rickmorty

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it picks the right tool and fills arguments automatically, handles natural language input, and returns results. However, it lacks details on limitations (e.g., rate limits, error handling, or data source constraints), leaving some behavioral aspects unspecified.

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 the core purpose, followed by operational details and concrete examples. Every sentence adds value: the first defines the tool, the second explains how it works, and the third provides usage guidance with examples. It is efficient and well-structured 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 (natural language processing with automatic tool selection) and lack of annotations or output schema, the description is mostly complete. It covers purpose, usage, and behavior adequately but omits details on output format, error cases, or specific data source limitations, which could enhance completeness for an agent.

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

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

Schema description coverage is 100%, with the single parameter 'question' well-documented in the schema. The description adds minimal value beyond this, only reiterating 'question or request in natural language' through examples. It meets the baseline for high schema coverage but does not enhance parameter understanding significantly.

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: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer from data source'), and distinguishes it from siblings by emphasizing natural language interaction versus browsing tools or learning schemas. The examples further clarify the scope.

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

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

The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It contrasts with sibling tools like 'discover_tools' or 'search_characters' by offering a simplified, natural language interface for general queries, providing clear alternatives and exclusions.

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

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

No annotations are provided, so the description must fully convey behavioral traits. It mentions returning paired data and resource URIs, implying a read-only comparison, but does not disclose potential side effects (e.g., rate limits, authentication requirements) or idempotency. Given the absence of annotations, this is minimal but adequate.

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

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

The description is three sentences, front-loaded with the core purpose. Every sentence adds value: purpose, type-specific details, and benefit (replacing multiple calls). No unnecessary words or 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?

The tool has no output schema, but the description covers the return data nature (paired data + URIs) for both entity types. Given the complexity of two distinct comparison modes, the description is reasonably complete, though it could specify the format of 'paired data' more explicitly.

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%, baseline 3. The description adds significant context: for the 'type' parameter, it lists the specific data fields returned for each entity type; for 'values', it provides examples and clarifies formatting (tickers/CIKs vs drug names). This goes beyond the schema's brief 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 action: comparing 2–5 entities side by side. It specifies two distinct entity types with enumerated fields, and notes the output includes paired data and URIs. This distinguishes it from sibling tools like resolve_entity or search_characters.

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 context for when to use this tool: for side-by-side comparisons, and explicitly states it replaces 8–15 sequential calls, indicating efficiency. It does not, however, specify when NOT to use it or mention alternative tools, leaving some guidance implicit.

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 of behavioral disclosure. It mentions the tool returns 'the most relevant tools with names and descriptions' and implies a search-based interaction, but lacks details on permissions, rate limits, error handling, or exact return format. It provides basic context but misses deeper 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 front-loaded with key information in two concise sentences, with no wasted words. Each sentence earns its place by stating the purpose and providing critical usage guidance, making it highly efficient and 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?

Given the tool's moderate complexity (search functionality with 2 parameters), no annotations, and no output schema, the description is mostly complete. It covers purpose and usage well but lacks details on output structure (e.g., format of returned tools) and behavioral aspects like error cases, which would enhance completeness for an agent.

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

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

The schema description coverage is 100%, so the input schema already documents both parameters (query and limit) thoroughly. The description adds no additional parameter semantics beyond what the schema provides, such as examples or usage tips for the query parameter. Baseline 3 is appropriate as the schema does the heavy lifting.

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 with specific verbs ('search', 'returns') and resource ('Pipeworx tool catalog'), and explicitly distinguishes it from siblings by emphasizing its role for discovery among 500+ tools. It goes beyond the name/title to explain 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 provides explicit guidance on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task'), including a clear condition (500+ tools) and alternative context (vs. directly using other tools). It effectively directs usage in a crowded tool environment.

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

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

The description discloses that it returns pipeworx:// citation URIs, bundles multiple data sources, and is a single composite call. It hints at performance by noting federal contracts are 'too slow to bundle.' However, with no annotations, it could be more explicit about being read-only and any side effects.

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

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

Two sentences, front-loaded with the core purpose, and each sentence serves a purpose. Lists what’s included and provides an alternative. 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?

Despite no output schema or annotations, the description is remarkably complete for a complex tool: it explains input format, data included, and usage alternatives. It could mention if there is a limit or pagination, but it captures the essential 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 coverage is 100%, baseline 3. The description adds significant meaning: explains that type is only 'company' now, and value can be ticker or CIK, and that names are not supported (requires resolve_entity). This goes beyond the schema 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 it returns a 'full profile of an entity across every relevant Pipeworx pack in one call,' listing the specific data included (SEC filings, XBRL, patents, news, LEI) and distinguishing it from sequential calls and the sibling tool usa_recipient_profile.

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

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

It explicitly says when to use (for company profiles across packs) and when not to use (for federal contracts, use usa_recipient_profile). It also gives a prerequisite: for names, use resolve_entity first.

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 of behavioral disclosure. It states this is a deletion operation, implying it's destructive and irreversible, but doesn't clarify permissions needed, side effects (e.g., if deletion affects other data), or error handling (e.g., what happens if the key doesn't exist). This leaves significant gaps for a mutation tool.

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

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

The description is a single, clear sentence with zero wasted words. It's front-loaded with the core action ('Delete') and resource, making it highly efficient and easy to parse, which is ideal for tool selection.

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 destructive tool with no annotations and no output schema, the description is inadequate. It doesn't explain what constitutes a 'stored memory', how deletion impacts the system, what the response looks like, or error scenarios. Given the complexity of a mutation operation, more context is needed to ensure safe and correct usage.

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, with the 'key' parameter fully documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as format examples or constraints, so it meets the baseline score of 3 where the schema does the heavy lifting.

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 ('Delete') and the resource ('a stored memory by key'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'recall' or 'remember', which likely interact with the same memory system, so it doesn't reach the highest 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 provides no guidance on when to use this tool versus alternatives like 'recall' (which might retrieve memories) or 'remember' (which might create them). There's no mention of prerequisites, error conditions, or typical use cases, leaving the agent with minimal context for decision-making.

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 rate limit ('5 messages per identifier per day') and the free nature, which are key behavioral traits. It also instructs on content limits (2000 chars). However, it does not mention what happens after sending or whether acknowledgments exist, but this is reasonable for a simple feedback tool.

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

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

The description is extremely concise: two main sentences plus a rate-limit note. No filler or redundant information. Every sentence serves a purpose, making it easy for an agent to parse quickly.

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 feedback tool with no output schema, the description covers essential aspects: purpose, use cases, parameter usage guidelines, and behavioral constraints (rate limit). It could optionally mention confirmation or response expectations, but the current information is sufficient for effective tool use.

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

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

Schema description coverage is 100% (all parameters and enum values described in detail). The description adds a meta-guideline about content style, which slightly enhances understanding, but the schema already provides rich context. With full schema coverage, a baseline of 3 is appropriate as the description adds marginal 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?

The description clearly states the tool's purpose: 'Send feedback to the Pipeworx team.' It further specifies use cases (bug reports, feature requests, missing data, praise), making it easy for an agent to understand what the tool does and distinguish it from unrelated 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 provides explicit guidance on when to use the tool ('Use for bug reports, feature requests, missing data, or praise') and content best practices ('Describe what you tried in terms of Pipeworx tools/data — do not include the end-user's prompt verbatim'). It lacks explicit when-not-to-use statements or alternative tools, but this is acceptable given the unique purpose.

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

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

With no annotations provided, the description carries full burden. It discloses the tool retrieves or lists memories stored across sessions, which is valuable behavioral context. However, it doesn't mention error handling (e.g., what happens if key doesn't exist), performance characteristics, or format of returned data. The description adds some value but lacks comprehensive 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 perfectly concise with two sentences that each earn their place. The first sentence explains the dual functionality, the second provides usage context. No wasted words, front-loaded with core functionality.

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 1 parameter, 100% schema coverage, and no output schema, the description is reasonably complete. It explains what the tool does and when to use it. However, without annotations or output schema, it could benefit from more detail about return format or error conditions. The description covers basics but leaves some contextual 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%, so the schema already documents the optional 'key' parameter. The description adds meaningful context by explaining the semantic effect of omitting the key ('list all stored memories') and relating it to retrieving session context. This goes beyond the schema's technical documentation to explain the parameter's purpose in practice.

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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings by mentioning session context and explicitly differentiating between retrieval by key vs. listing all keys.

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 usage guidance: 'Use this to retrieve context you saved earlier in the session or in previous sessions' establishes the primary use case. It also specifies when to omit the key parameter ('omit key to list all keys'), creating clear alternative usage patterns within the same 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?

With no annotations provided, the description carries full transparency burden. It discloses multi-source fan-out, parallel execution, acceptable date formats (ISO and relative), and return components (structured changes, total_changes count, pipeworx:// URIs). It does not mention rate limits, error handling, or data freshness, but the core behavioral traits are well-covered.

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 (4 sentences), front-loaded with the core purpose, and efficiently covers sources, date formats, and return values. Each sentence contributes useful information. Minor redundancy with schema descriptions, but overall 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?

Given the complexity (multi-source, parallel, date parsing) and absence of an output schema, the description provides essential return structure (structured changes, count, URIs) and input constraints (entity type, date formats). It could address edge cases (e.g., entity not found) but covers core workflows adequately.

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 descriptions already cover all three parameters with acceptable detail (type allowed values, since format guidance, value examples). The description adds minimal new semantics beyond restating schema content (e.g., ISO vs relative, use '30d' or '1m'). Since schema coverage is 100% and schema descriptions are informative, 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 uses specific action verbs and resources: 'What's new about an entity since a given point in time,' listing concrete sources (SEC EDGAR, GDELT, USPTO) for type=company. This clearly differentiates from sibling tools like entity_profile (static information) and compare_entities (comparison), making the purpose distinct and actionable.

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

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

The description explicitly states use cases: 'Use for "brief me on what happened with X" or change-monitoring workflows.' It provides guidance on date format preferences ('Use "30d" or "1m" for typical monitoring'). While it does not explicitly list when not to use or alternatives, the context is sufficient for common agent 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?

With no annotations provided, the description carries full burden and does well by disclosing important behavioral traits: it explains persistence differences (authenticated vs. anonymous sessions), session duration (24 hours for anonymous), and the cross-tool context capability. It doesn't cover rate limits or error conditions, but provides substantial operational 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?

Two well-structured sentences with zero waste. The first sentence states the core function, the second provides crucial behavioral context. Every phrase earns its place, and information is front-loaded appropriately.

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 2-parameter tool with no annotations and no output schema, the description provides good coverage of what the tool does, when to use it, and key behavioral aspects. It could benefit from mentioning what happens on duplicate keys or error conditions, but covers the essential context well given the tool's 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?

Schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema properties. It meets the baseline expectation when schema does the heavy lifting.

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 specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (retrieval) and 'forget' (deletion). It explicitly identifies the tool's function without restating the name.

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

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

The description provides clear context for when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but doesn't explicitly state when NOT to use it or mention alternatives like 'recall' for retrieval. It offers practical guidance without exclusions.

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

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

With no annotations, the description fully discloses that it returns ticker, CIK, company name, and pipeworx:// URIs. It also notes that v1 only supports type='company', indicating no side effects. No behavioral 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?

Three concise sentences, each serving a purpose: purpose, details with examples, and benefit. No wasted words, effectively 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?

Despite lacking an output schema, the description enumerates return fields. It covers the tool's scope, v1 limitations, and efficiency gain. Could specify error handling, but given simplicity, it is adequately complete.

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

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

Schema coverage is 100%, providing base descriptions for both parameters. The description adds value by specifying example inputs for value and clarifying that type is limited to 'company' for v1, which helps the agent understand usage.

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

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

The description clearly states the tool resolves entities to canonical IDs across sources, with specific input examples (ticker, CIK, name) and output fields. It distinguishes itself from sibling tools by claiming 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?

It provides clear context on when to use (single-call resolution across data sources), but does not explicitly state when not to use or name alternative tools. The benefit over multiple lookups is implied.

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

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

With no annotations, description fully discloses the tool's behavior: data sources (SEC EDGAR + XBRL), return values (verdict, structured form, actual value with citation, percent delta), and that it replaces multiple agent calls. No destructive behaviors mentioned.

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 concise (3-4 sentences) with each sentence adding information: purpose, scope, outputs, and efficiency gain. 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?

Despite no output schema, description thoroughly covers input, processing, and output. It includes verdict types and return fields, making the tool's behavior fully understandable for an agent.

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

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

Schema has one parameter with 100% coverage. Description adds value by providing concrete examples of valid claims and explaining the supported claim types beyond the schema's generic 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?

Description clearly states the tool fact-checks natural-language claims against authoritative sources, specifying support for company-financial claims via SEC EDGAR + XBRL. It lists the verdict types and outputs, distinguishing itself from other 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?

Description provides clear context for when to use (fact-checking financial claims for US companies) and implies scope limitations. However, it does not explicitly mention alternatives or when not to use the tool.

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