Silicon Friendly

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

With no annotations, the description carries the full burden. It states the operation is a 'quick check' and that it returns a simple response, clearly implying a read-only query. It describes the return fields (domain, in_directory, level, verified) without contradicting annotations. Minor omissions like auth requirements or edge cases, but for a simple check, the behavior is sufficiently disclosed.

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 concise and well-structured: a one-sentence summary followed by Args and Returns sections. Every sentence provides useful information without unnecessary elaboration. It is front-loaded with the main purpose and quickly covers inputs and outputs.

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 required parameter, no output schema), the description is complete. It explains the tool's purpose, the parameter, and the return fields (including the L0-L5 level range). This provides enough context for an agent to decide when and how to use it, even without an output 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 coverage is 0%, so the description must compensate. It explains the 'domain' parameter with examples ('stripe.com', 'github.com'), clarifying that it is the website domain. This adds meaning beyond the schema's bare 'Domain' title. It does not specify normalization details, but the examples are sufficient for basic use.

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 specific verb ('check') and resource (website in Silicon Friendly directory), and names the key output (agent-friendliness level). It distinguishes itself from siblings by being a 'quick check' for directory membership and level, which sets it apart from tools like get_website or verify_website.

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 implied usage is to check a domain's directory status and friendliness level, but it does not explicitly mention when to use this over alternatives or provide exclusion criteria. Since there is no explicit comparison to sibling tools, the guidance remains 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?

No annotations are provided, but the description indicates a read-only operation ('Get info') and describes what is returned. It does not disclose potential failure modes or edge cases, but for a simple no-parameter getter, this is adequate and adds useful output context.

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-loads the main purpose, and provides a clear 'Returns' note. Every word contributes value; no redundancy or irrelevant 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 (no parameters, no output schema), the description fully covers what the agent needs to know: what the tool does and what it returns. It is complete in context, even without explicit usage scenarios.

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 schema provides no parameter semantics. Per the rubric, zero parameters warrant a baseline of 4. The description does not need to explain parameters, and the output details are described instead.

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 information about the 5-level rating system and all 30 criteria. This specific verb+resource combination distinguishes it from sibling tools, which focus on website operations like verification and 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 implies the tool is used when an agent needs details about the rating system or criteria, but it does not explicitly state when to use it versus alternatives or mention any exclusions. The sibling tools have obviously different purposes, so the context is clear though not explicitly stated.

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 present, so the description carries the burden. It discloses the auth token requirement and return size limit (up to 10), but does not mention error behavior, whether the queue is empty, or if it has side effects (though 'get' suggests read-only). Lacks detailed behavioral context.

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 with clear Args and Returns sections, no fluff. Every line 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 a simple tool with one parameter and no output schema, the description covers the essentials: what it returns, the auth requirement, and the reward. Minor gaps like empty queue handling are not critical.

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 provides only a string with default; the description clarifies it's a Silicon bearer token used for authentication, giving purpose and type context beyond 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 retrieves a queue of websites needing verification, and adds the incentive context (10 search queries). This differentiates it from sibling tools like verify_website or list_verified_websites.

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 this is the entry point for verification tasks ('Verify them to earn...'), but does not explicitly state when to prefer it over list_verified_websites or search_websites. No exclusions are provided.

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 provided, the description carries the full burden, but it is transparent about being a read operation that returns website details. It lists the exact fields returned (name, domain, level, verification info, all 30 criteria), giving the agent a clear expectation of the output without hiding any side effects or prerequisites.

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 concise and well-structured with Args and Returns sections. It includes only essential information, no fluff, and is easy to parse. Every 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?

The tool is simple (1 param, no output schema), but the description provides sufficient context by listing the return fields. It mentions 'verification info' and 'all 30 criteria', giving the agent a complete picture of what to expect. No critical information 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 schema has 0% description coverage for the domain parameter, but the tool description fully compensates by explaining 'domain' with real examples ('stripe.com', 'github.com'), adding meaning beyond the bare schema. This makes the parameter's purpose and format clear.

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 ('Get details') and resource ('specific website in the Silicon Friendly directory'), distinguishing it from sibling tools that list or search websites. The specific focus on a single domain via 'domain' parameter makes 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?

The description implies the correct use case: when you have a specific domain and need details about it. It doesn't explicitly mention alternatives like 'search_websites', but the context of a specific website is clear, and no exclusions are needed.

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 transparency burden. It discloses sorting order, pagination size ('20 results per page'), and return content (level and basic info), but does not mention permissions, rate limits, or that the operation is read-only. This is adequate but not fully 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?

The description is concise: a one-sentence purpose, then an Args block and a Returns block. Every line adds useful information without filler, and the structure is easy to scan.

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 listing tool with no output schema, this description provides enough context: the resource, sorting, pagination, and return shape. It could list specific fields, but that is not necessary for selecting or invoking the tool.

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 0%, so the description is the only source of parameter meaning. It defines 'page' as a page number and notes '20 results per page,' adding real semantic value beyond the schema's bare integer type and default.

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 ('List') and resource ('all verified websites in the directory') with an explicit sorting detail ('sorted by most recently updated'). This clearly differentiates it from siblings like search_websites or get_website.

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 clearly implies when to use this tool (when a full paginated listing of verified websites is needed) and distinguishes it from search or single-website tools. However, it does not explicitly name alternatives or state when not to use it, so it falls short of a 5.

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 description explains the behavior well by detailing the return format and the difference between semantic and keyword searches. However, with no annotations provided, it does not explicitly state that the tool is read-only or mention potential rate limits, pagination, or error conditions, leaving some behavioral gaps.

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 succinct and well-structured, opening with a clear purpose sentence and then using an Args/Returns format. Every sentence provides useful information without waste.

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 description covers the essential aspects: purpose, parameters, and return values. Since there is no output schema, including the return fields is important. It lacks some detail on result limits or ordering, but for a simple search tool it is sufficiently complete.

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 fully compensates by explaining both parameters with examples and clarifying the default and distinction for search_type. It adds meaning well beyond the bare 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 a directory for AI-agent-friendly websites, using a specific verb and resource. It distinguishes itself from sibling tools like list_verified_websites by emphasizing query-based search rather than listing all entries.

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 the tool (query-based search) and explains the two search types and their trade-offs. However, it does not explicitly mention alternatives like list_verified_websites or when not to use this tool, so it lacks explicit exclusions.

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 provided, the description carries the full burden. It discloses the authentication requirement and notes that an error is returned if the website already exists, giving insight into idempotency and failure behavior. It does not cover rate limits or reversibility, but covers the key operational traits for a submit action.

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 with a clear opening, a note on authentication, an Args block, and a Returns block. It is somewhat lengthy but each sentence/block adds value, especially since the schema lacks descriptions. The purpose is front-loaded, making it efficient to scan.

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 (4 parameters, no output schema, no annotations), the description is fairly complete: it covers authentication, all parameters, and the return/error behavior. It does not mention potential prerequisites like checking for existing entries via sibling tools, but for a submission tool, this 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 description coverage is 0%, so the parameter descriptions in the tool description are essential. Each parameter (url, name, description, auth_token) is described with practical examples for url and name, and clear purpose for description and auth_token. This goes beyond the bare schema and adequately compensates for the missing schema descriptions.

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 'Submit a new website to the Silicon Friendly directory' with a specific verb ('submit') and resource ('website'). It distinguishes itself from sibling tools that check, get, list, search, or verify websites, making the tool's 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 implies usage (adding a website to the directory) and provides a prerequisite ('Requires authentication'), but does not explicitly mention when to use this tool over alternatives or any exclusions. Sibling tools that perform reads or verification are not contrasted, leaving the agent to infer the appropriate context.

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 discloses that the tool evaluates against 30 criteria, returns whether the verification is new, and awards search queries. This gives insight into deduplication behavior and outcomes. However, it does not mention potential side effects, failure modes, or permissions beyond the auth_token parameter, leaving notable behavioral gaps.

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 compact and well-organized: a two-sentence summary, then an Args section with per-parameter explanations, and a Returns line. Every element serves a purpose: the incentive statement ('Earns you 10 search queries'), the criteria example, and the return summary are all useful. No redundant or filler 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?

The tool has a nested criteria object and no output schema, but the description covers the essential usage: how to supply criteria (including cross-reference to get_verify_queue), what the return contains ('whether it was new and queries awarded'), and the auth requirement. It is complete enough for an agent to invoke correctly, though it could further explain error cases or the exact shape of the verification result.

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 provides only parameter names and types, so the description fully compensates with detailed explanations: domain includes an example, criteria is described as a dict of 30 boolean fields with a concrete example and cross-reference to get_verify_queue, and auth_token is clearly identified as a Silicon bearer token. All three parameters are given meaningful context beyond 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 action: "Submit a verification for a website - evaluate it against all 30 criteria." This uses a specific verb (submit/evaluate) and resource (website), and the phrase "all 30 criteria" distinguishes it from sibling tools like submit_website by indicating this tool runs the full verification evaluation, not just submitting a URL.

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 useful usage context: it mentions earning 10 search queries for each new verification, which signals reward/incentive, and explicitly tells the agent to "See get_verify_queue for field names," directing to a sibling tool for necessary parameter values. However, it does not explicitly state when to use this tool over alternatives or when not to use it, so it lacks direct exclusions.

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