gyibb-mcp

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 mentions the limit of 5 products and the content of each row, but does not cover side effects, authentication needs, rate limits, or error handling. It is adequate but not comprehensive.

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 short, three sentences, with the main action front-loaded. Every sentence adds value, no 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 low complexity (one parameter, no output schema) and sibling context, the description adequately covers the purpose and return structure. It mentions each row's fields, but could be more explicit about error scenarios or handling of invalid slugs.

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?

With 0% schema description coverage, the description adds meaning by specifying that 'slugs' are product slugs and that the comparison is limited to up to 5 products. It partially compensates for the missing schema descriptions, though format or constraints are not detailed.

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 performs side-by-side comparison of GYIBB products by slug, with output details (rating, confidence, pros/cons, source platforms) and a limit of 5 products. This distinguishes it from siblings like get_product_review, search_products, and verify_claim.

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 for comparing multiple products but does not explicitly state when to use it versus alternatives, nor provide when-not-to-use guidance. No exclusions or alternative tools are mentioned.

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?

Discloses that reviews under editorial floor return 'not found', and that recommended products include buy_url and affiliate_disclosure, with instructions not to invent links. No annotations present, so description carries burden.

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 concise, front-loaded with purpose, then edge cases and handling instructions. Every sentence adds value without 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?

Comprehensive for a single-parameter tool with no output schema and no annotations. Covers input formats, expected output, edge cases, and affiliate disclosure. Leaves little ambiguity.

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 single parameter 'query' has schema type string with no description. The description enhances meaning by specifying accepted formats: product name, slug, or 'category/slug'. Could be more precise about syntax.

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?

Clearly states it fetches GYIBB's synthesized review for a product. Specifies input formats (name, slug, category/slug) and output (rating, pros, cons, cited sources, confidence tier). Distinct from siblings compare_products, search_products, verify_claim.

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 guidance on when the tool returns 'not found' (reviews below editorial floor) and how to handle buy_url and affiliate_disclosure. Does not explicitly compare to siblings but contextually clear.

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 full burden. It discloses sorting order and limit, but does not state that it is read-only, mention authentication needs, or describe any side effects. Adequate but could be more 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?

Two concise sentences with no wasted words. The first sentence front-loads the core purpose, and the second sentence adds parameter guidance efficiently.

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 search tool with no output schema, the description covers essential behavior (search, sorting, filtering). It could mention that results include product details, but overall 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 description coverage is 0%, so the description must compensate. It explains 'limit' as max matches, 'category' with examples, and implies 'query' as keyword. This adds meaning beyond the schema, earning above baseline.

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 'Keyword search over the GYIBB catalog', specifying the verb (keyword search) and resource (catalog), and distinguishes from sibling tools (compare, review, verify) by focusing on 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 gives clear context on using limit and category parameters, including examples for category. However, it lacks explicit guidance on when not to use this tool versus alternatives like compare or verify.

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?

Without annotations, the description carries the full burden. It discloses the return type (supporting and contradicting evidence quoted from reviews) and the possible verdicts. However, it does not explicitly state that the tool is read-only or mention any required permissions.

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 paragraph of two sentences, front-loading the core purpose and outcomes. Every sentence adds value, with no redundant or vague language.

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 two plain string parameters, no output schema, and no annotations, the description covers the tool's purpose, outcome categories, and return content. It is slightly incomplete because it lacks parameter format guidance, but overall it provides a solid understanding of the tool's behavior.

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, so the description must add meaning. It mentions 'product_slug' and 'claim' but does not explain what a product_slug is, give format examples, or clarify the claim's expected structure. The parameter names alone are insufficient for an agent to know how to populate them correctly.

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 (check) and the resource (a specific claim about a product), listing possible outcomes (supported, contradicted, mixed, insufficient-data) and the data source (GYIBB's synthesized sources). It distinguishes itself from siblings like compare_products, get_product_review, and search_products by focusing on claim verification.

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 you have a specific claim and product slug, but it does not explicitly state when to use this tool versus alternatives. No instructions on prerequisites or when not to use it are provided.

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