AI Product Index

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

No annotations are provided, so the description must carry behavioral information. It states that the tool returns 'every published field', which discloses the completeness of the response. However, it does not mention error behavior (e.g., what happens if the slug is not found), authentication requirements, or any potential side effects. For a read-only fetch tool, this is a minimal but not exhaustive disclosure.

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 is concise, front-loaded with the action, and includes a meaningful qualifier ('with every published field'). It contains no fluff or redundant wording, making it highly 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 one-parameter fetch operation, the description is nearly complete. It covers what is fetched and the method, and the schema covers the parameter source. The absence of an output schema means the 'every published field' phrase serves as a return-value expectation. However, it could be slightly more complete by explicitly stating that the slug comes from search_products within the description itself, rather than relying on the schema.

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 has 100% coverage: the required 'slug' parameter is described as 'The listing slug, as returned by search_products.' The main description does not add any further semantic detail about the parameter, but the schema description is already informative and sufficient given the simplicity of the 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 'Fetch one listing from the AI Product Index by slug, with every published field' uses a specific verb ('fetch'), names the resource ('listing', 'AI Product Index'), and clarifies the selection method ('by slug'). It clearly distinguishes from sibling tools like search_products by focusing on retrieving a single item rather than searching.

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 the use case: obtain a single listing when you already have its slug. While it doesn't explicitly state 'use this instead of search_products when you have a slug' or list exclusions, the context is clear enough. The schema description for 'slug' explicitly says 'as returned by search_products', which reinforces the relationship to the sibling search tool.

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 the full burden. It discloses that the operation is autonomous, free, requires no human approval, and returns informational content (steps and schema). This effectively communicates a safe, read-only, self-service behavior without leaving major hidden behaviors uncovered.

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, well-structured sentence that front-loads the core purpose and adds key behavioral details (autonomous, free, no human approval) without any wasted 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?

For a zero-parameter, informational tool with no output schema, the description is complete: it tells the agent exactly what will be returned and the conditions of use. Nothing essential 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, so the baseline is 4. The description appropriately focuses on the return value rather than parameter details, and no parameter clarification is needed.

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 uses a specific verb ('Returns') and identifies the exact resource: the steps and schema for registering a product in the index. This clearly distinguishes it from sibling tools like get_product, search_products, and score_url, which serve different purposes.

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 makes it clear this is the tool to use when you need to know how to register a product, and it adds helpful context: the process is autonomous, free, and requires no human approval. It does not explicitly name alternatives or exclusions, but among the siblings none are direct alternatives for registration instructions.

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 the full burden of disclosure. It clearly states the tool performs 13 specific checks, is free, and returns which checks failed. It doesn't disclose potential network behavior (e.g., fetching the page, rate limits) but the visible scope is adequately transparent for an agent to understand what the tool does.

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 tight sentences, front-loaded with the core function and followed by cost/output details. The list of checks is informative without fluff; every sentence earns its place.

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 single-parameter, no-output-schema tool, the description tells the agent what it does, what it returns (which checks failed), and cost implications. It covers the main information needed to invoke it correctly. Minor gaps like the exact return format structure are not critical given the simple output.

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 fully describes the 'url' parameter as 'Absolute http(s) URL to grade' (100% coverage). The description adds the notion of 'public' and the grading output, but doesn't add significant meaning beyond the schema. The baseline of 3 applies due to high schema coverage.

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 uses a specific verb 'grade' and resource 'public URL', clearly defining the A–F outcome and enumerating the 13 checks. It is easily distinguished from sibling tools like search_products or get_product, which focus on product lookups, not site readability.

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 establishes when to use the tool: for grading any public URL for agent-readability. It doesn't explicitly discuss exclusions or alternatives, but the context of siblings implies this is the only site-grading tool. The free vs. paid endpoint mention gives useful context on what the tool returns, though it doesn't explicitly state 'use this when you want a quick grade'.

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 responsibility for behavioral disclosure. It goes beyond a simple statement by explaining that every result has a URL, that servers are remotely-callable without installation, and that installable-package-only servers are deliberately excluded. This provides meaningful context about the tool's filtering behavior and scope. It does not mention rate limits, pagination, or return format, but given the read-only nature of a search tool, the transparency is above average.

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 the core purpose and scale ('Search ~10,000 remotely-callable MCP servers'). The second sentence adds critical scope exclusions and framing without redundancy. Every phrase earns its place, and the structure is immediately informative.

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?

Despite lacking annotations and an output schema, the description covers the tool's domain, inclusion/exclusion criteria, and filter capabilities. It explains what makes these servers unique (URLs, no installation). It does not describe the return structure, but since no output schema exists, the description provides sufficient context for an agent to decide whether to invoke this tool. Slight gap: no mention of result quantity or pagination, but the limit parameter is in the schema.

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 80%, so baseline is 3. The description mentions filtering by transport, host, or credentials, which aligns with the schema's transport, host, and auth parameters, but adds little beyond what the schema already describes. The query parameter's purpose is implied by 'Search' but not elaborated, and the limit parameter is not mentioned at all. The description provides marginal added value over the 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?

The description clearly states the tool searches ~10,000 remotely-callable MCP servers from the official registry, using a specific verb ('Search') and resource ('MCP servers'). It distinguishes itself from siblings like search_products and search_x402_endpoints by focusing exclusively on MCP servers and emphasizing their immediate connectability.

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 usage context: use this to find MCP servers that can be called immediately via URL, not those requiring installation. It explicitly states what is excluded ('Servers distributed only as installable packages are deliberately excluded') and frames the tool as answering 'what can I call now' vs 'what exists'. It does not name alternative tools directly, but the exclusion guidance effectively tells the agent 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.

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds context about the self-registered index and the return content (URLs and endpoints), which is useful. However, it does not state that the operation is read-only, any rate limits, or the default limit and query-omission behavior that are only present in the schema.

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 the action, and each sentence provides meaningful information without redundancy. It is appropriately sized 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?

For a search tool with no required parameters and no output schema, the description gives a reasonable overview of the source, scope, and return type. However, it fails to mention how it relates to sibling search tools or edge cases like omitting the query, leaving some gaps in complete guidance.

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 100% coverage with clear descriptions for all four parameters. The description adds no additional parameter-level meaning, so the baseline 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 tool searches a directory of AI products, APIs, agents, and MCP servers, with a specific verb and resource. It lacks explicit differentiation from sibling search tools like search_mcp_servers, which also search the same index, so it is a clear but not fully distinguishing purpose.

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 context for using the tool as a general directory search, but it does not mention when to choose it over sibling tools such as search_mcp_servers or search_x402_endpoints, and it offers no exclusions. Usage guidance is implied rather than explicit.

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 the full burden of behavioral disclosure. It reveals the data source (Coinbase CDP Bazaar), scale (~14,700), and ranking behavior (relevance then cheapest first). The max_price parameter description also discloses how unrecognized-asset prices are excluded. While it doesn't detail response format or pagination, it provides substantial behavioral context beyond a basic 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 two sentences, front-loaded with the action and then the use case. Every sentence earns its place: the first states the resource and scope, the second explains when to use it and what to expect. No redundancy or fluff.

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 has 6 parameters, no output schema, and no annotations, the description provides essential context: source, scale, use case, and ranking. It mentions seeing costs before calling, implying return values include pricing. The main gap is not describing the exact return structure, but for a search tool this is relatively minor and the description is quite complete for an agent deciding to invoke it.

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 83% (5 of 6 parameters have descriptions), so the baseline is 3. The description adds a brief overview of filterable parameters but relies mostly on the schema. The max_price parameter description does add nuance about unrecognized assets, which is a small increment beyond schema, but overall the description doesn't significantly enhance parameter understanding.

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 searches ~14,700 x402-payable HTTP endpoints, specifying the exact verb and resource. It distinguishes from sibling tools like search_mcp_servers and search_products by focusing specifically on machine-payable web endpoints, making its unique 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 on when to use the tool: to find an API an agent can pay for per call and to check costs before calling. It doesn't explicitly mention alternatives or exclusions, but the specificity of x402 endpoints and the listed filter options imply appropriate usage scenarios. Sibling tools are not directly referenced, so a minor gap exists.

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