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

No annotations provided; description carries full burden. It mentions pricing per endpoint and global coverage but does not disclose error behavior, rate limits, authentication needs, 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 lengthy with a bullet list of endpoints and prices. It is somewhat structured but contains redundancy (e.g., 'Coverage: Global') and could be more concise.

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 20 parameters, the description does not explain what the tool returns or how parameters like 'min_tvl' filter results. It is incomplete for effective tool selection.

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 descriptions, but those descriptions are minimal (e.g., 'lang' is just 'lang'). The tool description adds context for the 'action' parameter listing endpoints, but other parameters lack additional meaning beyond names.

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 is a global alternative trading intelligence API with multiple endpoints (discover, signals, ea, etc.), distinguishing it from sibling 'pulse' tools by specifying its domain.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus other 'pulse' tools. It lists endpoints but does not provide criteria for selection or when to avoid.

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 such as auth requirements, rate limits, or side effects. The 'Execution-tier' hint is vague about costs but insufficient.

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 structured as a list of endpoints with prices, which is clear but verbose. It front-loads the purpose, but the endpoint list could be condensed or supplemented with usage examples.

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 46 parameters and 12 endpoints without output schema or mapping from params to actions, the description is incomplete. It does not explain how to construct valid requests for specific endpoints.

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?

Although schema coverage is 100%, most parameter descriptions are tautological (e.g., 'days', 'pair') adding no meaning. Only 'action' and 'lang' have meaningful descriptions. The tool description does not compensate by mapping parameters to endpoints.

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 scans arbitrage opportunities across multiple domains (DeFi, DEX, perpetuals, sports, ETF, commodities). However, it does not differentiate from sibling tools like cryptopulse or alphapulse, which may also have similar structures.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. The description only lists endpoints without context on selection 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, so the description must convey behavioral traits. It discloses endpoint costs and global coverage but omits details on idempotency, rate limits, data freshness, or side effects. The listing of endpoints and costs offers moderate 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 verbose, including cost per call for each endpoint which is not essential for tool selection. It is structured with bullet points and front-loads key info (name, coverage), but could be more concise by omitting pricing 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 complexity (20 parameters, 10 endpoints), the description provides a good overview of capabilities and parameter-action mapping. No output schema exists, but the endpoints are straightforward lookups. The description covers scope sufficiently, though response format is not mentioned.

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 individual parameter descriptions. The description adds context by mapping endpoints (action enum) to use cases (e.g., recall for safety recalls). This helps understand which parameters apply to which actions, adding value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as an automotive intelligence API with 10 specific endpoints covering recalls, problems, repair, comparison, etc. It distinguishes from siblings by focusing on automotive data (NHTSA, EPA, market). However, the cost-per-call detail is extraneous for purpose clarity.

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 lists endpoints but does not explicitly state when to use autopulse over sibling pulse tools. The 'automotive' focus implies domain, but no direct comparison or exclusions are provided.

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

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

With no annotations, the description carries the full burden. It discloses pricing per endpoint and global coverage, but omits critical behavioral aspects like output format, authentication requirements, rate limits, error handling, or whether the tool writes data. The 'data synthesis' phrasing hints at read-only aggregation but is not explicit.

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 organized as a bulleted list, which aids scanning, but the first line is a long run-on sentence and the text is truncated mid-sentence ('endangered s'). It front-loads the main purpose but lacks polish 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?

For a tool with 11 parameters, 9 endpoints, and no output schema, the description is insufficient. It lists endpoints but doesn't explain required parameters per action, sample requests, or response structure. An agent would struggle to invoke the tool correctly 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 100% (all params have descriptions), but several descriptions are trivial (e.g., 'lat', 'lng', 'lang'). The description adds endpoint cost information but does not map endpoints to required parameters or explain how to use specific params per action. Baseline 3 is appropriate since the schema covers all params.

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 as a global biodiversity intelligence API, synthesizing GBIF, IUCN, eBird, and iNaturalist data. It lists specific endpoints like species profiles, sighting reports, and migration tracking, which distinguishes it from sibling tools in other domains.

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 biodiversity-related queries through examples like species profiles and sighting reports, but it does not explicitly state when to use this tool versus alternatives or exclude other cases. No alternative tools are named.

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 does disclose per-endpoint pricing and mentions geographic coverage ('Global'), but it does not state whether operations are read-only, whether any writes or side effects occur, or describe error/return behavior. The truncation 'US-focused wi' adds ambiguity about geographic scope. Overall, limited behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably structured: a one-line purpose followed by a list of endpoints with prices. However, it is longer than necessary and includes repetitive endpoint paths. The disjointed 'US-focused wi' and 'Coverage: Global' lines reduce clarity. It earns a middle score for being organized but not tight.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 10 distinct actions and 15 parameters, but the description provides no per-action parameter requirements, no output schema, and no example usage. The endpoint list tells you what actions exist but not how to construct a valid request for each. This is a complex tool, and the description is insufficient for an agent to invoke it correctly without additional speculation.

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?

Although schema description coverage is marked 100%, the schema descriptions are merely the parameter names (e.g., 'zip', 'city'), providing no real meaning. The tool description lists actions but does not map which of the 14 non-action parameters are relevant to each action. The 'action' parameter is described via its enum, but other parameters lack contextual explanation.

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 domain: 'home construction and renovation intelligence' and lists specific capabilities (cost estimates, contractor vetting, permits, materials, ROI, inspections). This distinguishes it from unrelated sibling tools like 'cryptopulse' or 'climatepulse.' However, the purpose is somewhat broad across many sub-actions, so it doesn't fully narrow down a single primary use case.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool over alternatives. The description lists endpoints and prices but does not explain which actions are appropriate for a given scenario or mention any exclusions. There is no reference to sibling tools or comparative use 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?

No annotations are provided, so the description must carry the full burden. It mentions pricing per endpoint but does not disclose other behavioral traits such as authentication requirements, rate limits, data freshness, or what happens on invalid inputs.

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 somewhat verbose, listing all endpoints with pricing, but it uses bullet points and front-loads the general purpose. It could be more concise by omitting redundant details, but it is structured enough for scanning.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 18 parameters and 10 actions with no output schema, but the description does not provide usage patterns, examples, or expected responses. The parameter descriptions in the schema are minimal, and the description fails to compensate, leaving significant gaps.

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?

Although schema coverage is 100%, many parameter descriptions are tautological (e.g., 'to', 'from', 'current'). The tool description does not add any further meaning or context for parameters, leaving the agent with minimal guidance beyond parameter names.

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 is a 'Global career intelligence API' and lists 10 specific endpoints (salary, outlook, etc.), making the purpose well-defined. However, it does not differentiate from the many sibling 'pulse' tools, relying on the 'career' context.

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. The description lists endpoints but does not explain how to choose between them or give any usage conditions, exclusions, or prerequisites.

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 full behavioral disclosure. It does reveal coverage sources and pricing per endpoint, which is useful, but it omits critical behaviors like authentication requirements, rate limits, response formats, pagination, or what kind of data is returned (citations vs. summaries). The description is truncated mid-sentence ('Euro...'), further reducing 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 organized with a clear overview, coverage line, and bulleted endpoints with pricing, which helps navigation. However, it includes a truncated phrase and mixes pricing details with functional descriptions, making it longer than necessary. The front-loaded purpose is good, but the incomplete sentence and redundancy reduce structural quality.

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 18 parameters, 9 endpoints, and no output schema, the description is insufficient for an agent to correctly invoke the tool. It fails to explain endpoint-specific parameters, expected outputs, or examples, and does not clarify how the endpoints relate to the overall search capability. The one-line endpoint descriptions are too brief to guide 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?

The schema has 18 parameters with descriptions, so coverage is 100%, but these descriptions are mostly tautological ('era'->'era', 'lang'->'lang'). The tool description lists endpoints but does not map parameters to specific actions or explain which fields are required for each endpoint, leaving the agent without guidance on how to construct queries for person, immigration, or timeline research.

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 defines the tool as a 'Global genealogy and historical archive intelligence API' and specifies full-text search across known sources (Chronicling America, Library of Congress, Trove, British Newspaper Archive). It lists distinct endpoints (search, person, obituary, event, place, immigration, military, business, timeline) which distinguishes it from sibling *pulse tools by its historical/genealogical domain.

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 for genealogy and historical research by naming the sources and endpoint types, but it does not explicitly state when to use this tool over the many sibling pulse tools or when not to use it. There are no alternatives mentioned or exclusion criteria, leaving the agent to infer applicability from the domain context.

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

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

No annotations are provided, so the description carries the full burden. It discloses pricing per endpoint (e.g., '$0.15'), which is useful. However, it does not explain authentication, rate limits, data sources, or what happens on error. The behavior of each endpoint is only implied by its name (e.g., 'search', 'hospital'). Moderate 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 relatively concise, with a brief opening sentence followed by a clear bullet list of endpoints and costs. However, the opening sentence is a bit run-on and could be split. Overall, it is efficient and 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?

The tool has 20 parameters and 8 endpoints, but the description does not explain which parameters are required for each endpoint (e.g., 'procedure' needed for 'search', 'hospital' for 'hospital'). The schema alone, without per-endpoint guidance, leaves the agent to infer the correct parameter combinations, which is risky. Significant gaps for a complex 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% with each parameter having a concise description (e.g., 'zip': 'Patient zip code for geographic search'). The tool description does not add further parameter context beyond listing endpoints. Thus, the schema already provides adequate semantics, meeting the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is a 'Healthcare price transparency and cost navigation API' and lists specific endpoints. This distinguishes it from sibling tools which have different domain topics, so an agent can easily identify it for healthcare cost queries.

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 healthcare cost navigation, but does not explicitly state when to use this tool versus siblings (though domain is clear). Within the tool, it lists endpoints but provides no guidance on which endpoint to choose for a given task, relying on the agent to infer from endpoint names. Slight gap in actionable 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 full weight. It discloses the API source (Open-Meteo), AI synthesis, and global coverage, but fails to mention key behaviors like authentication, rate limits, data freshness, error handling, or any side effects. The description is largely a list of endpoints with pricing, not behavioral traits.

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 reasonably structured with a clear intro, coverage note, and bulleted endpoints. However, it is somewhat verbose with pricing details that could be optional or placed elsewhere. Minor redundancy with the schema enum.

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 (11 parameters, 9 actions) and lack of output schema or annotations, the description is insufficient. It does not explain how to choose between endpoints, what responses look like, or error conditions. Users are left to infer from parameter names alone.

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 schema already documents all parameters adequately. The description adds some context (pricing, global coverage) but no parameter-specific guidance 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 it's a global climate and weather intelligence API with specific endpoints, making the purpose evident. However, it does not distinguish from many sibling pulse tools (e.g., harvestpulse, waterpulse) that may have overlapping functionality.

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 climate/weather queries but provides no explicit guidance on when to use this tool versus alternatives, nor does it suggest exclusions or prerequisites. The listed endpoints offer some context but lack situational recommendations.

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

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

With no annotations provided, the description carries the full burden. It discloses data sources (ClinicalTrials.gov, FDA OpenFDA, PubMed, real-time news) and endpoint pricing, which adds valuable context. However, it does not mention rate limits, authentication details, or whether operations are destructive, so some gaps remain.

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 reasonably concise given the amount of information, starting with the core purpose and then listing endpoints. The list format makes it scannable. Minor redundancy (e.g., coverage mention) could be trimmed, but overall it is well-structured.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 16 parameters and no output schema, the description covers the high-level purpose and endpoint options but lacks guidance on how parameters combine or which parameters are relevant for each endpoint. It provides enough for simple use but misses deeper context needed for complex queries.

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?

Although schema coverage is 100%, many parameter descriptions are minimal (e.g., 'depth', 'focus', 'stage' are just the parameter name). The description does not elaborate on these generic parameters or clarify how they interact with different endpoints. While some parameters like nct_id and sponsor have examples in the schema, the description adds no extra meaning beyond what's 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 it is a 'Pharmaceutical pipeline and clinical trial intelligence API' and lists 10 specific endpoints with brief descriptions, making the tool's purpose immediately clear and distinguishing it from sibling tools that cover other domains.

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 list of endpoints with their intended uses (e.g., 'Phase 2/3 pipeline scan') and mentions a payment requirement ('All endpoints require x402 pay'). However, it does not explicitly state when not to use this tool or compare it to alternatives, which would be helpful given the many 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 the transparency burden. It usefully discloses that valuations are 'AI-synthesized', the per-endpoint pricing, and the on-chain GoPlus NFT context. However, it does not state whether the tool is read-only, what output shape to expect, rate limits, data freshness, or potential variability in results.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: it starts with the tool's purpose, then a coverage line, then a clear bulleted endpoint list. It is reasonably sized for an API with 11 endpoints, though the truncated sentence 'Real-' and the repeated pricing labels add slight noise. Overall, it is front-loaded and organized well.

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 10 parameters, 11 endpoints, and no output schema or annotations, the endpoint list provides a useful overview and helps an agent pick an action. However, it lacks per-endpoint required parameter information, return format descriptions, language/chain constraints, and operational notes, so an agent may need trial and error to invoke it correctly in some cases.

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 endpoint list adds meaningful semantics to the 'action' parameter by describing each endpoint's purpose. However, some parameter descriptions in the schema are tautological (e.g., 'lang', 'item1', 'item2'), and the tool description does not clarify which parameters are needed for which endpoints or provide additional parameter-specific details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as 'Global collectibles market intelligence API' with 'AI-synthesized valuations' for a specific domain, and lists 11 distinct endpoints with concrete actions (value, grade, authenticate, etc.). This strongly differentiates it from sibling tools, which are named as other domain-specific pulse 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 endpoint list gives clear context about what the tool can do and implies when to use it (collectibles valuation/investigation), but it does not explicitly state when to use this tool over alternatives or when not to use it. There is no guidance on which endpoint to choose beyond the brief one-line descriptions, nor any 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 carries the full burden. It fails to disclose behavioral traits such as read-only nature, authentication requirements, rate limits, or side effects. The only unusual detail is pricing, but it does not clarify if the tool fetches external data or what the return format is.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured but verbose, including marketing-like pricing and coverage information. It could be more concise by focusing on essential usage guidance. The length is acceptable but not optimally lean.

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 (11 parameters, no output schema, no annotations), the description is moderately complete. It lists endpoints and provides some context, but lacks guidance on parameter combinations, return values, or how to effectively use the tool beyond basic endpoint selection.

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 input schema already documents each parameter. The tool description adds marginal value by listing endpoints and their purposes, which maps to the 'action' parameter, but it does not provide additional meaning beyond the schema's 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 it is a 'Global regulatory intelligence API' with 8 specific endpoints (privacy, kyc, corporate, etc.), each with a brief purpose (e.g., 'Data privacy law by jurisdiction'). This provides a clear verb+resource. However, it does not explicitly differentiate from sibling tools beyond the general domain of compliance.

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 does not provide when-to-use or when-not-to-use guidance relative to siblings. It lists endpoints but does not explain which scenarios are best for this tool vs. other specialized pulse tools (e.g., cyberpulse, esgpulse). No alternatives 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?

With no annotations, the description should disclose behavior. It mentions endpoints and pricing but does not state that the tool is read-only, lacks side effects, or has rate limits. The description fails to provide essential behavioral context beyond a list of features.

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 overly long and unstructured, mixing marketing language, pricing, and endpoint lists. It is not concise; important functional details are buried in verbose text. The structure does not front-load key information for an agent.

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 22 parameters, 11 actions, and no output schema, the description is incomplete. It lists endpoints but does not explain what each action returns, how parameters relate to actions, or provide usage examples. The agent lacks sufficient context to 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?

Schema description coverage is 100%, but many parameter descriptions are mere labels (e.g., 'goal', 'profile') adding no meaning. The action parameter has a clear description with enum values. The description adds context for endpoints but not for individual parameters, so it marginally improves understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states it is a 'Global cryptocurrency intelligence API' and lists endpoints, which gives a general idea. However, it does not concisely state that the tool retrieves intelligence data based on an action parameter, and the inclusion of pricing and marketing language blurs the purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus its many siblings (e.g., alphapulse, marketpulse). There is no advice on selecting actions or prerequisites, making it difficult for an agent to decide when to invoke this tool.

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

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

No annotations are provided, so the description carries the burden of disclosure. It adds cost per endpoint (e.g., $0.10 for cve-brief) and notes 'authorized defensive use' for osint and attack-surface, implying authorization requirements. However, it does not discuss rate limits, authentication, or side effects. The behavior is somewhat transparent 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 quite long (over 20 lines) and structured as a bullet list with costs and each endpoint's function. It is not front-loaded; the main purpose is stated but followed by a more detailed list. It could be more concise by combining endpoint descriptions into a more succinct format.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (18 parameters, 10 endpoints, no output schema), the description covers each endpoint's purpose and provides examples for parameters. However, it lacks information on return values, error handling, or usage examples. It is moderately complete but leaves gaps.

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% (context signals), so the baseline is 3. The description adds value by providing detailed endpoint descriptions (e.g., 'CVE deep-dive — CVSS, exploitation status, patch urgency, remediation') that clarify the action parameter and give context for other parameters like cve, vendor, etc. This extra information helps an agent understand parameter usage beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it's a global cybersecurity intelligence API covering CVE briefs, vulnerability scanning, CISA KEV, OSINT, threat intelligence, ransomware tracking, breach checks, compliance gap analysis, dark web monitoring, and attack surface assessment. It lists specific endpoints with short descriptions, making the purpose clear. However, it lacks a concise one-line summary and includes trailing text.

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?

There is no explicit guidance on when to use this tool versus sibling tools (e.g., alphapulse, arbipulse) or alternatives. The description does not mention prerequisites, when not to use, or how to choose between endpoints beyond the action parameter. Implicitly, it's for cybersecurity intelligence, but no clear usage boundaries 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?

The description discloses per-endpoint costs (e.g., $0.05 per store call), which is a useful behavioral trait, but it does not mention authentication, rate limits, or whether operations are read-only. Given no annotations, the description carries the burden but only partially fulfills it.

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 contains a cut-off sentence and redundant phrasing ('DealPulse:'), but the bullet list of endpoints is well-structured. It is not maximally concise, but organized enough for scanning.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of 11 parameters and multiple endpoints, the description lacks guidance on which parameters to use with each action, and does not explain return values (no output schema). This leaves significant gaps for effective tool 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?

The input schema has 100% description coverage, so the description adds little beyond listing endpoints and their costs. The baseline of 3 is appropriate as the schema already documents all 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?

The description clearly states it is a 'Global deal intelligence API' and lists endpoints for best deals, price history, coupons, cashback, etc., establishing a distinct domain. However, the description is cut off and somewhat messy, slightly reducing clarity.

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 its siblings (other pulse tools). The description lacks context on selecting this tool over alternatives, leaving the agent without decision support.

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 payment mechanism (x402, USDC on Base mainnet, PAYMENT-SIGNATURE header), pricing per endpoint, supported jurisdictions, and language support. However, it does not disclose return format, error behavior, side effects, or whether the tool makes external calls. The mention of 'intelligence' and endpoint names like 'playbook' gives some idea, but not enough.

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 long (21 bullet points) but structured and front-loaded with payment and jurisdiction info. However, it contains a truncated sentence ('Add ?lang= for a') and redundancy ('Coverage: Global' contradicts the specific country list). While each bullet is informative, the overall verbosity and incomplete sentence detract from 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?

This is a complex tool with 39 parameters and no output schema. The description lists endpoints and prices but does not explain how to use the parameters for each action, what the response structure looks like, or how errors are handled. For such a multi-action tool, the description is insufficient for an agent to correctly invoke all endpoints without additional inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% description coverage, but many descriptions are tautological (e.g., 'goal': 'goal', 'type': 'type'), providing little meaning. The tool description adds minimal parameter context: it mentions '?lang=' for language and the endpoint bullet names imply relevant parameters (e.g., payoff calculator likely uses 'debts', 'balance'), but it does not explicitly map parameters to endpoints. Baseline of 3 applies due to high schema coverage, and the description does not significantly elevate it.

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 domain ('Global debt elimination intelligence') and enumerates 21 specific endpoints (e.g., payoff, snapshot, negotiate), each with a one-line purpose like 'Payoff calculator' or 'Debt burden snapshot'. This gives a specific verb+resource for each action, but it doesn't explicitly distinguish the tool from sibling 'pulse' tools beyond the debt focus.

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 important context: all endpoints require x402 payment via PAYMENT-SIGNATURE header, and jurisdictions are limited to US, UK, Australia, and Canada. The endpoint list implies usage scenarios (e.g., use 'payoff' for payoff calculations), but there is no explicit guidance on when to use this tool versus alternatives, nor any exclusions or comparison to 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 provided, so description carries full burden. It discloses that the tool returns a categorized list with specific details (descriptions, coverage, pricing, actions), which is sufficient for a read-only discovery tool. No side effects or auth requirements mentioned, but not critical here.

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 packed with relevant information. No unnecessary words. Front-loaded with the main 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?

Despite no output schema, the description fully compensates by detailing the return content. For a simple optional-parameter tool, it provides enough context for an agent to understand the tool's purpose and 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?

Schema coverage is 100%, so baseline is 3. Description does not add additional meaning beyond the schema for the 'category' parameter; the schema already describes the filter values. No extra semantic 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?

Description clearly states the tool's purpose: discovering all PulseNetwork verticals. It specifies the return content (categorized list of 67 APIs, 660+ endpoints, descriptions, coverage, pricing, actions) and distinguishes from sibling tools which are individual verticals.

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 to find the right vertical for a task,' indicating when to use it. While it doesn't directly state when not to use it, the context of sibling tools implies that if a specific vertical is known, those tools should be used instead.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It does add value by disclosing per-endpoint pricing, which is useful operational context. However, it does not describe response formats, latency, data freshness, error behaviors, or whether this is a read-only API. The truncated first sentence also leaves the actual output behavior unclear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: an opening summary, a coverage statement, and a bulleted endpoint list with prices. The list is easy to scan and the pricing information is relevant. However, the opening sentence is cut off, and the endpoint list somewhat duplicates the action enum in the schema, making it slightly longer than necessary.

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?

This is a complex tool with 10 parameters and 10 distinct actions, no output schema, and no annotations. The description provides the endpoint catalog and pricing but fails to explain which endpoints require which parameters (e.g., iso2 vs iso3), what kind of data each returns, or how to compose a valid request. Critical usage context is missing, leaving the agent to guess.

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 schema already explains parameters like iso2 (World Bank) and iso3 (IMF). The description's endpoint list adds useful context for the action enum, but it does not explain how parameters like period, lens, focus, or regime should be used per endpoint, nor which parameters are required for specific actions. Thus it neither adds nor detracts beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as an economic intelligence API covering 190+ countries with specific data sources (World Bank, IMF, satellite nightlights, AIS shipping). It lists ten distinct endpoints, which helps differentiate this from the many other 'pulse' sibling tools. However, the first sentence is truncated ('to produce institu...') and it lacks a strong action verb, so it stops just short of a 5.

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 explicit guidance on when to use this tool versus alternatives, nor does it specify which endpoint to choose for a given scenario. It mentions 'Alternative economic intelligence' which implies a use case, but there are no exclusions, prerequisites, or decision criteria. The endpoint list is a catalog, not usage 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 must describe behavior. It mentions pricing per endpoint and vague capabilities (e.g., 'pinpoints knowledge gaps'), but omits critical details like whether operations are read-only, destructive, or require authorization. Behavioral traits are inadequately disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is overly long, listing 14 endpoints with pricing. It is not front-loaded with a clear summary, and the bullet list includes redundant entries (explain appears twice). The structure is verbose without being informative.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (29 parameters, 14 actions), the description is grossly incomplete. It lacks guidance on required combinations, output format, usage constraints, and error scenarios. The agent would be unable to invoke the tool correctly based on this description alone.

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?

Despite 100% schema description coverage, the parameter descriptions are tautological (e.g., 'count' description is 'count', 'difficulty' is 'difficulty'). The lengthy tool description fails to map parameters to specific actions, leaving the agent unable to determine which parameters to use for each endpoint.

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 mentions 'Global education intelligence API' and lists 14 endpoints, but does not clearly state what the tool as a whole accomplishes. The overarching purpose is vague, and the list of endpoints reads like a catalog rather than a coherent tool definition.

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?

There is no guidance on when to use this tool versus its siblings (all 'pulse' tools). The description does not provide context for appropriate usage or mention 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 the description must disclose behavior. It only lists endpoints and prices, but does not describe side effects, authorization needs, rate limits, or whether operations are read-only/destructive. Insufficient 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 verbose, includes pricing details that may be unnecessary, and appears truncated ('All end'). It is not front-loaded or concise.

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 22 parameters and no output schema, the description does not explain how endpoints relate, what output format to expect, or how to choose between endpoints for a given task. The tool is complex but the description is incomplete.

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 100% schema description coverage (all 22 parameters described in JSON), baseline is 3. The description does not add extra meaning beyond the endpoint list; parameter details are 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 identifies the tool as AI-powered ESG intelligence, listing specific endpoints (CSRD, taxonomy, etc.) that distinguish it from sibling pulse tools. However, the truncation 'All end' and mixed pricing details slightly reduce clarity.

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?

Endpoint names imply usage scenarios (e.g., csrd for compliance roadmaps), but no explicit guidance on when to use this tool versus alternatives, or when not to use it. The description lacks when/when-not instructions.

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

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It adds useful context: outputs are 'AI-synthesized', coverage is 'Global', and per-endpoint pricing is given. However, it omits side effects, error handling, rate limits, authentication needs, or any caveats about result quality, leaving an incomplete behavioral profile.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: a short intro, coverage line, and a bulleted endpoint list with prices. It is information-dense but not overly verbose, and the front-loaded intro establishes the tool's purpose quickly. The pricing details are extra but not distracting.

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 16 parameters and 9 endpoints, the description fails to connect parameters to actions. For example, it does not state which parameters are needed for 'lore' versus 'compare'. There is also no output schema to fall back on, and the description doesn't mention return format or limitations. This is a critical gap for correctly invoking the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% (each of the 16 parameters has a description field), satisfying the high-coverage baseline. However, those schema descriptions are mostly tautological (e.g., 'item', 'lang') and the tool description itself does not explain what parameters mean or how they map to specific endpoints. The description adds no value beyond the schema, so the 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 identifies the tool as a 'Global fandom intelligence API' and lists specific capabilities (lore analysis, collectibles valuation, discography deep-dives, etc.). The resource (fandom/entertainment) and actions are specific, making the tool's purpose unambiguous and distinct from generic APIs.

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 its many siblings (e.g., franchisepulse, collectablespulse, gamepulse). It neither mentions alternatives nor states exclusions or prerequisites, leaving the agent to infer the appropriate use case from the domain 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 provided, so the description must fully disclose behavior. It fails to mention authentication, rate limits, pricing model (though costs per call are noted), error handling, or idempotency. The action is presumably read-only 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?

The description is structured with a clear opening sentence, coverage note, and bulleted endpoints. It is front-loaded with the core purpose. However, it is slightly verbose with redundant 'intelligence' phrasing and could be trimmed.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (8 parameters, 10 endpoints, no output schema), the description is insufficient. It does not clarify when to use lat/lon vs region, what data each endpoint returns, or prerequisites (e.g., some endpoints may require specific parameters).

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 marginal value by explaining each endpoint's purpose (e.g., 'ET0-based irrigation recommendation' for irrigation) but does not elaborate on parameter usage 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 tool as a global precision agriculture intelligence API synthesizing satellite NDVI, soil/weather, and market data. It lists specific endpoints (yield-forecast, weather-risk, etc.) with brief purposes, distinguishing it from siblings like marketpulse or alphapulse which serve other domains.

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 explicit guidance on when to use this tool vs alternatives. It lists endpoints but does not provide conditional logic (e.g., 'use yield-forecast for production estimates, weather-risk for short-term hazards'). No when/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, and the description fails to disclose any behavioral traits such as rate limits, authentication, destructive actions, or side effects. It includes pricing but no operational details.

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 overly long and includes extraneous information such as pricing per endpoint, making it cluttered. It is not front-loaded with the most important information; an AI agent would have to parse through marketing text.

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 no output schema and 13 parameters, the description fails to explain how endpoints relate, what inputs are expected for each action, or what the return values are. It is incomplete for an AI agent to use 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?

Although schema coverage is 100%, many parameter descriptions are mere repetitions of the parameter name (e.g., 'ticker1', 'company1'). Only a few parameters like 'event' and 'action' have meaningful descriptions. The description adds minimal value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description positions the tool as an API for SEC/EDGAR filings with multiple endpoints, but it lacks a single clear verb-resource statement. It reads like a product description rather than defining a specific action for an AI agent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus the many sibling 'pulse' tools. The description does not differentiate its use case or provide any context for 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?

No annotations provided, so description carries transparency burden. It discloses pricing per endpoint and global coverage, but missing details on rate limits, authentication, side effects, or return formats. The pricing info is helpful for cost-conscious agents.

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 lengthy with pricing embedded in bullet points. It front-loads the main purpose but includes many details that could be streamlined. Structure is acceptable but not optimally concise.

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 16 parameters, no output schema, and no annotations, the description should map parameters to endpoints or provide usage examples. It fails to guide which parameters are relevant for each action, leaving the agent to infer from parameter names alone.

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% but descriptions are minimal (often just parameter names). The description adds value by documenting the 'action' parameter as an enum of endpoints, clarifying its function. For other parameters, it provides no additional meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Universal finder and discovery API' and lists multiple endpoints with their specific purposes (e.g., 'Best product for use case', 'Cheaper alternatives'). It distinguishes itself from specialized sibling tools by covering a broad range of searches, though not explicitly naming siblings.

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 outlines endpoints and their use cases, providing implicit guidance on when to use each. However, it lacks explicit direction on when to prefer findpulse over sibling tools or what factors to consider for endpoint 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?

No annotations are provided, and the description only mentions endpoints and pricing, without disclosing behavioral traits such as rate limits, idempotency, or potential side effects. The description does not compensate for missing 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 structured with a heading and bullet list, but it is somewhat lengthy and includes pricing information that may be irrelevant for an AI agent. It could be more concise.

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 22 parameters and no output schema or annotations, the description lacks guidance on how parameters relate to specific endpoints, and does not provide sufficient context for the AI agent to use the tool 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?

The input schema has 100% coverage with parameter descriptions, so the schema already provides meaning. The tool description adds value by grouping endpoints and indicating their purpose, but does not significantly enhance parameter understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Global fitness intelligence API' and lists endpoints covering various fitness domains, indicating a fitness-focused tool. However, it does not explicitly differentiate from sibling tools, though the sibling names suggest different domains.

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 fitness-related queries through its content and endpoint list, but lacks explicit guidance on when to use this tool versus alternatives or any 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, and the description does not disclose behavioral traits such as rate limits, authentication requirements, or whether it is read-only. It leaves these aspects unclear.

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 relatively long, enumerating endpoints with prices. It is structured with bullet-like formatting but could be more concise. Some information could be moved to the schema or omitted.

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 20 parameters, no output schema, and missing behavioral context, the description is not sufficiently complete. Critical aspects like how to combine parameters for different endpoints are not explained.

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?

Although schema coverage is 100%, many parameter descriptions are merely the parameter name (e.g., 'lang', 'home', 'away') without additional meaning. A few provide examples (e.g., line: 'e.g. -0.5'), but overall the descriptions add little value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is a global football/soccer betting intelligence API with specific endpoints. The name 'footballpulse' distinguishes it from siblings like 'alphapulse' or 'marketpulse'.

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 lists distinct endpoints with brief use-case explanations (e.g., 'match preview — team form, H2H, injuries'). While it doesn't explicitly compare to siblings, the specificity to football is 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 provided, the description carries the full burden. It discloses coverage ('Global') and per-endpoint pricing, which adds useful context. However, it does not mention authentication, rate limits, response formats, or whether operations are read-only. The 'AI-synthesized' tag hints at behavior but lacks detail. The truncation also undermines 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 structured with a summary, coverage, and a bulleted endpoint list, making it easy to scan. However, it is somewhat lengthy, and the truncated opening sentence ('and franchise br') is a clear flaw that interrupts readability. It earns credit for front-loading the main purpose but loses points for the incomplete thought.

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 (15 parameters, no output schema, no annotations), the description is incomplete. It does not specify which parameters are required or optional for each endpoint, nor does it describe the response structure. An agent would struggle to know exactly how to invoke actions like 'discover' or 'compare' with the right parameters. The endpoint list is helpful but insufficient 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?

Schema description coverage is 100%, but most parameter descriptions are tautological (e.g., 'location' is described as 'location'). The description lists endpoints, which indirectly suggests which parameters might be relevant (e.g., 'concepts' for comparison), but does not directly explain parameters. It adds some value beyond the schema but does not compensate for the unhelpful 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 this is a 'Global franchise intelligence API' and lists specific endpoints with one-line explanations (e.g., 'FDD analysis', 'franchise opportunity discovery'). This distinguishes it from sibling tools by focusing on franchise-related data. However, the opening sentence is truncated ('and franchise br'), preventing a perfect score.

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 franchise intelligence through its title and endpoint list, but it does not explicitly state when to use this tool vs alternatives. There is no mention of exclusions or specific prerequisites. The endpoint list provides context for different use cases, but no direct guidance on when to select this tool over sibling 'pulse' 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 provided, the description carries full burden. It discloses per-endpoint costs, which is a behavioral trait, but lacks details on authentication, rate limits, error handling, data freshness, or response structure. The 'AI-synthesized' mention hints at generation but not reliability.

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 front-loaded with purpose and structured as a clear list of endpoints with costs. It is informative but slightly verbose due to the endpoint list relative to the schema enum. Every sentence provides value (purpose, coverage, endpoints) with minimal waste.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (15 parameters, 17 endpoints, no output schema), the description lacks important context: it does not describe return formats, error scenarios, rate limits, or authentication requirements. The cost breakdown is helpful but insufficient for complete usage understanding.

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%, with terse parameter descriptions (e.g., 'card' -> 'card'). The description does not elaborate on parameters beyond listing endpoint names; it adds cost context but that is already implied by the enum values. Baseline 3 is appropriate since the description does not significantly augment parameter 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 clearly states 'Global gaming intelligence API' and lists specific endpoints (deals, meta, esports, TCG) that distinguish it from sibling tools focused on other domains. The verb 'analyze' or 'provide' is implied by the API nature, making purpose explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly suggests use for gaming queries via its endpoint list, but provides no explicit guidance on when to use this tool versus sibling *pulse tools. There is no mention of alternatives or exclusions, leaving the agent to infer from domain keywords.

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 carry behavioral disclosure. It usefully discloses per-endpoint pricing and claims real-time global coverage, but it omits important traits such as read-only behavior, response format, rate limits, authentication needs, or data limitations. The pricing information adds some transparency 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 relatively compact, but it includes marketing language and a truncated phrase ('for 19') that reduces clarity. The endpoint list largely duplicates the action enum, and the structure is more promotional than instructional, though it is not excessively 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 11 parameters, 10 endpoints, no output schema, and no annotations, the description is incomplete. It fails to explain which parameters are required or meaningful for each action, what the response looks like, or how to construct a valid request. The endpoint list alone is insufficient for an agent to reliably invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents each parameter. The description adds endpoint labels and prices but does not clarify how parameters map to specific endpoints or provide additional semantic detail beyond the schema. It meets the baseline but does not compensate for ambiguity around parameter combinations.

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 identifies the tool as 'Real-time geopolitical intelligence' and enumerates 10 concrete endpoints such as country-risk, conflict-scan, and sanctions-intel, making the tool's function reasonably clear. It distinguishes itself from siblings by its geopolitical focus, though it does not explicitly contrast with other 'pulse' 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 provides endpoint names and prices but no guidance on when to choose this tool over sibling tools or which endpoint to use for a given scenario. It does not mention exclusions, prerequisites, or alternative tools, leaving the agent to infer usage from endpoint labels.

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 carry behavioral disclosure. It does mention per-endpoint pricing and data sources, which adds context, but omits important details such as required authentication, rate limits, error behavior, or response format. This is a significant gap for a tool with 12 parameters and multiple endpoints.

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 structured with a bullet list, which aids readability, but it is verbose and ends abruptly with 'development bank opp' (clearly truncated). The opening sentence is somewhat marketing-like and could be tightened, while the list does provide useful information.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (9 endpoints, 12 parameters, no output schema), the description is incomplete. It lists endpoints and prices but does not explain which parameters apply to which endpoints, what the response looks like, or any usage examples. This leaves the agent under-informed for selecting and invoking the correct action.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already provides 100% coverage with descriptions for all 12 parameters, so the baseline is 3. The tool description does not add any extra meaning about how parameters relate to specific endpoints or provide examples beyond the schema's own 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 identifies the tool as a global government procurement intelligence API and lists its 9 endpoints with data sources, distinguishing it from the many 'pulse' siblings by domain. However, it lacks a single specific verb like 'query' or 'fetch', relying on the resource list to convey 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 endpoint list implies when to use each action (e.g., 'us-contracts' for US federal awards), but there is no explicit statement of when to choose this tool over alternatives or any exclusions. The coverage note ('Global') gives some context, but no direct comparison to other 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?

No annotations provided. The description mentions payment requirement (x402, USDC) and coverage, but is cut off ('Flagship match endpoint inc') and lacks details on error handling, rate limits, or data behavior. Incomplete and missing critical 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 a mix of narrative and list, but it's cut off and poorly structured. It includes redundant details (endpoint list with prices) that could be appended. The incomplete sentence ('Flagship match endpoint inc') indicates sloppy formatting.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 19 parameters and no output schema or annotations. The description fails to explain return values, error responses, endpoint behaviors, or how parameters interact across endpoints. The cut-off text makes it severely 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?

Schema description coverage is 100%, so baseline is 3. Many parameter descriptions are tautological (e.g., 'days', 'lang'), adding little value. The 'action' parameter and a few others (sector, country, org_type) provide meaningful lists. Overall, the description adds marginal value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it's a grant discovery and application intelligence API with multiple endpoints. However, it doesn't differentiate from the many sibling 'pulse' tools, which cover diverse domains like alpha, arbi, climate, etc. The purpose is evident but sibling context is missing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. The description lists endpoints but doesn't explain selection criteria or when not to use it. Given the many sibling tools, explicit usage context 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 are provided, so the description must fully convey behavioral traits. It mentions global coverage and endpoint costs but does not disclose side effects, idempotency, authentication requirements, or rate limits. This is insufficient for a tool with many parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with bullet points for endpoints, front-loading the core purpose. While not extremely concise, each sentence contributes useful information without redundancy.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (18 parameters, no output schema), the description covers the endpoints adequately but lacks details on return values, error handling, or prerequisites for each endpoint. More context on typical responses would improve 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?

Schema description coverage is 100%, so the schema already documents parameters. The description adds context by listing endpoints with short descriptions, but does not significantly enhance parameter understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the domain (energy grid intelligence) and the types of analysis it provides (solar feasibility, rate analysis, etc.), making it highly specific and distinguishable from the many 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 lists endpoints but does not explicitly guide when to use this tool over siblings or which endpoint to choose for a given task. The domain specificity provides implicit guidance, but explicit usage context is missing.

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?

There are no annotations, so the description carries the full burden. It discloses pricing per endpoint ($0.05–$0.15), data sources (USDA + ERS), and coverage (Global), which are useful operational details. However, it does not mention whether authentication is required, rate limits, error behavior, or whether the tool modifies any state; the informational nature is implied by terms like 'finder' and 'guide' 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?

The description opens with a clear one-sentence summary and then uses a structured bullet list of endpoints with prices, which is easy to scan. However, the endpoint list is quite long (15 items) and could potentially be condensed, but given the tool's multiple actions, the detail is justified and every line carries factual 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 tool's complexity (15 actions, 24 parameters, no output schema), the description is incomplete. It fails to map specific parameters to their relevant actions, leaving the agent to infer parameter usage through the schema. It also does not describe the structure or content of responses, which is critical since there is no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers 100% of the 24 parameters with descriptions, so a baseline of 3 is appropriate. The tool description does not add any parameter-specific meaning; it only lists endpoint names and prices, which are not directly tied to parameters. The schema itself provides all relevant parameter explanations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies HarvestPulse as a global farm-to-table and agricultural intelligence API, and enumerates 15 distinct endpoints with one-line descriptions (e.g., 'find: Local Farm & Market Finder', 'season: Seasonal Produce Calendar'). This gives a specific verb+resource for each capability and distinguishes the tool from unrelated sibling pulse APIs.

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 strong context about the tool's domain and coverage ('Global', 'USDA + ERS data synthesis'), implying when it should be used for agricultural/food-related queries. However, it does not explicitly mention alternative tools or boundary conditions, such as when not to use HarvestPulse or which sibling tools to use instead.

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