Parse

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

The description adds value beyond the readOnlyHint annotation by explaining the output format (JSON-text with human-readable text). However, it does not disclose error behavior, rate limits, or required permissions. The annotation already indicates read-only, so the description moderately enhances 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 with no fluff. It front-loads the key information (compatibility alias, what it does) and uses examples efficiently. Every word 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?

Given the tool's simplicity (one parameter, no output schema), the description covers the output format and parameter examples. However, it lacks guidance on limitations, error handling, and when to prefer this over sibling tools. For a compatibility alias, more context on deprecation or migration would be helpful.

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 compensates by providing example fetch IDs ('brand:stripe', 'prompt:best-crm'), implying the expected format for the single 'id' parameter. This adds meaningful context 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 states it resolves fetch IDs into JSON-text results with human-readable text, and gives examples like 'brand:stripe'. This clarifies the tool's purpose as a compatibility alias, though the verb 'resolves' is somewhat vague. It distinguishes itself from sibling tools like parse_get_brand by being a generic fetch.

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?

No explicit guidance on when to use this tool vs. siblings like parse_get_brand or search. The description mentions 'compatibility alias' but does not elaborate on context, prerequisites, or alternatives. The agent is left to infer usage.

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, so the description adds value by detailing the return content (strengths, prompts, citations, etc.), which informs the agent about what the brief includes. However, it does not disclose additional behavioral aspects such as data freshness, request rate limits, or error handling.

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 concise sentence that efficiently communicates the tool's output and contents without unnecessary words. It is front-loaded with the key action 'Fetch a concise public marketing brief'.

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 with one parameter and no output schema. The description adequately lists return fields, but the lack of parameter explanation and any hint about error conditions or edge cases (e.g., brand not found) leaves the agent with some ambiguity for a complete call.

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 coverage is 0%, so the description must explain the parameter 'slug_or_id' but only vaguely refers to 'one brand', failing to clarify whether it expects a slug, database ID, or URL-encoded name. This leaves the agent uncertain about correct input format.

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 fetches a concise public marketing brief for a single brand, enumerating specific content like Parse score, strengths, weak spots, etc. It distinguishes from sibling tools such as parse_get_prompt or parse_get_stats by focusing on a brand-level overview.

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 a brand's marketing brief is needed, but does not provide explicit guidance on when to use or avoid this tool versus siblings like search or fetch. No exclusions or prerequisites are mentioned, leaving the agent to infer 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?

Annotations already declare readOnlyHint=true, so the description's 'fetch' is consistent. The description adds the specific behavior of returning the AI-search question, but does not cover potential errors, response format, or other edge cases. With annotations providing the safety profile, this is adequate.

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, clear, and well-front-loaded sentence. No redundant words; every part is 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?

Given low complexity (1 param, no output schema), the description covers the basic purpose and use case. However, it does not describe the return structure or potential errors, which could be helpful. It is minimally complete but not thorough.

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% for the only parameter 'slug'. The description says 'by slug' but does not explain what a slug is, its format, or where to obtain it. This adds minimal meaning beyond the schema, falling short of compensating for the lack of schema description.

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 (fetch), the resource (public organic prompt), and the context (by slug, to inspect AI-search question). It effectively distinguishes from sibling tools like 'search' and 'parse_get_stats'.

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: 'when the user wants to inspect the exact AI-search question behind a result.' It implies when to use but does not explicitly exclude alternatives or mention when not to use it. This is good but 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?

Annotations already indicate readOnlyHint=true, so the description's 'Explain' aligns. No additional behavioral details beyond what annotations provide; context on what is explained adds marginal value.

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?

Single sentence with 14 words, no filler, immediately conveys the purpose.

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 read-only tool with no parameters and no output schema, the description adequately covers what it does. Could hint at output format, but 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?

No parameters exist, so schema coverage is 100%. Baseline for 0 params is 4, and the description does not need to add parameter information.

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 explains the public Parse index scale and freshness, specifying tracked brands, organic prompts, and citation observations. It uses a specific verb and resource, and distinguishes from siblings like parse_get_brand and parse_get_prompt.

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?

No guidance on when to use this tool versus siblings like parse_get_brand or search. It does not mention prerequisites or when to avoid using 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?

Annotations already declare readOnlyHint: true, and the description's 'Find' aligns with a read operation. The description does not add additional behavioral context beyond what annotations provide (e.g., no mention of rate limits, pagination, or side effects). With annotations covering the safety profile, a 3 is appropriate.

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 extremely concise and efficient: two sentences with no waste. The first sentence states the purpose and scope, the second provides usage guidance. It is front-loaded with essential information.

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 complexity (3 parameters, no output schema), the description covers the high-level purpose and usage cue but lacks detail on parameter specifics and return values. For a search tool, the description is adequate but could be improved by clarifying what the output looks like or providing more parameter context.

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 lists the types of things (brands, prompts, sources, niches) which hint at the 'types' parameter's enum values. However, it does not explain the 'query' parameter's semantics beyond being a search string, nor does it mention the 'limit' parameter. This is insufficient for a tool with 3 parameters and no 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 the tool's purpose: 'Find brands, organic AI prompts, citation sources, and market niches for marketer research.' It uses a specific verb ('Find') and lists the resources. It distinguishes itself from siblings like parse_get_brand and parse_get_prompt by positioning itself as the first step ('Use this first').

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 states when to use the tool: 'Use this first when the user names a brand, category, source, or AI visibility question.' This provides clear context for invocation and implies it precedes more specific tools. It lacks explicit 'when not to use' but the guidance is strong enough for a 4.

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, and the description adds transparency by stating it is an alias. No conflicting or hidden behaviors are implied.

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 extremely concise with only two sentences, front-loading the critical alias purpose immediately without any unnecessary 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?

Given that it is a simple alias tool with annotations covering read-only behavior, the description is adequately complete for its limited scope, though it omits parameter explanations.

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%, and the description provides no information about the parameters (limit, query) beyond their existence in the schema, failing to add meaning for the agent.

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 identifies the tool as a compatibility alias for parse_search, explicitly stating its purpose and distinguishing it from the actual search tool.

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 instructs to use this tool for clients that expect a generic search tool, providing clear contextual guidance on when to use it versus alternatives like parse_search.

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