RoboHub Robot Catalog

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

No annotations are provided, so the description must carry the full burden. It discloses the comparison and possible verdict but does not mention read-only behavior, error handling (e.g., missing slugs), or data freshness. Adequate but not detailed.

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 sentence that immediately conveys the core function and additional feature (verdict). No redundant words; front-loaded and 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?

For a simple comparison tool with no output schema and no annotations, the description covers the main inputs and output. It could be slightly more complete by mentioning output format or handling of unavailable robots, but it is sufficient.

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% with both parameters described concisely. The description adds context 'by slug' and 'editorial verdict' but does not enhance parameter meaning beyond the schema descriptions. Baseline 3 is appropriate.

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 a side-by-side spec comparison of two robots by slug, with an optional editorial verdict. This specific verb+resource distinguishes it from siblings like get_robot (single robot) and search_robots (search).

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 implicitly conveys when to use this tool (comparing two robots) vs siblings (e.g., get_robot for single robot info). However, it lacks explicit when-not or alternative guidance, such as noting that this tool is not for filtering or listing.

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 accurately describes the output but does not explicitly state that the operation is read-only or idempotent, nor disclose any potential side effects, rate limits, or authentication requirements. The name implies a safe read, but explicit disclosure would improve transparency.

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 sentence of 12 words, front-loading the purpose and listing included fields concisely. No unnecessary words or repetition.

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 nested objects), the description is complete. It covers what the tool returns, how to specify the robot, and the source of the slug, leaving no critical gaps.

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% for the single parameter slug, and the description adds value by specifying that the slug comes from search_robots and providing an example ('unitree-g1'). This helps the agent understand how to obtain valid input.

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 retrieves a full catalog record for one robot by slug, listing specific fields (description, specs, SDKs, use cases, pricing, links). This distinguishes it from sibling tools like search_robots (list) and compare_robots (comparison).

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 needing full details for a specific robot and references the slug from search_robots, providing clear context. However, it does not explicitly state when not to use it or name alternatives, though the sibling tool names make the distinction 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?

No annotations are provided, so the description must disclose behavioral traits. It only states the tool returns data without indicating whether it is read-only, has rate limits, or other side effects. For a simple listing, this is a gap.

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, both front-loaded with essential information. No redundant words. Every sentence serves a purpose: what the tool does and when to use it.

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 tool with no output schema, the description adequately covers what is returned (categories with counts and examples) and its role. Adding a note about read-only nature or response format would improve completeness.

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 zero parameters and schema coverage is 100%, so the description does not need to add parameter information. Baseline for 0 parameters is 4, and the description meets this expectation.

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 robot categories with counts and example robots, and identifies its use case as a first call for catalog coverage. This distinguishes it from siblings that focus on individual robots or comparisons.

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 explicitly says 'Good first call to understand catalog coverage,' providing clear context for when to use it. However, it does not mention when not to use it or alternatives, missing the opportunity for 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, the description fully shoulders transparency. It reveals the outcome (email reply with shortlist, pricing, intros), timeline (48 hours), and constraints (free, vendor-neutral). It doesn't describe error handling or data persistence, but covers key behavioral traits.

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 three concise, front-loaded sentences. Every sentence adds essential information: the action, the outcome/timeline, and the usage condition. No unnecessary words or redundancy.

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 (10 parameters, no output schema), the description is reasonably complete. It explains the submission process, expected response, and key constraints. It lacks error or edge-case details but aligns with typical B2B request tools.

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 input schema already provides 100% coverage with detailed descriptions for each parameter. The description adds little beyond emphasizing 'real contact details' for necessary parameters, but the schema is sufficient. A baseline score of 3 is appropriate.

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 ('Submit a B2B sourcing request') and the target ('RoboHub'), with a specific verb and resource. It contrasts effectively with sibling tools like 'compare_robots' and 'search_robots', which serve different functions.

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 explicit usage context: 'Only call this with the user's explicit consent and their real contact details.' It also sets expectations ('Within 48 hours...') and clarifies vendor neutrality. However, it does not explicitly list when not to use or offer direct alternatives.

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 provided, so description must describe behavior. It adds a note that robots with unknown pricing are excluded when max_price_usd is set, which is helpful. However, it does not mention other behavioral traits like sorting, pagination, or any limitations such as rate limits.

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?

Description is two sentences plus a concise note. It is front-loaded with the main action and efficient in wording.

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 search tool with 4 parameters and no output schema, description covers main functionality and one important behavioral note. Could mention default limit and output format details, but overall adequate.

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. Description adds value only for max_price_usd by explaining exclusion of unknown-priced robots. Other parameters are already well-described in schema.

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 verb 'Search', resource 'RoboHub's catalog', and method 'by free-text query and optional filters'. It distinguishes from siblings like get_robot (single) and compare_robots (comparison).

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?

Description does not explicitly specify when to use this tool versus alternatives. It implies usage for searching but lacks guidance on when to use free-text vs category vs price filter, or when not to use it.

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