics about the response shape, pagination, or any error/failure modes, which would make it fully complete.

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?

The schema has 0% description coverage, but the tool description compensates thoroughly by explaining each parameter with allowed values and semantics. For example, 'min_icp_score: 0-100, minimum ICP fit score' and 'max_age_days: only leads enriched within N days (1-365)' provide meaning far beyond the bare schema definitions.

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 tool 'Search enriched B2B leads by ICP criteria', identifying both the action (search) and the resource (enriched B2B leads). It is specific enough to distinguish from the sibling tools (get_usage, validate_lead), though it does not explicitly call out those alternatives.

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 when to use the tool (when searching for B2B leads based on ICP criteria) but provides no explicit guidance about when not to use it or which sibling tool to choose instead. There is no exclusion or alternative recommendation, so it meets an 'implied usage' standard.

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 provided, the description carries the full burden of behavioral disclosure. It indicates a read-only 'check' but does not mention potential side effects (e.g., network calls for website reachability), authentication requirements, or error behavior if the lead does not exist. This lack of transparency could surprise the agent.

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 extremely concise with a single purpose sentence and a well-formatted Args block. Every word earns its place, and no fluff is present. The structure is clean and easy to parse.

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?

Although the tool is simple with one parameter and no output schema, the description does not explain what the check returns, how results are formatted, or what happens if the lead is invalid. An agent invoking this tool would lack expectations about the response, making the description incomplete for real-world use.

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?

The schema only defines lead_id as a string with no description, so the description adds crucial meaning by specifying it is a 'UUID of the lead to validate.' This gives the agent format and context beyond the bare schema, fully covering the single 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 clearly states the tool's purpose: 'Check the freshness and website reachability of a specific lead.' The verb 'check' and the resource 'specific lead' make the action explicit, and the two aspects (freshness, reachability) define its scope. This distinguishes it from siblings like search_leads, which is for finding leads, and get_usage, which likely returns usage data.

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?

There is no guidance on when to use this tool versus alternatives. It does not specify scenarios, prerequisites, or exclusions. The description only states what the tool does, leaving the agent to infer when it should be chosen over search_leads or get_usage.

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