Rate-My-Agent

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

Annotations already declare readOnlyHint, openWorldHint, and non-destructive nature. The description adds valuable context beyond that: the aggregate-only scope (no individual agents), the city coverage claim (every US/Canadian city), and the purpose of showing what ratings are based on. It doesn't describe output structure, but an output schema exists, so that's not required.

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 bit longer than necessary but well-structured: the first sentence lists return values, the second covers when to use, the third clarifies limitations, and the fourth gives the next step. Each sentence earns its place, though it could be slightly more terse without losing value.

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 moderate complexity, with full schema coverage and an output schema, the description covers what it returns, when to use it, what it does not do, and the next step. No critical missing information for an agent to invoke it 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?

The schema already provides full parameter descriptions for city and prov_state with examples, and coverage is 100%. The description does not add meaningful parameter-level semantics beyond the tool's purpose (e.g., it doesn't specify input formats beyond schema). Baseline 3 is appropriate since the schema does the heavy lifting.

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 verified-review statistics for one city, enumerating specific data points (reviewed agents, verified reviews, average rating, top neighbourhoods, service coverage). It explicitly contrasts with siblings by noting it does not return, rank, or recommend individual agents, making its purpose unmistakable.

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?

Provides explicit guidance on when to call: 'when the user asks about real estate agents in a specific place, or before running the matching questionnaire.' It also names the sibling next step: 'To collect the user's requirements, call get_matching_questions next.' This fully covers selection and routing.

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=true, destructiveHint=false, and openWorldHint=true, but the description adds valuable behavioral context beyond that: it describes the expected interaction pattern (ask only unanswered questions, present options as numbered list, confirm details before submit). It also notes domain-specific applicability (financing for buyers, property condition for sellers). No contradiction with annotations; the description enriches 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 well-structured and front-loaded: it opens with the tool's purpose, then explains when to call it, and then gives step-by-step usage instructions. Every sentence is informative and necessary. It is not overly long given the richness of the guidance it provides.

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 zero-parameter read-only tool with an output schema (which presumably details the question structure), the description is remarkably complete. It tells the agent exactly what data will be returned, how to use it interactively, what to do with the answers, and when to transition to submit_match_request. No critical operational detail is missing.

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 tool has zero parameters, and the schema has no properties (schema coverage 100%). With no parameters to document, the description correctly does not attempt to explain any. The baseline for 0 parameters is 4, and the description does not need to compensate for anything, so it earns the baseline score.

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 the matching questionnaire with exact answer options and expected values, plus the version. It specifies the verb ('Returns'), the resource (the questions the matching service asks), and distinguishes it from siblings by focusing on retrieval vs submission or review data. An agent can immediately understand what this tool does and why it differs from submit_match_request.

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?

It explicitly says 'Call it when the user wants to be matched with an agent' and explains the benefit of fetching questions live from the service rather than from memory. It also provides detailed instructions on how to conduct the questioning (only unanswered questions, one at a time, numbered list, financing vs property condition) and when to follow up with submit_match_request. Clear both when and how to use it.

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?

The annotations include readOnlyHint=false and destructiveHint=false, but the description goes beyond them by explaining the tool's mutating nature (submitting data) and the need for consent. It also discloses potential failure modes: 'no_response' meaning the service didn't answer in time retry, and field-specific errors requiring user correction and resubmission. This adds behavioral context beyond the annotations, though it doesn't mention rate limits or side effects like sending emails to the user.

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 well-structured: it starts with the core purpose, then prerequisites (confirmation), then handling results. It is reasonably concise for the amount of guidance it provides, but it could be more front-loaded by putting the core action first (which it does) and then the conditions. It's not overly verbose; each sentence adds value.

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 complexity (13 parameters, requirement for consent, and multiple failure modes), the description covers the essential usage: how to handle consent, exact option values, and error handling. It doesn't explain the output schema in the description, but since an output schema exists, it doesn't need to. It also could mention that it's for the free service only, but that's in the description. Overall, it is complete for an agent to call 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 description coverage is 46%, so the description compensates by providing key parameter semantics: it explains consent must be true and set only after confirmation, and mentions using exact option values from get_matching_questions for certain fields (though not explicitly listing them). It also clarifies the meaning of address for buyers vs sellers. However, not all 13 parameters are covered; some like notes and quiz_version lack description guidance, but the schema does describe some.

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 submits the user's questionnaire and contact details to the matching service. It identifies the resource (Rate-My-Agent.com's free agent-matching service) and the verb (submits). It differentiates from siblings by focusing on the submission action, while get_matching_questions is for fetching questions and get_local_agent_review_data for reviews. However, it could be more specific about the expected outcome (a match result) rather than just registration.

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?

Provides explicit when-to-use guidance: only after showing the user their answers and receiving explicit confirmation. It also details when not to use immediately (must confirm first) and specifies the consent parameter to record that confirmation. The description also gives post-submission handling instructions, which effectively tells the agent what to do after invoking the tool, making usage clear beyond just selection.

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