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

With no annotations provided, the description carries the full burden of disclosure. It conveys a read-only polling action but does not describe expected behavior such as whether it returns the final result or a job status, how it handles in-progress or failed jobs, or any rate limits. This lack of detail is a notable gap for a tool that will be invoked iteratively.

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, front-loaded sentence that efficiently conveys the tool's purpose. Every word earns its place, with no redundant or extraneous content.

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 (one parameter, no output schema, no annotations), the description is too sparse. It fails to explain what the response will contain (e.g., whether it returns the actual enrichment result or just a status), or how to interpret the poll outcome. This missing information is critical for an agent to know how to handle the result and decide next steps.

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 provides complete coverage of the single parameter 'jobId' with a clear description referencing submit_lookup. The tool description adds minimal extra meaning beyond what the schema already states, merely echoing that the job is created with submit_lookup. Therefore, it meets the baseline for high schema coverage without adding significant new information.

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 action ('Poll') and resource ('the result of an enrichment job'), and it explicitly references the sibling tool 'submit_lookup' for job creation. This distinguishes it from the sibling tools and leaves no ambiguity about its function.

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 the proper usage: after creating a job with submit_lookup, use check_lookup to poll its result. It provides clear context for when to use this tool, though it does not explicitly state when not to use it or mention any alternatives beyond the implied sequence.

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 must carry the burden of disclosing behavior. It does disclose that the token is session-scoped, which is valuable. However, it doesn't mention side effects like overwriting an existing token, validation behavior, or whether the token persists beyond the session. The word 'set' implies a write operation, but deeper behavioral details are missing.

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, front-loaded sentence with no wasted words. It states the action, scope, and where to get the token in one breath. Every part contributes value, making it highly concise and well-structured.

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 is simple with one parameter and no output schema, so the description doesn't need to explain return values. It covers what the tool does and where to get the token. However, it doesn't explicitly state that this token is required for the sibling tools (submit_lookup, check_lookup), which would enhance completeness for an agent navigating the toolset.

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 covers 100% of parameters, with the token parameter described as 'Your YourICP API token'. The description adds useful context by providing the URL to obtain the token, which clarifies the token's origin and purpose beyond the schema. This enrichment justifies a score 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 clearly states the action: 'Set the YourICP API token for this session.' The verb 'set' is specific, and the resource 'API token' is unambiguous. It clearly distinguishes from sibling tools (submit_lookup, check_lookup) which perform lookups rather than setup.

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 provides clear context: the token is set for the current session, implying it should be used before making authenticated calls. It also instructs where to obtain the token ('Get one at https://app.youricp.com'). While it doesn't explicitly mention alternatives or when not to use, the session scoping makes the usage context obvious.

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 must carry the behavioral disclosure burden. It reveals the async nature (returns a jobId) and the polling requirement, but does not disclose auth needs, error handling, idempotency, or side effects of enrichment, which are material for a tool with potential write behavior.

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, front-loaded with action and inputs, then the return mechanism. It is concise and every word contributes useful information with no filler.

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 async submission tool with only two parameters and no output schema, the description adequately covers the purpose, input types, and the expected return (jobId) in relation to check_lookup. It doesn't mention prerequisites like token setup, but the sibling set_token hints at that. Overall, it's sufficient for an agent to invoke 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?

Both parameters are fully documented in the schema with descriptions ('Contact email addresses to enrich' and 'LinkedIn profile URLs to enrich') and formats, giving 100% schema coverage. The tool description repeats these inputs without adding extra meaning, so the baseline score of 3 applies.

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 function: 'Enrich one or more contacts by email address or LinkedIn URL.' It also distinguishes itself from check_lookup by explaining that it returns a jobId to poll, making the purpose unambiguous.

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 provides clear context for when to use this tool: it's the submission step that yields a jobId for polling with check_lookup. However, it doesn't explicitly mention any alternatives or when not to use it, leaving a small gap.

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