Inaturalist

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

The description reveals that Pipeworx routes to internal tools and fills arguments, which is key behavioral context. However, it does not disclose safety aspects like read-only nature, rate limits, or potential side effects, and no annotations are present to supplement this.

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

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

The description is concise (3 sentences plus examples), front-loads the core function, and uses examples efficiently. No extraneous content.

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

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

Given the single parameter and lack of output schema, the description is complete for an agent to understand when and how to use the tool. It explains the routing behavior, which is the key complexity.

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

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

With 100% schema coverage, baseline is 3. The description adds value by framing the parameter as 'in plain English' and providing examples, which aids the agent in understanding input format beyond the schema's 'natural language' description.

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

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

The description clearly states the tool accepts a natural language question and returns an answer from the best source. It provides examples and contrasts with browsing tools/schemas, distinguishing it from sibling tools like search_observations.

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

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

Explicitly says to 'just describe what you need' and provides examples of appropriate queries. It implies use for open-ended questions but does not explicitly state when not to use it, though the context of sibling tools covers alternatives.

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

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

No annotations are provided, so the description carries the full burden. It discloses return data (paired data with resource URIs) and metrics per type, but does not mention read-only nature, side effects, authentication, or error handling.

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

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

Three sentences: first states purpose, second gives per-type details, third mentions output and efficiency. Every sentence is essential with no repetition or fluff.

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

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

Given the tool's complexity (2 parameters, no output schema), the description covers purpose, input usage, output format, and efficiency benefits comprehensively.

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

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

Schema coverage is 100% and the description adds value by explaining how to use the 'values' parameter for each entity type with examples, and listing the specific data fields returned per type.

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

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

The description clearly states the tool compares 2-5 entities side by side, with specific metrics for company and drug types. It distinguishes itself from siblings by noting it replaces 8-15 sequential agent calls.

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

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

The description explains when to use the tool (comparing 2-5 entities of type company or drug) and implies efficiency gains. It does not explicitly state when not to use it or name alternatives, but the context is clear.

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

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

No annotations are provided, so the description carries the full burden for behavioral disclosure. It describes the tool as a search that returns results, implying it is read-only and safe, but it does not explicitly state that it has no side effects or mention any constraints (e.g., rate limits, permissions).

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

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

Two concise sentences that front-load the purpose and then provide usage guidance. Every sentence earns its place with no wasted words.

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

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

Given the simple two-parameter schema and no output schema, the description adequately covers what the tool does, what it returns (tool names and descriptions), and when to use it. It could mention pagination or the return format for completeness, but current info is sufficient for basic use.

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

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

Schema coverage is 100%, so the schema already documents both parameters clearly. The description does not add meaning beyond the schema; it only restates the purpose. Baseline 3 is appropriate as the description adds minimal value for 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 searches the tool catalog by describing a need, returning relevant tools with names and descriptions. It distinguishes itself from siblings like search_observations and search_taxa by emphasizing it's for discovering tools among 500+ options.

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 advises to call this tool FIRST when many tools are available, providing clear when-to-use guidance. It does not explicitly mention when not to use, but the context is sufficient for an agent.

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

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

Discloses that it returns pipeworx:// citation URIs and replaces 10–15 sequential calls. No annotations provided, but the description covers the read nature and data breadth. Lacks details on error handling or rate limits, but overall transparent.

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

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

Four sentences with no fluff. First sentence states purpose, second lists contents, third mentions return format, fourth gives alternative. Front-loaded and efficient.

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

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

Despite no output schema, the description explains return format and data categories. It also provides context on when to use the tool and what it replaces. Comprehensive for a profile 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?

Adds crucial context beyond schema: explains that 'value' can be ticker or CIK, and explicitly states names are not supported—directing agents to use resolve_entity. Schema coverage is 100%, and the description enhances understanding.

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

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

The description clearly states the tool's purpose: 'Full profile of an entity across every relevant Pipeworx pack in one call.' It enumerates specific data types (SEC filings, XBRL data, patents, news, LEI) and distinguishes itself from siblings like resolve_entity (for name resolution) and compare_entities.

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

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

Provides explicit guidance: for federal contracts, use usa_recipient_profile directly; for names, use resolve_entity first. This helps the agent choose the correct tool for different scenarios.

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 behavioral traits. It states the destructive action 'delete' but does not mention reversibility, side effects, or error conditions. Minimal 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?

Single sentence, no fluff. Directly conveys the purpose with minimal words.

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

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

For a simple delete operation with one parameter and no output schema, the description is mostly complete. It could mention irreversibility but is adequate.

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. The description adds no extra meaning beyond the schema; 'by key' merely echoes the parameter description.

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

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

The description explicitly states the action 'delete', the resource 'stored memory', and the means 'by key'. It clearly distinguishes from sibling tools like 'remember' (store) and 'recall' (retrieve).

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 vs. alternatives. Usage is implied as the inverse of 'remember', but there is no mention of prerequisites or when not to use.

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

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

Discloses rate limiting (5 per day per identifier) and that it's free. However, lacks details on what happens after submission (e.g., acknowledgment, processing). Without annotations, more context would be beneficial.

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?

Extremely concise: three sentences convey purpose, usage, content guidelines, and rate limit. No unnecessary words.

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

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

For a simple feedback tool with no output schema, the description covers all essential aspects: what it does, when to use, what to include, and rate limits. No gaps identified.

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. The description adds no new information about parameters beyond what's already in the schema (e.g., message length, type enum descriptions).

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

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

The description clearly states the tool's purpose (sending feedback), lists specific use cases (bug reports, feature requests, missing data, praise), and is distinct from all sibling tools which are for data retrieval or memory operations.

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

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

Explicitly states when to use (for feedback types) and provides content guidelines (describe what you tried, avoid verbatim prompts). Mentions rate limit but doesn't exclude alternatives (though none are relevant).

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 explains the dual behavior (single retrieval vs list all) but does not disclose error handling (e.g., behavior if key not found), output format, or side effects. Adequate but could be more detailed.

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

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

Two sentences, no filler, front-loaded with core action. Every sentence adds value.

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

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

For a simple retrieval tool with no output schema, the description covers usage and context (previous sessions). Missing only minor details like expected return type, but overall complete for the complexity level.

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%, and the description reinforces the parameter purpose without adding new insights. Baseline score of 3 is appropriate as the schema already carries the 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 the tool retrieves a specific memory by key or lists all memories if key is omitted. It explicitly distinguishes from sibling tools like 'remember' and 'forget' by focusing on retrieval.

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

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

The description specifies when to use the tool ('to retrieve context you saved earlier'). It implies not to use for storing or forgetting via sibling tool names but lacks explicit when-not-to-use or alternative tool references.

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 fully covers behavioral details: the fan-out to SEC EDGAR, GDELT, USPTO; accepted date formats (ISO and relative); and return structure (structured changes, total_changes count, pipeworx:// URIs). There are no contradictions.

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

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

The description is concise (about 100 words) and front-loaded with the main purpose. Every sentence adds value—examples, data sources, return format, use cases—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 3 parameters, no output schema, and missing annotations, the description is remarkably complete. It covers the fan-out behavior, date parsing, return structure, and appropriate use cases, leaving no major gaps for an agent to understand 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?

Input schema has 100% coverage, but the description adds significant value beyond the schema: it explains that 'since' accepts both ISO and relative dates (with examples), clarifies that 'value' can be a ticker or CIK, and recommends '30d' or '1m' for monitoring. This extra guidance greatly enhances parameter understanding.

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

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

The description clearly states the tool's purpose: retrieving what's new about an entity since a time point. It specifies the supported entity type (company) and the parallel data sources it queries, making the purpose distinct from siblings like entity_profile or compare_entities.

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 explicit use cases: 'brief me on what happened with X' and change-monitoring workflows. It does not mention when not to use or alternatives, but the context is clear enough for typical scenarios.

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 fully handles transparency. It discloses persistence differences for authenticated vs anonymous users (persistent vs 24-hour expiry), which is valuable behavioral context 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?

Two sentences only: first sets purpose, second provides usage and behavioral info. No redundancy, front-loaded, every sentence adds value.

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

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

Given no output schema and no annotations, the description covers purpose, usage context, and behavioral nuance (persistence). Lacks mention of return value, but for a simple store operation this is acceptable.

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

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

Schema coverage is 100% with parameter descriptions including examples. The description adds no further parameter-level detail, achieving baseline score for high schema coverage.

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

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

The description clearly states the action 'store' and the resource 'key-value pair in session memory'. It implicitly distinguishes from sibling tools like 'recall' (retrieve) and 'forget' (delete) by focusing on storage.

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?

Explicit guidance to use for saving intermediate findings, user preferences, or context across calls. Does not include when not to use or mention alternatives, but context is clear enough for an agent.

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, but the description accurately describes the tool's behavior: it is a read-only lookup that returns IDs and URIs. No contradictions, but could mention if results are cached or limited.

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

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

The description is three sentences, each adding essential information: purpose, type-specific details, and output/benefit. No wasted words.

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

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

Given the tool's simplicity (2 parameters, no nested objects, no output schema), the description fully covers input formatting, supported types, and expected output, making it self-contained.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by providing concrete examples for each type (e.g., ticker AAPL, CIK 0000320193) and describing the output, which goes beyond the schema's definitions.

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 resolves entities to canonical IDs across Pipeworx data sources, with specific examples for 'company' and 'drug' types, and distinguishes it from siblings by noting it replaces 2-3 lookup calls.

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

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

The description gives clear context on when to use (resolving entities) and mentions it replaces multiple lookup calls, but does not explicitly exclude alternatives or name specific 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 exist, so the description carries the full burden. It mentions return fields ('photographed sightings with coordinates, dates, and observer info') but lacks details on pagination, rate limits, authentication, or the default sorting behavior. This is adequate but not exhaustive.

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

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

Three sentences with no filler. The purpose is stated first, followed by filters, then return info and a usage hint. Every sentence adds value.

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

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

With 6 parameters and no output schema, the description covers the main features: filters, return fields, and a cross-validation suggestion. It does not mention pagination details or error cases, but the per_page parameter is in the schema, so the description is sufficient for a search tool.

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

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

Schema coverage is 100%, so baseline is 3. Description adds minor extra context (e.g., 'Auto-resolved to place_id' for place) but does not significantly enhance understanding beyond the schema. For most parameters, the schema already describes them adequately.

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

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

The description uses a specific verb ('Search') and resource ('citizen-science observations on iNaturalist'). It clearly distinguishes from siblings like search_taxa (which searches taxa, not observations) and top_species.

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 the main filters (taxon, place, year, threatened, quality_grade) and suggests cross-validation with GBIF. While it doesn't explicitly state when not to use it or compare to alternatives, the context is clear enough to infer usage.

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

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

With no annotations, the description covers the basic behavior: it returns specific fields. However, it does not disclose whether it is read-only, any rate limits, or other operational traits beyond what is 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?

Two sentences: first states purpose and outputs, second instructs on usage. No redundant information, front-loaded effectively.

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

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

Given no output schema, the description lists returned fields and suggests downstream use. All parameters are covered in schema and explanation is sufficient for a search tool.

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

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

Schema coverage is 100%, but the description adds meaning by clarifying that 'query' accepts common or scientific names, 'rank' restricts to a rank, and 'per_page' defaults to 10. It also explains output usage, 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 states the tool searches iNaturalist taxa by name (common or scientific), and lists the returned fields (taxon ID, rank, ancestry, conservation status, photo URL). It distinguishes itself from sibling tools like 'search_observations' by focusing on taxa.

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 mentions using the taxon ID for downstream filters or disambiguation, but does not explicitly state when to use this tool over alternatives like 'resolve_entity' or when not to use it.

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

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

With no annotations, the description carries full burden. It discloses the auto-resolution of place names and the return content (ranked species, counts, taxonomic info), but does not mention potential errors, rate limits, or data freshness. No contradictions with 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?

Three sentences with no wasted words. The main purpose is front-loaded, and each sentence adds value: purpose, behavior, and usage suggestion. Highly 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 the simplicity (3 params, no output schema), the description covers the tool's function well. Missing details like error handling or pagination behavior for per_page, but these are minor. Overall adequate.

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. The description adds context for the 'place' parameter (auto-resolution) but does not enhance understanding of 'year' or 'per_page' beyond the schema descriptions. Parameters are adequately documented.

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 returns the most-frequently-observed species in a place over a date range, with specific details like auto-resolving place names and returning ranked species with counts and taxonomic info. It distinguishes itself from sibling tools like search_observations and search_taxa by focusing on aggregated, ranked results.

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 with 'Great for what wildlife lives here? questions,' providing some context but no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives among sibling tools or specify 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?

With no annotations provided, the description fully bears the burden of behavioral disclosure. It transparently explains the output (verdict, structured form, actual value with citation, percent delta), the underlying sources and pipeline (SEC EDGAR + XBRL, replacing multiple steps), and no behavioral contradictions exist.

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

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

The description is four sentences with zero waste: first sentence states core purpose, second defines scope/version, third lists outputs, fourth explains efficiency gain. Every sentence adds value and the key information is front-loaded.

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

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

Given the tool's simplicity (single parameter, no output schema), the description provides complete context: supported domains, data sources, return values, and how it replaces multiple agent calls. No gaps remain for an agent to understand the tool's capabilities and 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 input schema already covers the single parameter (claim) with examples and 100% description coverage. The description adds context about supported domains and structured extraction but does not significantly enhance parameter semantics beyond what the schema provides, resulting in a baseline score.

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 fact-checks natural-language claims against authoritative sources, specifies supported claim types (company-financial) and data sources (SEC EDGAR + XBRL), and distinguishes itself from siblings like resolve_entity by being a composite replacement for multiple agent calls.

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

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

The description explicitly limits usage to company-financial claims for US public companies (v1), providing clear context for when to use. It does not, however, provide explicit exclusions or mention alternative tools for other claim types, though the domain restriction is clear.

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