TO1 Intelligence Fleet

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

No annotations are provided, so the description must carry the behavioral transparency burden. It states the tool fetches 'all activity suggestions' for a given category and enumerates valid types. However, it omits details about return format, pagination, or error handling, which would enhance 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 three sentences long with no wasted words. It front-loads the purpose, immediately lists valid types, and provides usage guidance. Every sentence is necessary and well-placed.

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, no nested objects), the description fully covers what the tool does, its parameters, and when to use it. No significant 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 already covers the parameter 'type' with enum and description (100% coverage). The description adds value beyond the schema by listing valid types in natural language and giving usage examples, reinforcing the semantic meaning.

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 begins with a clear verb ('Get') and resource ('activity suggestions') and specifies the filtering aspect ('matching a specific category'). It lists valid types and provides usage examples, clearly distinguishing it from sibling tools like get_random_activity.

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 tells when to use the tool with an example ('Use this when the user wants ideas within a particular theme') and provides concrete phrases like 'something relaxing'. Though it does not state when not to use or mention alternative tools, the guidance is clear and sufficient.

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 explains that the tool returns one completely random activity and lists the included fields. For a read-only random generator, this is sufficient behavioral disclosure. No contradictions with annotations (none exist).

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 sentences: the first specifies the action and output content, the second gives usage direction. No unnecessary words. Ideal conciseness.

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 no parameters, no output schema, and no annotations, the description fully informs the agent about what the tool does, what it returns, and when to use it. Complete for a simple random selection 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?

With zero parameters and 100% schema coverage, the description does not need to add parameter info. The baseline for no parameters is 4, and the description meets that.

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 verb ('Get'), the resource ('random activity suggestion'), and the specific purpose ('to fight boredom'). It lists what is included (activity description, category type, etc.) and explicitly says 'no filters', distinguishing it from siblings like get_activity_by_type or suggest_activity_for_group.

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 second sentence explicitly tells when to use this tool: 'when the user just wants a single spontaneous idea with no filters.' This provides clear usage context. It does not explicitly state when not to use, but the contrast with filtered siblings is implied.

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 does not mention behavioral traits like input validation (e.g., positive integer), rate limits, or side effects. It assumes reasonable use but lacks explicit safety or idempotency info.

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 long, directly states the tool's purpose and when to use it, with no unnecessary words. Excellent front-loading.

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 tool with 2 parameters and no output schema, the description provides sufficient context about purpose and usage. It could mention the expected output briefly, but the lack of output schema reduces the need.

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 the baseline is 3. The description adds value by giving examples of participant values (solo, couple, group) and explaining the optional type parameter's role, exceeding the 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 gets activity suggestions sized for a specific number of participants, with optional category filtering. It distinguishes from siblings like get_activity_by_type and get_random_activity by focusing on the participant count.

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 'Use this when the user specifies how many people are involved' and provides example queries. It does not explicitly mention when not to use it, but the context is 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 the full burden. It discloses that the tool returns a random piece of advice from a specific source (Advice Slip) and describes the output as 'thoughtful, concise.' It does not mention any side effects or safety info, but given the tool's read-only nature, 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 extremely concise: two sentences. The first sentence defines the action and source, and the second adds context about the output and use cases. There is no wasted text, and the key information is front-loaded.

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 tool with no parameters and no output schema, the description covers essential aspects: source, randomness, output quality, and use cases. It could mention potential rate limits or caching, but it is sufficient for an agent to understand and invoke the tool correctly.

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 is empty, meaning no parameters are needed. The description does not need to explain parameters, and it implicitly handles this by not mentioning any. Since schema coverage is 100%, the description adds no redundancy.

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: 'Get a random piece of wisdom or life advice from Advice Slip.' It specifies the source and the nature of the output. It implicitly distinguishes from sibling tools like advice-intel__get_multiple_advice and advice-intel__search_advice by focusing on a single random piece.

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: for motivation, life coaching, or adding wisdom to AI responses. While it doesn't explicitly state when not to use or list alternatives, the use cases are well-defined and suggest that for multiple advices or search, other tools would be appropriate.

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 carry the full burden. It mentions the random nature and count constraints (2-10) but does not disclose whether the operation is idempotent, whether it has side effects, or what happens on invalid count. For a simple read tool, this is minimally 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 two sentences long, front-loaded with the operation and constraints, and contains no extraneous words. Every part is useful 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 tool with a single parameter, no output schema, and a straightforward purpose, the description covers the key aspects: what it does, the count range, and a typical use case. The only gap is the lack of return format, but for a simple random advice tool, this is acceptable.

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% and the schema already describes the 'count' parameter. The description adds the phrase 'random advice' but does not add substantive meaning beyond what the schema provides. 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 the tool retrieves multiple random advice pieces (2-10) and distinguishes it from siblings like get_advice (single) and search_advice (search). The verb 'Get' is specific and the resource 'random advice' is 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 a use case ('useful when you need a selection of wisdom to present to the user'), which implicitly guides when to use it. However, it does not explicitly exclude alternatives like get_advice or search_advice, which slightly reduces clarity.

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 but only states high-level purpose. It does not disclose return format, result count, or any behavioral traits like pagination or determinism.

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 one sentence plus a list of examples, no wasted words. It is 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 search tool with one param, the description covers basic intent. However, it lacks details on result format, number of results, or any output schema, which a full search tool might need.

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 a descriptive param. The description adds some example topics, but the param's own description already provides examples, so the added value is marginal.

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 for advice by topic or keyword, with examples. It is distinguishable from sibling tools like get_advice and get_multiple_advice, which suggest different retrieval methods.

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 using this for topic-based search but does not explicitly compare to siblings or specify when not to use it. Alternatives are neither named nor excluded.

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, but description mentions returns plain text and no parameters. Could add potential error info, but adequate for a simple fetch.

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?

Concise single paragraph, front-loaded, no filler.

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?

Complete for a simple tool: source, return type, usage context. No missing details.

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?

Zero parameters, so baseline 4. No need for parameter info.

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 verb 'Fetches' and resource 'single random positive affirmation'. Distinguishes from sibling 'get_multiple_affirmations'.

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?

Explicitly says when to use: for one uplifting statement, with examples. Implicitly excludes multiple affirmations via sibling.

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?

Since no annotations are provided, the description carries full burden. It discloses that the tool 'internally loops the underlying API' due to lack of a native batch endpoint and warns that 'duplicates across calls are possible' due to randomness. This provides useful behavioral insight beyond 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 long and front-loaded: the first sentence defines the purpose, the second provides usage guidelines and behavioral context. 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?

Given the tool's simplicity (one parameter, no output schema), the description covers purpose, usage context, and internal behavior. It does not specify return format, but the sibling tool likely provides that context. Overall sufficient for effective use.

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 100% for the single 'count' parameter, which has a clear description of its range. The description reinforces the batch size purpose but does not add new semantic details beyond what the schema already provides.

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 verb 'fetches', the resource 'batch of 2 to 10 random positive affirmations', and the source 'affirmations.dev'. It explicitly distinguishes from the sibling tool 'get_affirmation' by noting batch vs. single.

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 when to use this tool: 'when an agent needs a list of several distinct affirmations at once' with concrete examples like 'daily-affirmation calendar, rotating banner, or content batch'. It contrasts with repeated calls to 'get_affirmation'.

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 takes on full disclosure responsibility. It explicitly lists the output components (average change, gainer/loser count, etc.), but does not mention data freshness, read-only nature, or potential side effects. However, the listed outputs provide good transparency for a no-parameter snapshot tool.

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, front-loaded with the tool tag. It contains no extraneous information and conveys all necessary details 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?

Given no parameters and no output schema, the description adequately explains the tool's output with specific metrics. It is complete enough for a simple snapshot tool, though it could mention data source or update frequency for full 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, so schema coverage is 100%. The description adds meaning by detailing exactly what the tool returns, exceeding the baseline expectation. This provides clear value for tool invocation.

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 retrieves an aggregate sector health snapshot for AI/agent infrastructure tokens, listing specific metrics (average change, gainer/loser count, best/worst performer, sentiment). This differentiates it from siblings like get_ai_tokens and get_market_sentiment.

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 obtaining a broad sector overview, but it does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. There is no mention of when not to use it or of related siblings.

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 must fully disclose behavior. It only states the output (prices and performance) but omits details like update frequency, data source, or any caveats. The behavioral profile is minimal.

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, front-loaded sentence that conveys the core purpose without unnecessary words. Every word contributes to understanding.

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, no-parameter tool, the description adequately covers the core functionality and return value shape (prices and performance). However, the lack of output schema and any behavioral context keeps it from being fully 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?

There are zero parameters and schema coverage is 100%, so the description's mention of specific tokens adds value beyond the empty schema. Per rules, baseline for 0 params is 4.

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 verb ('get') and resource ('live prices and 24h performance for top AI/agent ecosystem tokens'), listing specific tokens and implying a distinct focus on individual token data, which differentiates it from sibling tools like get_ai_sector_snapshot.

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 is provided on when to use this tool versus alternatives (e.g., get_ai_sector_snapshot or get_market_sentiment). Agents have no context for preferring this tool over related ones.

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 discloses that it returns present and historical data along with prices and interpretation, but does not explicitly state that it is read-only, any permissions needed, or potential side effects. Without annotations, the description provides a reasonable but not fully transparent picture.

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 efficiently communicates the tool's purpose and output. It is front-loaded with the key action and resources, though could be slightly more 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 tool with no parameters and no output schema, the description adequately covers what the tool returns. It lists specific data points, but could benefit from mentioning the format or structure of the 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?

There are no parameters, so the schema coverage is 100%. The description compensates by detailing the output content (index values, BTC/ETH prices, AI interpretation), which adds meaning beyond the empty 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 it retrieves the Crypto Fear & Greed Index (with yesterday, last week, and trend) along with BTC and ETH prices, plus an AI-sector interpretation. This specific verb+resource distinguishes it from sibling tools like cryptomarket-intel__get_fear_greed_index which only returns the index.

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 a combined use case but does not explicitly state when to prefer this tool over alternatives (e.g., using separate cryptomarket tools for index or prices). No 'when not to use' or comparison is 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?

No annotations provided, so description carries full burden. It mentions ranking by risk-adjusted cost but does not disclose ranking direction (ascending/descending), whether all options are returned, or the definition of 'risk-adjusted cost.' Lacks details on side effects or read-only nature.

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 the namespace prefix, front-loading essential information. It is concise and free of 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?

The tool is a comparison/ranking function with no output schema or annotations. The description lacks details on return format, ranking specifics, and how to structure each option. It is incomplete for an agent to use correctly.

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 only vaguely says 'multiple possible agent actions' for the 'options' parameter. It does not explain the structure or required fields of each object in the array, leaving the input format ambiguous.

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 compares multiple agent actions and ranks them by risk-adjusted cost. It uses a specific verb ('compare' and 'rank') and resource ('possible agent actions'). It distinguishes from siblings like 'quote_action_risk' (single action) and 'make_coverage_note' (cover notes).

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 use when comparing multiple actions, but does not explicitly state when not to use it or mention alternatives. No exclusions or context beyond the basic purpose.

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 mentions a 'stable quote hash' but does not disclose whether the tool is idempotent, what data it modifies, or any required permissions. Lacks essential behavioral context for a creation operation.

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 a domain prefix, making it concise but lacking structure. It front-loads the purpose but omits necessary details, reducing its effectiveness.

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?

No output schema, no annotations, and a complex parameter (object) are present. The description does not explain what a coverage note is, how it is created, or the purpose of the quote hash. Incomplete for an agent to use reliably.

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 3 parameters with 0% schema description coverage. The description alludes to 'exclusions and a stable quote hash' which likely relate to the 'quote' object, but does not explain 'payer' or 'actionId'. Fails to compensate for the schema gap.

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 creates a coverage note, specifying key components: exclusions and a stable quote hash. This distinguishes it from sibling tools like quote_action_risk and compare_risk_options, which deal with quoting and comparison respectively.

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 no guidance on when to use this tool compared to alternatives, no prerequisites, and no exclusions. It merely states what it does, leaving the agent to infer usage 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?

With no annotations provided, the description must fully disclose behavioral traits. It states the tool 'quotes' risk premium, which implies a read-only operation, but does not explicitly confirm no side effects, state changes, or permission requirements. The lack of behavioral context beyond the basic action is insufficient for safe agent invocation.

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, which is concise but under-specified. It lacks necessary detail for a tool with 8 parameters and no schema documentation. However, it is front-loaded and not verbose, earning a middling score.

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 (8 parameters, no parameter descriptions, no output schema, no annotations), the description is woefully incomplete. It fails to inform the agent about parameter usage, return value format, or behavioral context, making it nearly unusable for an autonomous agent.

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 zero description coverage for all 8 parameters, and the tool description does not explain any parameter meanings or relationships. This forces the agent to guess parameter semantics (e.g., what 'action', 'asset', 'irreversible' represent), making correct invocation unlikely without external knowledge.

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: it quotes a risk premium and coverage recommendation for an agent action. It uses a specific verb ('quote') and resource ('risk premium and coverage recommendation'), and it implicitly distinguishes from sibling tools like 'compare_risk_options' or 'make_coverage_note' by focusing on generating a quote rather than comparing or making notes.

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 no guidance on when to use this tool versus alternatives like 'compare_risk_options' or 'make_coverage_note'. It does not mention prerequisites, context, or when not to use it, leaving the agent to infer usage based on the tool name alone.

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 of behavioral disclosure. It only says 'Draft... product card,' but does not indicate whether this creates a persistent record, returns text, or requires any permissions. The draft nature implies mutability but no specifics are given.

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, which is concise, but it lacks structure and substance. It does not front-load key information beyond the basic purpose, and the brevity sacrifices clarity and completeness.

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 is incomplete given the lack of output schema and parameter details. It does not state what the tool returns, how it fits into the niche radar workflow, or any context for using it among many similar intel 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?

Schema description coverage is 0%, meaning the description adds no meaning to the three parameters (nicheName, tollMoment, targetBuyer). The description only mentions 'for a niche' loosely mapping to nicheName, but tollMoment and targetBuyer are not explained at all.

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: 'Draft a tight first product card for a niche.' The verb 'Draft' and specific resource 'first product card' distinguish it from sibling tools like 'score_agent_niche' and 'rank_niche_basket', 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 no guidance on when to use this tool versus alternatives, no prerequisites, and no context about when not to use it. Among siblings like 'score_agent_niche', there is no comparison or exclusionary advice.

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 full burden. It does not disclose whether the tool is read-only, has side effects, or what happens to the input. The minimal description lacks 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 very short (one sentence), which is concise, but it is under-specified. It front-loads the main action but omits critical details, sacrificing completeness for brevity.

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 no output schema, no annotations, and a single undocumented parameter, the description is severely incomplete. It does not explain return value format, required permissions, or error conditions.

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 'niches' has 0% schema coverage. The description adds the context that it is a list of 'possible niches', but does not explain what properties each niche object should contain.

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 verb 'Rank' and resource 'many niches', and specifies the output 'return a build queue'. However, it does not differentiate from the sibling tool 'score_agent_niche', which likely performs a similar function.

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 no guidance on when to use this tool versus alternatives like 'score_agent_niche'. No context about prerequisites or exclusion criteria.

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, and the description does not disclose behavioral traits such as whether it is read-only, side effects, or rate limits. For a scoring tool, it likely has no destructive effects, but this is not confirmed.

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 front-loads the purpose, but it is too brief given the tool's 11 parameters. It lacks structure for complex details.

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 11 parameters, no output schema, and no annotations, the description is woefully incomplete. It does not explain what inputs are needed, how to interpret results, or any prerequisite 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?

With 11 parameters and 0% schema coverage, the description provides no explanation of what any parameter (beyond 'name') means. It fails to compensate for the lack of 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 action ('Score') and the resource ('one agent-economy niche') with the goal of explaining worthiness. It distinguishes from siblings like 'draft_niche_product_card' and 'rank_niche_basket' by focusing on scoring a single niche.

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 it's for evaluating a single niche, but does not explicitly state when to use vs alternatives like 'rank_niche_basket'. No exclusion or alternative criteria 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?

No annotations exist, so the description should disclose behavioral traits. It mentions output components (reasons, risk score, policy hash, receipt) but does not explain whether the tool is read-only, requires authentication, or has side effects. The description is insufficient for understanding the tool's behavior.

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 front-loads the core purpose. It is concise and avoids redundancy, though it sacrifices necessary detail for brevity.

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 10 parameters, 7 required, no output schema, and no annotations, the description is critically incomplete. It fails to explain how to invoke the tool, interpret results, or understand dependencies, making it inadequate for a complex decision 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?

With 10 parameters and 0% schema description coverage, the description should clarify parameter meanings. It adds no value beyond the schema—just lists output aspects. Parameters like 'action', 'policy', and 'spentTodayUsd' remain unexplained, leaving the agent in the dark about what to provide.

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 verb 'Decide' and the resource 'whether an agent should spend money on a tool call', with specific outputs like reasons, risk score, policy hash, and receipt. It distinguishes from sibling tools by focusing on evaluation rather than policy creation or vendor scoring.

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 that this tool is used when an agent needs a spending decision, but it does not explicitly state when to use it versus alternatives like make_toll_policy or score_tool_vendor. No exclusions or conditional guidance 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?

No annotations exist, and the description does not disclose behavioral traits such as idempotency, side effects, mutation, or authorization needs. The term 'normalize' is ambiguous, and the hash generation process is not explained.

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 with no extra information. It includes the tool group prefix. However, its brevity sacrifices necessary detail for a complex tool.

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 an 11-parameter tool with no output schema and no annotations, the description is severely incomplete. It does not explain the return value format, how parameters define the policy, or any constraints. The tool lacks sufficient context for correct invocation.

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 11 parameters and only 27% schema coverage, the description adds no parameter-level details. It does not explain parameters like allowedTools, blockedTools, or riskTolerance. The schema provides defaults for three parameters but most lack 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 action (normalize) and resource (agent spending policy), with a specific outcome (return a stable policy hash) and its purpose (attached to future paid actions). It is distinct from sibling tools like evaluate_agent_spend and verify_toll_receipt.

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 usage guidance is provided. The description does not indicate when to use this tool over alternatives, nor does it specify prerequisites or exclusions. Sibling tools exist but no comparison is made.

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 only says 'score' without explaining what scoring entails (e.g., rating, classification), whether it is read-only, or any side effects. No mentions of auth or 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?

The description is a single sentence that conveys the essential purpose without unnecessary words. It is front-loaded with the tool's core action and resource.

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 7 parameters, no output schema, and no annotations, the description is insufficient. It does not explain the scoring logic, required inputs, or expected output, leaving the agent without enough context to use it correctly.

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 does not discuss any parameters. While property names like name, feeUsd, endpoint are somewhat self-explanatory, the description adds no meaning about their format, constraints, or usage.

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 ('score'), the resource ('a paid tool or agent listing'), and the context ('before another agent buys it'). This distinguishes it from sibling tools like evaluate_agent_spend (which evaluates spend) and make_toll_policy (which creates policies).

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 ('before another agent buys it'), indicating when to use the tool. However, it does not explicitly state when not to use it or mention alternatives, leaving some ambiguity.

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 bears full burden. It fails to disclose whether the tool is read-only, what happens on mismatch, or any side effects. The behavior beyond the check is opaque.

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?

A single, front-loaded sentence with no waste. Every word 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?

Despite low complexity, the description omits return values, error conditions, and prerequisites. With no output schema or annotations, the agent lacks essential context for reliable use.

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 adds context for 'receipt' (toll receipt) and 'expectedPolicyHash' (hash to compare), but does not explain the receipt structure or the 'decision body' referent.

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), the resource (toll receipt), and what it checks against (decision body and optional policy hash). It distinguishes from sibling tools like verify_action_receipt by specifying 'toll receipt' vs 'action receipt'.

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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or refer to sibling tools like verify_action_receipt.

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 full burden. It states it retrieves data (read operation) but does not disclose potential issues like missing city handling, rate limits, or data freshness. Basic but incomplete.

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 with no unnecessary words. It is front-loaded with the essential information and wastes no space.

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 read tool with only two parameters and no output schema, the description is adequate. It covers the core functionality but omits details on error handling, response structure, or any special conditions.

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 100%, so the baseline is 3. The description adds context about geocoding but does not significantly expand on parameter semantics beyond what the schema already provides.

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 verb 'Get', the resource 'air quality', and the scope 'for a city, current and next-hour'. It distinguishes from the sibling tool 'get_air_quality_by_coordinates' by focusing on city-based lookup.

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 for city-based queries via 'using Open-Meteo geocoding', but it does not explicitly state when to use this versus the coordinates sibling. No direct guidance on when to use or not use is 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?

No annotations are provided, so the description must bear the full burden of behavioral disclosure. It states it retrieves current and next-hour air quality (a read operation), but does not disclose any behavioral traits such as data freshness, rate limits, error conditions, or what happens if coordinates are invalid.

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 includes the tool prefix and clearly states the action and resource. It is efficiently structured with no redundant words, though it could potentially include more detail without becoming verbose.

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 (2 required parameters, no nested objects, no output schema), the description provides minimally adequate context. However, it omits details about the return format, units of coordinates, and any limitations, which would help an agent anticipate 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 input schema has two parameters (latitude, longitude) with no schema descriptions (coverage 0%). The description merely names the parameters ('for latitude and longitude') but adds no additional meaning such as valid ranges, format (e.g., decimal degrees), or coordinate system.

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 gets current and next-hour air quality at given coordinates. It uses a specific verb ('Get') and resource ('air quality'), and distinguishes from the sibling 'get_air_quality_by_city' by mentioning coordinates. However, it does not specify what air quality metrics are included (e.g., AQI), leaving some ambiguity.

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 coordinates are available, but does not explicitly state when to use this tool versus alternatives like 'get_air_quality_by_city'. No guidance on when not to use or context for selection is 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?

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It states that the tool returns 'complete information' including specific fields, which implies a read operation. However, it does not mention potential errors (e.g., invalid ID), authentication requirements, rate limits, or any side effects. With no annotations, a score of 3 is appropriate as the description provides basic but not comprehensive 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 two sentences: one for purpose, one for return data. It is front-loaded, concise, and every sentence adds value without 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?

For a simple tool with one parameter and no output schema, the description is complete enough. It specifies the input (mal_id), the output fields (genres, themes, studios, synopsis, trailer URL), and the source (MyAnimeList). The omission of further detail is acceptable given the low complexity.

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% description coverage for the single parameter mal_id. The description adds concrete examples (e.g., '5114 for Fullmetal Alchemist Brotherhood, 1 for Cowboy Bebop'), which help the agent form correct parameter values beyond the schema's generic 'MyAnimeList anime ID' definition.

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 verb ('Get'), resource ('full details for a specific anime'), and identifier ('by its MyAnimeList ID'). It also lists specific data returned (genres, themes, studios, synopsis, trailer URL). This distinguishes it from sibling tools like search_anime (searches by query) and get_top_anime (lists top anime).

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 that the tool is for looking up an anime when you already know its MyAnimeList ID. The context signals show there are siblings like search_anime and get_top_anime, but the description does not explicitly state when to use this tool over those. However, the purpose is clear enough that an agent would 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?

No annotations provided, so description carries full burden. It discloses the tool is read-only and lists optional filter, but does not mention rate limits, default limit, output format, or side effects. Adequate for a simple read tool.

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 of 18 words, front-loaded with core purpose. No superfluous 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?

Tool is simple (list top anime), no output schema. Description covers main functionality and optional filter. Could mention default limit or result structure, but sufficient for a straightforward listing 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 100%, so baseline is 3. Description mentions 'type' filter with examples (TV series, movies, OVAs, specials) but does not add meaning beyond the schema's enum. Limit parameter not mentioned.

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 'Get the top-ranked anime on MyAnimeList' with a specific verb and resource. It also mentions optional type filtering, distinguishing it from siblings like search_anime (search) and get_top_manga (manga vs anime).

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 indicates when to use (get top-ranked) and mentions optional filtering, but does not explicitly state when not to use or suggest alternatives. It is clear for its intended use case.

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 carries full burden. It only states the operation without disclosing if it's read-only, whether it requires authentication, or any side effects. For a listing tool, minimal behavioral context is provided.

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 a single sentence with clear prefix, immediate action, and resource. No wasted words, 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?

Tool is simple with one optional parameter and no output schema. Description covers the main purpose but does not hint at return format, pagination, or what happens if limit is exceeded. Adequate but not 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?

Schema coverage is 100%, so baseline is 3. The description does not add additional meaning beyond what the schema already provides for the 'limit' parameter (default 10, max 25). No new semantic context.

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 'Get', resource 'top-ranked manga on MyAnimeList', and criterion 'by community score'. It distinguishes easily from siblings like 'get_top_anime' (anime vs manga) and 'search_manga' (search vs list).

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 retrieving a ranked list, but no explicit when-to-use or when-not-to-use compared to similar tools like search or pagination. Siblings are named but no guidance is given.

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 full burden. It discloses return fields and works for any anime, but lacks details on pagination, rate limits, auth needs, or behavior when no results are found. This is adequate but not complete.

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: one sentence for purpose, one for return fields, and one with examples. No unnecessary words, and key information is front-loaded.

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 no output schema or annotations, the description covers the essential aspects: what it searches, what it returns, and examples. It could mention result ordering or how to get full details, but it is adequate for a simple search 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 100%, so baseline is 3. The description adds example queries and clarifies the 'query' parameter with specific keywords, enhancing understanding beyond the schema's bare 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 searches for anime series by title or keyword, specifies return fields (MAL score, rank, etc.), and provides examples. It distinguishes from siblings like search_manga (different medium) and get_anime_by_id (by ID).

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 title/keyword searches but does not explicitly state when to use this tool versus alternatives like get_anime_by_id or search_manga. No 'when not to use' or alternative suggestions 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?

No annotations provided, so description bears burden. It implies a read operation with 'returns', but does not explicitly state read-only nature, auth needs, or rate limits. Sufficient for a simple search tool.

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 two clauses, no redundancy, efficient and front-loaded with key 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 low complexity (2 params, no output schema), description adequately covers purpose and output. Missing annotations but not critical for this tool type.

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 descriptions for both parameters. Description adds minor context for `query` (title or keyword) but not for `limit`. 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?

Description clearly states it searches for manga by title or keyword and specifies returned fields. While sibling tools like `get_top_manga` and `search_anime` exist, the purpose is distinct and well-defined, but lacks explicit differentiation.

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 implies usage context (search by title/keyword) but does not provide explicit guidance on when to use or avoid, nor mentions alternatives among siblings.

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 full burden. It discloses that the tool returns 'complete information including all images, full artist biography, provenance, and tags', which is useful. However, it does not mention potential side effects, authentication needs, rate limits, or error conditions. For a read-only lookup, this is adequate but has 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?

Two sentences: the first states the purpose and required identifier, the second lists the return contents. No unnecessary words, front-loaded with key 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 simple nature of the tool (one parameter, no output schema), the description is complete enough. It covers input requirements and output contents. Could potentially mention that the tool is specific to the Met Museum and does not support free-text search.

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 a single parameter already described. The description adds no extra semantic value beyond restating 'Met Object ID' and the example. Baseline score of 3 applies.

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 verb 'Get' and the resource 'full details for a specific artwork by its Met Object ID'. It effectively distinguishes from siblings like art-intel__search_artwork and art-intel__random_artwork, 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 implies when to use: when you have a specific Met Object ID and need full details. It does not explicitly state when not to use or provide comparisons to alternative tools, but the context is clear with sibling tools available.

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 lists 17 departments, implying a finite set, and it is a read-only operation with no side effects. This is transparent enough for an agent to understand behavior.

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 main purpose, and each sentence serves a distinct function: stating what it does and how to use its output. No 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 tool with no input or output schema, the description sufficiently explains what the tool returns (list of departments with IDs) and how to use that output in a subsequent tool (search_artwork). It is complete given the tool's simplicity.

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 schema coverage is 100%. The description adds value by providing examples of the data returned (department names) and its usage context, giving the agent more semantic understanding than the schema alone.

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 lists all 17 departments in the Met collection, with specific examples (Egyptian Art, Modern Art, Musical Instruments), and explains how to use the output (department ID) with search_artwork, distinguishing it from sibling tools.

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 tells the agent to use the department ID in search_artwork to filter results, providing clear guidance on how to use this tool as a prerequisite for another tool. It doesn't mention when not to use it, but for a simple list tool, this is sufficient.

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 carries burden. Discloses default return of public domain works, but lacks details on randomness mechanism, return format, or any restrictions beyond the default.

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, no wasted words, front-loaded with purpose and default behavior.

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 optional param, no output schema), description provides sufficient context: purpose, default, use case, and randomness.

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 covers the single parameter fully (100% coverage). Description aligns with parameter but adds no extra meaning beyond what schema already provides.

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 verb 'get', resource 'random artwork from Met collection', and default scope 'public domain artworks with images'. Distinguishes from siblings like 'get_artwork' and 'search_artwork'.

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?

Mentions use cases: 'art-of-the-day features or random discovery'. Does not explicitly state when not to use, but context is 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?

No annotations are provided, so the description carries the full burden. It mentions the collection size and return fields, but does not disclose behavior such as result ordering, rate limits, or pagination. The description is minimally adequate for a search tool.

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 includes essential information: source, scale, examples, filter capability, and return fields. It is concise and front-loaded with the tool's name prefix and main action.

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 no output schema, the description compensates by listing return fields. It covers the main purpose, includes collection size and examples, and mentions filtering. It could mention the default limit (5) and max (10) but those are in the schema. Overall fairly 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?

Schema coverage is 100%, so baseline is 3. The description adds value by specifying the return fields (artist, date, medium, dimensions, image URL), which are not in the schema. This helps the agent understand what data will be returned.

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 the Metropolitan Museum of Art collection, lists example artwork types (paintings, sculptures, etc.), and mentions filtering by department. This distinguishes it from sibling tools like get_artwork (single artwork by ID) and random_artwork.

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 explains what the tool does and that it can be filtered by department, implicitly guiding when to use search vs get_artwork. However, it does not explicitly state when not to use it or provide alternatives, which is a minor gap.

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 carries full burden. It discloses sorting by edition count, return content (bibliography, publication years, reader stats). No mention of pagination or data freshness, but acceptable for a simple read operation.

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 sentences, front-loaded with main function and sorting, then return content. No 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?

No output schema, so description must describe returns. Mentions 'publication years and reader stats' but lacks explicit structure (e.g., array of books). Could be more precise.

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 clear parameter descriptions. The description does not add significant extra meaning to parameters beyond what schema already provides.

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 verb 'Get' and resource 'books by a specific author', and specifies sorting by number of editions. It differentiates from sibling tools like get_book_by_isbn and search_books by focusing on author bibliography, but does not explicitly contrast them.

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?

Implied usage: use when you need books by an author. No explicit guidance on when not to use (e.g., when searching by title use search_books) or 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 are provided, so the description bears full responsibility for behavioral disclosure. It mentions the return payload (publisher, date, pages, subjects, description) but does not disclose any side effects, error handling, rate limits, or whether it calls external APIs. This leaves gaps in 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 two sentences, directly stating the action, input, output, and purpose. Every sentence serves a purpose, no wasted words. It is well-structured and easily scannable.

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, single-parameter lookup tool without an output schema, the description adequately covers input, output, and use case. However, it omits potential behavioral details (e.g., error handling for invalid ISBNs) and lacks annotations. Given the tool's simplicity, it is mostly complete but could be enhanced.

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 complete coverage (100%) for the single 'isbn' parameter, including format examples and allowed hyphens. The description adds limited semantic value by stating it accepts ISBN-10/13 and the use case, but does not significantly enhance beyond the schema. 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's action: 'Look up a specific book by ISBN (ISBN-10 or ISBN-13).' It specifies what is returned (full book details including publisher, publication date, pages, subjects, description) and its use case ('useful for identifying exact editions'). This distinguishes it from sibling tools like 'books_by_author' and 'search_books'.

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 when to use this tool ('when you have an ISBN and need exact edition details'), but does not explicitly state when not to use it or alternatives. The context is clear enough for an agent to infer appropriate usage, but explicit exclusions would improve 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?

No annotations are provided, so the description must disclose behavioral traits. It doesn't mention rate limits, pagination, data freshness, or side effects. The search is inherently read-only but not stated.

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 concise sentences plus a tagline, front-loaded with the main purpose and return details. No superfluous 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?

Despite no output schema, the description explains return fields (publication year, authors, etc.). However, it lacks operational details like pagination handling, result limits beyond the 'limit' parameter, or total result count.

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 100%, so the baseline is 3. The description adds minimal extra meaning beyond the schema, like listing example values for query and explaining return fields matching search types.

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 Open Library by title, author, subject, or keyword, and lists return fields. It distinguishes from siblings like 'books_by_author' and 'get_book_by_isbn' by being a general 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 it's the general search tool with multiple search types (title, author, subject, general). It doesn't explicitly exclude when-not-to-use or compare with alternatives, but the schema's 'search_type' enum provides practical 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?

No annotations are provided, so the description carries the full burden. It states the filtering behavior and the requirement that at least one of city/state/country must be provided, but does not disclose response format, pagination behavior beyond per_page parameter, or error cases. For a data retrieval tool, this is adequate but not rich.

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 purpose, and provides a usage example and constraint. Every sentence adds value, with no 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 the tool's simplicity (find breweries by location with optional parameters) and no output schema, the description covers purpose, usage, and constraints. It lacks details on default pagination (default 10 from schema) and return format, but is mostly complete for an agent to use correctly.

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 descriptions for all four parameters. The description adds value by stating the constraint 'At least one of city, state, or country must be provided; combine them for a more precise match,' which is not in the schema. This helps the agent understand parameter interaction.

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 function: 'Find breweries filtered by city, state/province, and/or country.' It provides specific usage examples (e.g., 'breweries in San Diego, California') and implicitly distinguishes from sibling tools like find_breweries_by_type or random_brewery by focusing on location-based filtering.

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 says 'Use this when the user wants to discover breweries in a specific place' and gives examples, which provides clear context. However, it does not explicitly tell when to avoid this tool (e.g., if the user wants breweries by type, use find_breweries_by_type) or mention alternatives among siblings, 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?

No annotations are provided, so the description must fully convey behavioral traits. It only states that the tool filters by type and lists valid values, but it does not mention that the tool is read-only, what the response format is, or any side effects, rate limits, or error conditions. This lack of detail reduces 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 a bullet list and examples, no fluff, and the purpose is front-loaded. Every sentence adds value: purpose, valid types, usage guidance.

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 only two parameters and no output schema, the description covers the main purpose and usage context. However, it does not describe the return format (e.g., a list of breweries with fields) or any additional behavior, which a completely helpful description would include.

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 100%, so the baseline is 3. The description repeats the enum values and adds usage examples, but does not provide additional semantic meaning beyond what the schema already provides. The description does not explain the per_page parameter further.

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 'Find breweries filtered by their operational type/category,' which is a specific verb-resource combination. It lists all valid types and provides examples of user intents that map to this tool. It distinguishes from sibling tools like find_breweries_by_location and search_breweries by focusing exclusively on type-based filtering.

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 tells when to use the tool: 'Use this when the user wants a specific kind of producer.' It provides concrete examples. However, it does not explicitly mention alternatives or when not to use it, though the purpose implies that other brewery tools are for different criteria.

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 carries full burden. It states randomness and no filters, but does not disclose any other traits like rate limits, data freshness, or that it is read-only (inferred). Adequate for a simple fetch operation.

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 sentences; first states the core action, second provides usage guidance. No redundant information. 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?

Given low complexity (1 optional parameter, no output schema), the description covers purpose and usage adequately. Lacks explanation of return format, but that is acceptable without output schema. Mentions 'full dataset' which adds 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 coverage is 100%, so the schema fully describes the 'size' parameter. Description does not add additional meaning beyond what the schema provides (default 1 is implied by schema's default, but description doesn't restate). Baseline score of 3.

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 'Fetch one or more random breweries from the full Open Brewery DB dataset' with specific verb and resource. It distinguishes from sibling tools (e.g., find by location/type/search) by emphasizing 'random' and 'no filters'.

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?

Explicitly states when to use: 'for discovery, inspiration, trivia, or when the user just wants a surprise brewery suggestion with no filters.' Implicitly advises against use when filters are needed, but does not explicitly name 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 carries full burden. It mentions returns up to per_page records and lists returned fields but does not explicitly state it's read-only or disclose any side effects, permissions, or 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?

Concise, front-loaded with purpose, and every sentence adds value. 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 no output schema, description helpfully lists return fields. It explains when to use and provides examples. Could mention that it's not for location/type-based queries, 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 100%, so baseline is 3. Description adds minor value by giving search examples and noting return fields, but the schema already covers parameter meaning adequately.

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 'Full-text search for breweries, cideries, and other beverage producers by name or keyword' with examples. It distinguishes from sibling tools like find_breweries_by_location and find_breweries_by_type by focusing on name/keyword 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?

Explicitly says 'Use this when the user names or partially names a specific brewery they're looking for.' Provides clear context but could further specify when not to use (e.g., location-based queries).

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 full burden for behavioral disclosure. It says 'Check if a proposed spend fits', suggesting a read-only boolean or status result, but does not clarify side effects, required plan structure, or error behavior (e.g., what if plan is invalid).

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 no redundancy, including namespace prefix. Efficient but could be more structured (e.g., separating key info).

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?

No output schema, no annotations, 5 parameters with 0% schema description coverage. The description lacks return value details, error handling, and detailed parameter roles, making it incomplete for a check 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 description coverage is 0%, so the description must compensate. It mentions 'budget plan and category limits', providing high-level context for the plan object and category parameter. However, it does not explain daysElapsed, spentSoFarUsd, or the structure of the nested plan object.

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 states 'Check if a proposed spend fits inside a budget plan and category limits', using a specific verb (check) and resource (proposed spend, budget plan, category limits). It clearly differentiates from sibling tools like forecast_budget_burn (projecting burn rate) and make_budget_plan (creating plans).

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 implies usage for checking spend compliance but does not explicitly state when to use this tool versus alternatives (e.g., forecast_budget_burn for projections). No prerequisites or exclusions 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the basic function without mentioning side effects, idempotency, data sources, or any other behavioral traits. The description adds minimal transparency beyond the literal 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 a single sentence, but it includes the redundant domain prefix '[budget-oracle-intel]' which consumes space without adding value. The core information is front-loaded, but the lack of structure (e.g., separate lines for inputs/outputs) reduces readability. It is concise but not optimally structured for an MCP tool.

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 3 parameters, no output schema, and no annotations, the description is too brief. It fails to explain the return format, expected behavior for different inputs, or any edge cases. A forecasting tool requires clearer documentation to be used correctly, especially without schema descriptions.

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%, meaning none of the three parameters have descriptions in the input schema. The description only mentions 'spend events' generically and does not clarify the structure of the events array, the meaning of budgetUsd, or the role of periodDays. This forces the agent to guess parameter semantics.

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 ('Forecast') and identifies the resources ('budget burn, runway, and overspend risk') and source ('from spend events'). This clearly distinguishes it from sibling tools like check_budget_event and make_budget_plan, which handle different budget operations.

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 forecasting based on spend events, but it does not explicitly state when to use this tool versus alternatives (e.g., check_budget_event for single events or make_budget_plan for planning). No exclusions or caveats are provided, leaving the agent to infer context from the name alone.

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 must carry the full behavioral disclosure. It only states 'create a budget plan', which implies a mutation, but does not detail side effects (e.g., whether it overwrites existing plans), authorization requirements, or durability of the plan. The description is insufficient for an agent to understand the impact.

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 extra words, achieving conciseness. However, the inclusion of the domain prefix '[budget-oracle-intel]' is redundant since the tool name already contains it. The structure is acceptable but the content is too sparse to be highly useful.

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?

With 4 parameters, no output schema, and no annotations, the description is critically incomplete. It omits parameter descriptions, return value details, error conditions, and behavioral expectations. An agent cannot reliably invoke this tool without additional 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%: the description does not explain any of the 4 parameters (budgetUsd, categories, periodDays, reservePct). Even the required parameter budgetUsd is not described. The agent cannot infer parameter meanings or constraints from the description alone.

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 verb 'create' and the resource 'budget plan', specifying it can be daily, weekly, or task-based. It distinguishes from siblings like check_budget_event and forecast_budget_burn, which focus on checking or forecasting budgets rather than creating them.

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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or when not to use it. The phrase 'for an agent' is vague and does not help an agent decide between this and sibling tools.

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 it returns up to 50 breeds and includes attributes, but does not mention pagination, authentication requirements, or output format. It is adequate but lacks depth for a fully transparent description.

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 front-loads the tool prefix in brackets, is immediately clear, and contains no extraneous 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 the simplicity of the tool (one optional parameter, no output schema, no annotations), the description covers the essential: purpose, returned fields, and limit. It could specify the response structure but is sufficient for a straightforward list 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 description coverage is 100% (the only parameter 'limit' is fully described in the schema with range and default). The description adds the context of 'up to 50 breeds' but does not significantly enhance parameter understanding beyond what the schema provides.

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 'Get a list of cat breeds' with specific fields (country of origin, coat type, pattern) and a limit. It clearly distinguishes from sibling tools like get_cat_fact and get_multiple_cat_facts, which focus on facts rather than breeds.

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 needing a list of cat breeds with specific attributes). While it does not explicitly state when not to use it or name alternatives, the context from sibling tools makes the purpose evident.

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 sufficiently discloses behavior: it returns a random cat fact with predictable scope. It implies no side effects, though it doesn't explicitly state read-only or output format.

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?

Three sentences, each earning its place: core function, scope of facts, and use cases. No 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 simple, parameterless tool, the description covers what, why, and when. However, it does not specify the return format or that it returns exactly one fact, which might be inferred but not explicit.

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 no parameters, so coverage is 100% trivially. The description adds value by enumerating the types of facts covered, which helps the agent understand the tool's domain.

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 gets a random cat fact, listing specific topics (biology, behavior, etc.). It does not explicitly differentiate from siblings like get_cat_breeds or get_multiple_cat_facts, but the name and context suggest singularity.

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 broad usage context (cat lovers, education, fun fact) but no guidance on when to choose this over siblings 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.

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

No annotations are provided, so the description carries full burden. It correctly states the tool returns random cat facts but omits any behavioral traits like side effects, authorization needs, or rate limits. The read-only nature is implied but not explicitly stated.

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, concise, front-loaded with tool tag. No wasted words. Perfect structure for quick comprehension.

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 tool with one parameter and no output schema, the description covers the core functionality, constraints, and use cases. It could mention data source or caching but is largely complete for typical use.

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% (one parameter 'count' fully described). The description adds context about valid range (2-20) and batch use, but this value is marginal given the schema already documents the parameter. 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 action ('Get'), resource ('random cat facts'), quantity ('2-20'), and purpose ('batch use cases, quizzes, or educational content'). It distinguishes from siblings like 'get_cat_fact' (single fact) and 'get_cat_breeds' (breeds) by specifying multiple facts.

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 usage context (batch, quizzes, educational) but does not explicitly state when not to use it or compare with alternatives. It lacks clear guidance on choosing this over 'get_cat_fact' for single fact needs.

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 full burden. It states the operation (fetches random joke filtered by category) but does not add behavioral insights beyond that, such as side effects or rate limits. Adequate for a simple read operation.

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 sentences, zero waste, front-loaded with the verb 'Fetches' and clearly structured: purpose then guidance. 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 single-parameter tool with no output schema, the description is complete: explains function, usage context, and a prerequisite tip. Could briefly describe return format, but not essential.

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 parameter description already listing valid categories. The description adds only example categories, no extra meaning beyond the schema. 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 the tool fetches a random Chuck Norris joke filtered by a specific category, with examples. It distinguishes from sibling tools like get_random_joke (no filter) and search_jokes (search-based).

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?

Explicitly says to use when a themed joke is desired and advises calling get_joke_categories first if unsure of valid categories. Lacks explicit when-not guidance but provides strong context for correct 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?

No annotations are provided, so the description carries full burden. It does not disclose behavioral traits beyond listing categories, such as read-only nature, return format, or error handling. For a simple tool, this is adequate but minimal.

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 sentences, each serving a clear purpose: stating the function and providing usage guidance. No wasted words, front-loaded with the core 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?

Given the tool's simplicity (no params, no output schema), the description is fairly complete with examples and usage guidance. It could mention the return type explicitly, but overall it's 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?

The tool has no parameters, so schema coverage is 100% trivially. The description adds no parameter info, which is appropriate. Per guidelines, 0 params yields baseline 4.

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 lists all available joke categories, provides examples (animal, career, etc.), and distinguishes it from siblings like get_joke_by_category.

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?

Explicitly states when to use this tool ('Use this first when the user wants to know what categories of jokes exist, or before calling get_joke_by_category to validate a category name'), providing clear context and an alternative.

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 adequately discloses behavior: fetches a single random joke from a database of 800+, no destructive effects. It conveys safe read operation.

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 sentences, no redundancy, each sentence adds essential information: what the tool does and when to use 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?

Given no parameters and no output schema, the description fully covers what an agent needs to select and invoke the tool correctly: purpose, source, and usage 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?

Tool has zero parameters, so schema coverage is 100%. Description adds value by mentioning the source and joke count, which aids understanding beyond the empty 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 fetches a single random Chuck Norris joke from a specific API, distinguishing it from siblings like get_joke_by_category or search_jokes by emphasizing 'no specific theme'.

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 advises use for 'quick laugh, random fact-style joke, or filler content with no specific theme requested', implicitly indicating not to use when a specific category or search is 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 provided, so description carries full burden. It states it searches the full database and returns count and several matches, but doesn't mention pagination, limits, or rate limits. Adequate but not exhaustive.

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 sentences, clearly stating purpose and usage context. No 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?

No output schema, but description informs what is returned (count and matches). Given simplicity and no nested objects, 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 coverage is 100% with one parameter. Description adds context that the search is against joke text, beyond the schema's 'keyword to search for within joke text.'

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 jokes by keyword, returns match count and up to several jokes. It distinguishes from siblings like get_random_joke and get_joke_by_category.

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?

Explicitly suggests using this tool when the user wants jokes about a specific word/name/concept rather than a fixed category, providing clear context for when to use.

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 bears full responsibility for behavioral disclosure. It only states that it compares and scores, without explaining side effects (e.g., whether it is read-only), output format, or any destructive actions. The description adds minimal value beyond the obvious.

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 conveys the core purpose without redundancy. However, it is too minimal given the tool's complexity (nested objects, no output schema). Could be restructured to include key details without losing brevity.

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 fails to address many gaps: no details on the two object parameters, no output schema, no behavioral guarantees. Given the lack of schema descriptions and annotations, the description should provide much more context to be usable by an agent.

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 two object parameters ('original', 'candidate') with zero schema descriptions. The tool description does not explain what these parameters represent or their expected structure, leaving agents to guess. With 0% schema description coverage, this is a critical omission.

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 that the tool compares two tool listings and scores copycat overlap. It uses a specific verb ('compare', 'score') and resource ('tool listings'). The purpose is distinguishable from sibling tools like 'fingerprint_listing' or 'scan_slop_listing' which have different actions.

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 no guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. It implicitly suggests use for copycat detection but lacks explicit context or contrast with siblings.

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 must fully disclose behavior. It implies a deterministic output (stable fingerprint) but does not clarify if the action is read-only, mutating, or requires authentication. No discussion of side effects or 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?

A single sentence with a namespace prefix. It is front-loaded and concise, though the phrasing could be more precise. Every word serves a 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?

Given five parameters with no schema descriptions, no output schema, and modest complexity, the description is insufficient. It does not explain what a fingerprint is, how it is returned, or when to use this tool compared to siblings.

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 five input fields (name, description, endpoint, schema words, pricing) but does not precisely map to the schema (e.g., 'pricing' likely maps to feeUsd, 'schema words' may map to toolNames). Ambiguity remains, and no additional meaning is provided.

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 creates a stable fingerprint for a tool listing and lists inputs (name, description, endpoint, schema words, pricing). The verb 'Create' and resource 'fingerprint' are clear, but it does not differentiate from siblings like compare_listings or scan_slop_listing.

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 vs alternatives such as compare_listings or scan_slop_listing. There is no mention of prerequisites, context, 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.

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

With no annotations provided, the description must fully disclose behavioral traits. It states the tool 'Flag[s]...' but does not reveal side effects, processing details, or whether it is read-only or destructive. There is no mention of authentication, rate limits, or what happens to the input data.

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, making it concise. However, it lacks structure and does not front-load key information beyond the tool name prefix. While efficient, it fails to provide necessary detail.

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 5 parameters, no output schema, and no annotations, the description is incomplete. It does not explain how parameters influence behavior, what the output looks like, or when to use the tool. The purpose is clear, but operational context 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?

Schema description coverage is 0%, so the description must add meaning to parameters. The description does not explain any of the 5 parameters (name, description, feeUsd, endpoint, toolNames). It only provides a general purpose without linking to input fields.

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: to 'Flag thin, risky, duplicated, overpriced, or low-trust agent listing copy.' It specifies the verb ('Flag') and resource ('agent listing copy'), and lists specific criteria that differentiate it from sibling tools like 'compare_listings' and 'fingerprint_listing'.

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 lacks guidance on when to use this tool versus alternatives. No explicit when-to-use or when-not-to-use information is provided, and no alternatives are mentioned. The implied usage is for evaluating a single listing, but without context compared to siblings.

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 returns up to 20 drinks with IDs, which is good behavioral info. However, it does not mention potential side effects, rate limits, or what happens if no results are found. The description adds value beyond the name 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?

The description is very concise: two short sentences that front-load the purpose and key details (result limit, ID usage). Every sentence earns its place with no 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?

Given the low complexity (one required parameter, no output schema), the description is fairly complete. It covers the what, the filter, the result limit, and the purpose of IDs. It could mention that IDs are meant for use with get_cocktail_by_id, but that is implied. Overall, it provides sufficient context for this simple 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 100% with an enum description. The schema already defines the parameter 'type' with values 'alcoholic' and 'non_alcoholic'. The tool description reinforces this by mentioning 'alcoholic or non-alcoholic cocktails' but does not add new semantic information beyond what the schema provides.

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: to get a list of alcoholic or non-alcoholic cocktails. It uses a specific verb ('Get'), identifies the resource ('cocktails'), and specifies the filter. It also mentions the output limit and the fact that IDs are for further lookup, which distinguishes it from sibling tools like random_cocktail or get_cocktail_by_id.

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 filtering by alcoholic type, but it does not explicitly state when to use this tool versus alternatives (e.g., search_cocktail or filter_by_ingredient). No when-not-to-use guidance or comparison with other cocktail tools is 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?

Without annotations, the description carries full burden. It states return values (names and IDs) but does not disclose sorting, pagination, case sensitivity, or handling of missing ingredients. Basic transparency but incomplete.

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 sentences: the first explains purpose and output, the second gives a practical example. No wasted words, front-loaded.

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 filter tool with no output schema, the description adequately mentions return values and use case. Could add behavior for no results or large result sets, 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 coverage is 100% with a detailed parameter description and examples. The tool description does not add significant extra meaning beyond 'specific ingredient', so baseline 3 applies.

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 finds all cocktails containing a specific ingredient, returning names and IDs. It distinguishes from siblings like filter_by_alcoholic and search_cocktail by focusing on ingredient-based filtering.

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 an example query ('what can I make with vodka?') implying usage, but does not explicitly state when to use this over alternatives like search_cocktail or filter_by_alcoholic. It lacks when-not or alternative tool mentions.

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. The description only states it gets a full recipe, with no mention of potential errors, return behavior, or side effects. For a simple read operation, this is adequate but not comprehensive. No contradiction with annotations since none exist.

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: first states purpose, second provides usage guidance. No 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?

For a simple get-by-id tool with no output schema, the description adequately conveys the operation and usage context. It could mention the return format explicitly, but the phrase 'full recipe' is fairly intuitive.

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% and the description adds no new information about the 'id' parameter beyond what is in the schema (e.g., example value). With high schema coverage, 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 'Get full recipe for a specific cocktail by its TheCocktailDB ID', specifying the verb (get), resource (full recipe), and identifier type. It also distinguishes from siblings by suggesting it be used after filtering tools.

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?

Explicitly says 'Use after filter_by_ingredient or filter_by_alcoholic to get full details', providing clear context on when to use this tool vs alternatives like search or filter.

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?

Simple read operation with no annotations. Description clearly states it returns a cocktail recipe, no side effects implied. Adequate for a trivial tool.

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 short sentences: one stating purpose, one for use case. No unnecessary words, front-loaded with key 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?

For a parameterless random tool with no output schema, the description fully covers what the tool does and when to use it. No missing information.

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; input schema is empty. Description need not add parameter info. Baseline score of 4 for zero parameters.

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 states 'Get a completely random cocktail recipe' with clear verb and resource, and the 'random' aspect distinguishes it from sibling tools like filter_by_alcoholic, search_cocktail, etc.

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?

Explicitly says 'Great for discovering new drinks or giving users a surprise recommendation', indicating when to use. Does not explicitly state when not to use, but the context is clear given the sibling tools.

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 but only states basic behavior (returns up to 10 results with full recipes). It does not disclose edge cases like no matches, partial matches, rate limits, or read-only nature. The 'search' verb implies read, but not explicitly.

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, with purpose front-loaded in the first sentence. The prefix '[cocktail-intel]' is somewhat redundant given the tool name but does not hinder clarity. Examples are appended 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 one parameter and no output schema, the description adequately covers what the tool does and returns. Missing information like error handling or read-only nature would be nice but not critical for such a straightforward 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?

The single parameter 'query' has 100% schema coverage with examples. The description adds more example values but does not add new semantics or constraints beyond the schema. 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 the tool searches for cocktail recipes by name, listing what is returned (up to 10 matching cocktails with full recipes). It is specific but does not explicitly differentiate from sibling tools like filter_by_ingredient or filter_by_alcoholic, though the name and description imply a name 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 provides examples but offers no guidance on when to use this tool versus alternatives. It does not mention scenarios like 'use this if you know the cocktail name' or refer to sibling tools for filtering by ingredient or random selection.

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 rate limit (60 requests/hour per IP) which is key behavioral info. Without annotations, description carries full burden, and it adequately describes the fetch as a read operation. Could mention idempotency or authentication, but overall good.

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?

Three sentences: purpose, usage guidance, rate limit. Front-loaded with action and resource. No 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?

Lists fields returned (stars, forks, etc.), which compensates for lack of output schema. Also covers rate limit. For a simple lookup, this is 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?

Schema already covers parameter descriptions with 100% coverage. The description mentions owner and repo name but adds no new detail beyond what's in the schema, so baseline of 3 applies.

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 detailed metadata for a specific repository given its owner and repo name, listing fields like stars, forks, etc. It distinguishes from siblings (search, top repos) by specifying 'one specific repository' when you know the exact pair.

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 explicit guidance to use when the exact owner/repo pair is known. Mentions rate limit. Could explicitly state alternatives (search, top repos), but context from siblings makes it 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?

Discloses the proxy metric (most-starred), data source (public search endpoint), and rate limit. No annotations provided, so the description carries full burden. It is transparent about limitations and methodology.

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 paragraph, front-loaded with purpose, followed by default and rate limit info. Could be slightly more concise, but no extraneous 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?

No output schema exists, and the description does not detail what fields are returned for each repository (e.g., name, stars, URL). While the purpose is clear, an agent may need to know the shape of the output to use the results effectively.

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 100% (both limit and since have descriptions). The description only adds the rate limit context, which is not parameter-specific. It repeats the default for since, which is already in 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 'Return the most-starred repositories created after a given date' and explains its role as a proxy for trending because no official unauthenticated trending endpoint exists. This distinguishes it from sibling tools like get_repository_details and search_repositories.

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 default behavior (30 days if no date), rate limit info (~10 req/min per IP), and context for why this tool exists (lack of official trending endpoint). Does not explicitly state when not to use or compare to alternatives, but implications are 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?

No annotations exist, so the description must fully disclose behavior. It states the API is unauthenticated, rate-limited (~10 req/min per IP), and may return 429 under heavy use. Results are ranked by star count. This is good disclosure, though missing details like response structure or pagination behavior.

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 efficient sentences: purpose, use case, and limitation. Each sentence earns its place, is front-loaded with key action, and avoids verbosity. Perfectly concise for the tool's complexity.

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 no output schema and no annotations, the description should outline return format and error behavior. It covers rate limiting and ranking, but omits response structure (e.g., what fields like 'name', 'description', 'stars' are returned) and whether pagination exists beyond the limit parameter. Adequate but not fully 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?

Input schema has 100% coverage with examples and descriptions. The description adds context about qualifiers and ranking, but does not add critical meaning beyond the schema. Baseline 3 is appropriate as the description provides modest extra value.

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 public code repositories with keywords, topics, or qualifiers, and ranks by star count. It provides specific qualifier examples. While it doesn't explicitly differentiate from siblings 'get_repository_details' and 'get_top_new_repos', the purpose is unambiguous and distinct.

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 suggests use for 'finding popular projects in a given ecosystem or niche', which implies appropriate contexts. The rate limit warning indirectly advises against bulk automated use. However, it lacks explicit when-not-to-use guidance or comparisons with sibling tools, but is clear enough for most cases.

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 details the output: all formats, human description, contrast ratios, properties. It does not mention any side effects, which is appropriate for a read-only analysis. It could mention that it doesn't modify anything, but it's implicit.

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-loading the purpose and then detailing input formats. Every word adds value; no 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?

For a simple tool with one parameter and no output schema, the description thoroughly covers what the tool does and what it returns, making it complete for an agent to decide when to use 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?

The description adds significant context to the single parameter 'color' with examples (hex #FF5733, CSS name coral, rgb, hsl), going beyond the schema's description. This helps the agent understand acceptable formats.

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 analyzes a color and lists all outputs: all color formats, human description, WCAG contrast ratios, and color properties. It distinguishes itself from sibling tools like check_contrast and generate_palette by being a comprehensive analysis.

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 specifies input formats (hex, CSS name, rgb, hsl) implying it can handle various color specifications. However, it does not explicitly state when to use this tool versus alternatives, but the distinction is clear from the comprehensive output.

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 the output format (contrast ratio and pass/fail thresholds) and mentions the ratio range. However, it does not mention error handling for invalid colors, rate limits, or other behavioral aspects that would be expected without annotations.

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 (two sentences) with no fluff. It front-loads the essential purpose and clearly explains outputs, making it 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?

Given the tool's simplicity (2 required params, no output schema, no nested objects), the description fully covers what the tool does and what it returns. No additional information is needed for an AI agent to use it correctly.

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 covers 100% of parameters with descriptions. The tool description adds meaningful context by specifying that color1 is foreground/text and color2 is background, which aids in correct usage beyond the schema's basic type/format info.

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 checks WCAG accessibility contrast ratio between two colors for web accessibility. It specifies the output includes contrast ratio range and pass/fail for AA and AAA standards. This distinguishes it from sibling tools like color-intel__analyze_color and color-intel__generate_palette.

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 web accessibility but does not explicitly state when to use this tool versus alternatives, nor does it provide when-not-to-use guidance. It lacks explicit usage guidelines beyond the general purpose.

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 fully disclose behavior. It explains palette types but does not mention return format, side effects, or error conditions. The agent lacks information about what the output looks like.

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 one sentence plus a compact list of six palette types with brief explanations. No unnecessary words; efficient and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.