Clinicaltrials

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds valuable behavioral details: it returns per-model scores, confidence, signals, raw_response, and a combined view. It also discloses cost implications for Anthropic (BYO key). 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 (two sentences) and well-structured: first sentence gives purpose and default, second sentence details optional key and return structure. 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?

The tool has 4 parameters and no output schema. The description explains the return structure (per-model results + combined view), which is sufficient for an agent to understand what to expect. Complexity is moderate, and the description is complete enough for effective use.

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

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

Input schema has 100% coverage with descriptions for all 4 parameters. The description adds context beyond schema: e.g., default model is Workers AI Llama-3.3-70b (free), _apiKey is optional and passed straight through, and context helps disambiguate. This adds meaningful 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: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' This verb-resource combination is specific and distinguishes it from sibling tools like ask_pipeworx 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 clear usage context ('Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains default model and optional API key for Anthropic. However, it does not explicitly state when not to use this tool or mention alternatives among siblings.

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

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

No annotations are provided, so the description carries full burden. It mentions automatic tool selection and argument filling but does not disclose potential limitations, such as scope of data sources, response format, latency, or error behavior. The description lacks important 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?

Description is concise and front-loaded with the core purpose. Each sentence adds value, though the examples could be slightly more varied. No 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 tool's simplicity (one string param, no output schema), the description is mostly adequate but lacks detail on what happens after a question is asked (e.g., whether it returns a citation, confidence score, or raw text). Behavioral gaps reduce 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 baseline is 3. The description adds minimal extra meaning beyond the schema, mostly elaborating on the single parameter's purpose through examples.

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: answering natural language questions by automatically selecting the best data source and filling arguments. It distinguishes itself from sibling tools by acting as a general-purpose query interface rather than a specific data source tool.

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

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

The description implies when to use (when you have a plain English question) but does not explicitly state when not to use it or provide alternatives among sibling tools. Examples help, but no exclusion criteria are given.

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

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

Description goes beyond annotations (readOnlyHint, openWorldHint, destructiveHint) by detailing the internal process: resolving the market, classifying the bet, fanning out to relevant packs, and returning an evidence packet with a market-vs-model comparison. 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?

Description is concise at 5 sentences, front-loaded with the core purpose, then elaborates on mechanism, output, and use cases. Every sentence adds value 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?

Despite lacking an output schema, the description explains the return value: an evidence packet plus a simple market-vs-model comparison. It covers input, process, output, and use cases, making the tool 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 coverage is 100%, but description adds significant value: it explains that the 'market' parameter accepts slug, URL, or question text (consistent with schema), and for 'depth' it clarifies 'quick = 2-3 evidence sources, thorough = full fan-out. Default thorough.' This provides actionable detail beyond the schema enum.

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 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call', specifying the verb (research), resource (Polymarket bet), and the data system (Pipeworx). It distinguishes itself from sibling tools like ask_pipeworx by specifically targeting Polymarket bets and providing a comprehensive evidence packet.

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 explicit use cases: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?". It does not explicitly state when not to use it or compare to alternatives, but the context is clear enough for an agent to decide.

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; description details returned data (e.g., revenue, adverse events) and URIs. Lacks error handling or data freshness but sufficient for read operation.

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

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

Three sentences, front-loaded with purpose, no redundant words. Clearly 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?

No output schema, but description fully explains return values, resource URIs, and entity count range. Sufficient for agent 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 coverage 100%; description adds mapping from type to returned fields and format examples (tickers, drug names), significantly enhancing 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?

Clearly states the tool compares 2-5 entities side by side, with specific data for company and drug types. Differentiates from sequential calls, no sibling overlap.

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 replaces 8-15 sequential calls, indicating efficiency. Does not specify when not to use or list alternatives, but context is clear.

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

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

No annotations are provided, so the description should disclose behavioral traits. It doesn't mention if the count is approximate or exact, if it includes all phases by default, or any rate limits. The description is minimal and lacks important 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 concise with two sentences. The first sentence clearly states the primary function. However, the second sentence about use cases could be integrated or made more specific. Overall, it's appropriately sized 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?

Given there is no output schema, the description could explain what the output looks like (e.g., just a number? also grouped by phase?). The tool has 3 optional parameters, but the description doesn't guide on how filters affect counting. It's minimally 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%, so all parameters have descriptions. The description adds no additional parameter meaning beyond the schema. Baseline 3 is appropriate as the schema already documents parameters 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 states it counts clinical trials by condition, which is clear. However, it doesn't differentiate from sibling tools like ct_search, which also deals with clinical trials. The verb 'count' helps distinguish, but more explicit distinction would improve 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 mentions 'landscape analysis and competitive intelligence' as use cases, which is good. But it doesn't specify when not to use this tool or compare it to alternatives like ct_search or ct_get_study. No guidance on excluding 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 are provided, so the description carries full burden. It states returns complete protocol sections, which is helpful. However, it doesn't disclose whether the tool requires authentication, has rate limits, or what happens if the NCT ID doesn't exist (e.g., error behavior). The description adds moderate value beyond the schema but lacks depth.

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 immediately provide scope. Every word adds value: 'full study details', 'by its NCT ID', and listing returned sections. No redundancy or filler.

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

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

Given the tool's simplicity (one parameter, no output schema, no annotations), the description is nearly complete. It covers what the tool does and what it returns. Minor gaps: no mention of error handling or output format, but for a straightforward lookup tool this is acceptable.

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

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

Schema coverage is 100%, so the description need not repeat parameter details. It adds context by explaining the tool's purpose (full study details) and the content returned (eligibility, outcomes, results), which clarifies what the parameter 'nct_id' is used for. This adds meaning beyond the schema's basic 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 retrieves full study details for a clinical trial by NCT ID. It specifies the verb 'Get', the resource 'study details', and the identifier type 'NCT ID'. It also lists returned content (eligibility, outcomes, results), distinguishing it from siblings like ct_search or ct_count_by_condition.

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

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

The description implies when to use this tool: when needing full study details for a known NCT ID. It doesn't explicitly mention when not to use it or alternatives, but the context of sibling tools (e.g., ct_search for broader queries) provides implicit guidance. The clear purpose helps the agent decide.

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 cover behavioral traits. It clarifies sorting by last update date, but does not disclose other behaviors like rate limits, authentication needs, or whether results are cached.

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 with no waste. Front-loaded with the action and result, then a one-sentence use case.

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 input schema with only two optional parameters and no output schema, the description adequately covers purpose and usage. It could mention return format or behavior when no results are found, but is complete enough for this 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 baseline is 3. The description adds value by indicating that the 'query' is 'Optional' and for narrowing results, but does not add meaning beyond the schema descriptions.

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

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

The description uses a specific verb 'Get' and resource 'recently updated or posted clinical trials' with clear sorting criteria. It distinguishes from siblings like ct_search by focusing on recency rather than general search.

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

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

The description states it is 'Good for monitoring pipeline changes,' providing a clear use case. However, it does not explicitly mention when not to use it or suggest alternatives among the listed siblings.

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

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

No annotations are provided, so the description carries full burden. It describes the search and filter behavior and the return structure, which is adequate but lacks details on pagination, rate limits, or data freshness. A neutral score is appropriate.

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

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

The description is a single, concise sentence that front-loads the key information: what it searches, by which filters, and what it returns. No extraneous words.

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

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

Given the complexity (5 parameters, no output schema), the description is minimal but covers the basic search and filter functionality. It does not explain the response structure beyond 'study count and array of matching trials', which may be insufficient for an agent to parse results.

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

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

Schema description coverage is 100%, so the baseline is 3. The description mentions keyword, status, phase, and sponsor as filter options, adding no semantics beyond the schema's parameter descriptions.

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

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

The description clearly states the tool searches clinical trials by keyword and filters (status, phase, sponsor), and mentions the return value (count + array of trials). It distinguishes itself from siblings like ct_get_study (single study) and ct_count_by_condition (count only), though it could be more explicit about the difference.

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 searching trials with filters, but provides no explicit guidance on when to use this tool vs alternatives like ct_count_by_condition or ct_get_study. No exclusions or prerequisites 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?

Annotations are empty, so the description carries full burden. It indicates read-only behavior (listing), but does not disclose rate limits, pagination, or data freshness. Lacks explicit statement that it is non-destructive.

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 concise two-sentence structure. The first sentence states purpose, the second adds context. No wasted words.

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

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

The tool has 4 parameters (1 required) and no output schema. The description provides minimal additional context beyond the schema. It mentions pipeline analysis but does not explain return format, ordering, or error handling.

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 does not add parameter semantics beyond the schema. It does not explain the relationship between parameters or provide examples of how filters interact.

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

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

The description clearly states the tool lists clinical trials by sponsor, which is a specific verb+resource combination. It differentiates from siblings like ct_search (general search) and ct_count_by_condition (counts by condition).

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

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

The description says 'useful for pipeline analysis,' implying when to use, but does not explicitly state when not to use or mention alternatives like ct_search for broader queries.

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

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

Description states it returns the most relevant tools but does not specify the ranking algorithm, whether it uses semantic search, or any limitations (e.g., rate limits). No annotations provided, so some behavioral detail is missing.

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 describes output, third gives usage guidance. No wasted words, front-loaded with key information.

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

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

Given no output schema, description clarifies what is returned (names and descriptions). Tool has simple inputs; description covers essential aspects. Could mention what happens on empty results or errors, but not critical.

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

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

Input schema has 100% coverage with descriptions for both 'query' (natural language description) and 'limit' (max number). Description reinforces the natural language aspect, adding value beyond 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?

Description clearly states it searches a tool catalog and returns relevant tools with names and descriptions. The verb 'search' plus the resource 'Pipeworx tool catalog' makes the purpose specific and distinct.

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

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

Explicitly says 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear when-to-use guidance and implies it's a discovery step before using other 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 return format (pipeworx:// URIs) and performance characteristic (bundling multiple sources). Lacks explicit read-only indication, but the nature is implied.

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

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

Three sentences with no wasted words. Each sentence adds distinct value: purpose, content, and exclusion guidance. Front-loaded with essential information.

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

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

Given low parameter count (2), full schema coverage, and no output schema, the description covers all necessary context: what data is returned, when to use alternatives, and how to prepare inputs.

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 extra guidance: 'Names not supported — use resolve_entity first,' which enhances schema description. No further parameter details needed.

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

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

The description clearly states 'Full profile of an entity' and lists specific data types included (SEC filings, revenue, patents, news, LEI). It distinguishes from siblings by mentioning 'For federal contracts call usa_recipient_profile directly' and implies replacement of 10-15 sequential 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?

Explicitly states when to use (comprehensive profile) and when not to (federal contracts), and suggests using resolve_entity first if only a name is available. Provides clear alternatives.

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

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

No annotations provided, so description carries full burden. It states 'Delete' (destructive), but doesn't disclose whether deletion is permanent, if confirmation is needed, or if it affects other data. The description is too brief to cover 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?

Extremely concise (6 words) and front-loaded with the action. However, it is perhaps too terse, lacking context that would earn a 5.

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 (1 required param, no output schema), the description is minimal but complete enough to convey basic purpose. However, it lacks any behavioral or usage context that would help an agent decide to invoke it.

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

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

Schema coverage is 100% with a single parameter 'key' described as 'Memory key to delete'. The description adds no additional semantic value beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'Delete', the resource 'stored memory', and the means 'by key'. It distinguishes from siblings like 'remember' (create) and 'recall' (retrieve), though it could explicitly contrast with them.

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

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

No guidance on when to use this vs alternatives. The tool is for deletion, but no context is given about prerequisites (e.g., memory must exist) or consequences (irreversible?). No mention of 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?

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) already indicate a safe, read-only operation. Description adds process details (fetches page, extracts title/description/key links, emits markdown) without contradicting annotations. Lacks mention of potential errors or rate limits, but sufficient given annotation coverage.

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 describes process, third lists use cases. Front-loaded and every sentence adds value with no 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 no output schema and simple parameters, the description fully covers purpose, process, use cases, and parameter hints. Annotations provide safety context. Complete for an agent to select and invoke 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?

Input schema provides 100% coverage with clear descriptions for both parameters. The description does not add significant new meaning beyond the schema, so baseline score of 3 is appropriate.

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

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

Description explicitly states it generates an llms.txt file for a URL, with specific verb 'generate' and resource 'llms.txt'. It distinguishes from sibling tools like ai_visibility_check and scan_competitor_ai_presence by focusing on file generation rather than visibility analysis.

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

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

Explanation includes 'Useful for:' with three concrete scenarios: client indexing, own project drafting, and competitor auditing. It provides clear context but does not explicitly list when not to use or mention alternatives, which would elevate to a 5.

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 key behaviors: rate-limited to 5 per day per identifier and free. It does not detail what happens after submission (e.g., confirmation), but the provided information is sufficient for a 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 two sentences plus a rate-limit note, all relevant and front-loaded. No superfluous content; every sentence adds necessary context.

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

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

The description is complete for the tool's purpose, covering all essential aspects: purpose, usage, content rules, and constraints. It could mention submission handling but is adequate given the tool's simplicity.

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

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

Schema coverage is 100% with detailed parameter descriptions. The description adds value by specifying that the message should describe what was tried and exclude the end-user's prompt verbatim, enhancing the schema's guidance.

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 tool is for sending feedback to the Pipeworx team, listing specific categories (bug reports, feature requests, missing data, praise). It clearly distinguishes the tool from siblings that are data query or manipulation 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 clear when-to-use guidance, specifying allowed feedback types and content guidelines. It mentions rate-limiting but does not explicitly contrast with sibling tools like ask_pipeworx, leaving some ambiguity about 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?

Annotations already mark it as read-only, idempotent, non-destructive. The description adds context about data source (CF analytics-engine), privacy (no PII), and caching (5min-1h).

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 total, front-loaded with what is returned. Every sentence provides necessary 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?

For a simple read-only tool with one parameter and no output schema, the description is complete: explains output, data source, privacy, caching, and use 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 covers the single parameter 100% with enum and description. The description adds value by explaining the behavior of different window lengths (shorter vs longer).

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 top tools, top packs, and total call volume over a window. It distinguishes itself from siblings by focusing on aggregate trending data.

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?

Three explicit use cases are provided (discovering hot data sources, confirming canonical choice, aligning use case with demand). Exclusions are not explicit, but guidance is strong.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds context about how it searches and groups markets, and what it returns. No contradictions, and it provides useful behavioral details beyond 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 well-structured with a clear front-loading of purpose and two modes. Each sentence adds value without extraneous information. It is appropriately sized for the complexity.

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

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

Given no output schema, the description adequately explains what the tool returns (ranked opportunities with trade direction and reasoning). It covers both modes and their trade-offs, making it complete for an agent to invoke correctly.

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

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

Schema coverage is 100% with basic descriptions. The description adds meaningful context, explaining how each parameter is used and providing examples. It bridges the gap between raw parameter names and real-world 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 finds arbitrage opportunities by checking monotonicity violations. It distinguishes two modes (event and topic) and explains the resource (Polymarket markets). This differentiates it from siblings like polymarket_edges.

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 explains when to use each mode, with an example of cross-event mode catching cases single-event misses. It provides clear guidance on choosing between them.

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 details internal behavior: scanning top markets, grouping by asset, fetching price history once, computing model probability via a lognormal model, and ranking by |edge|. Annotations already mark it as read-only and non-destructive, but the description adds valuable operational context (e.g., one-time price fetch, grouping logic) that helps an agent understand side effects and data freshness.

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 six sentences long, each serving a purpose: purpose, model details, process, output, and intended use. It is front-loaded with the core action. Could be slightly more concise by combining model and process sentences, but overall 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 has three optional parameters, no output schema, and moderate complexity, the description fully explains what it does, how it works internally, and what it returns (top N edges with trade direction). It is self-contained for an agent to understand the tool's capabilities without additional documentation.

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?

All three parameters have descriptions in the input schema (100% coverage), so the description adds little beyond restating defaults and ranges. The minimal edge filtering and volume window are already clear from the schema. The description's mention of 'Top N edges' aligns with the 'limit' parameter but does not provide new semantic meaning.

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

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

The description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market prices. It specifies the model used (lognormal from FRED and coinpaprika) and the output (ranked by edge magnitude with trade direction). This differentiates it from sibling tools like 'polymarket_arbitrage' which likely focuses on cross-platform discrepancies.

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 frames the tool for 'what should I bet on today' and mentions it avoids manual paging through hundreds of markets. While it does not explicitly list when not to use it or name alternatives, the use case is clearly implied and the context is sufficient for an agent to decide when to invoke 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?

The description adds valuable behavioral context beyond the annotations: it explains the typical spread range (2-25pp), the reason for discrepancies (different participant pools), and the return format (leg-by-leg prices and spread). Annotations already indicate read-only and idempotent, and the description aligns with them. No contradiction.

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, clearly separating the two modes and explaining the return format. It is slightly verbose with explanatory context (e.g., 'that delta is a real arb signal') but every sentence adds value. It could be tighter, but overall it is effective.

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 lack of an output schema, the description fully explains the return values: each venue's leg-by-leg prices (in raw probability 0-1) and the spread in percentage points. It also covers both input modes and their overrides. The tool is moderately complex but the description leaves no obvious 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?

The input schema has 100% coverage with descriptions for all three parameters. The description reinforces the two modes and explains how parameters interact (topic vs explicit overrides). This adds some meaning beyond the schema, but since the schema already covers parameter details, the baseline is 3.

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

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

The description clearly states the tool's purpose: compute cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes itself from sibling tools like 'polymarket_arbitrage' by specifying the cross-venue nature and the real arbitrage signal. The verb 'spread' combined with the context of two venues makes the purpose unambiguous.

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

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

The description provides clear usage context: it explains when to use the tool (for arbitrage signals between venues) and describes two distinct modes (topic shortcuts or explicit custom pairings). While it doesn't explicitly state when not to use it or mention alternatives, the explanation of modes guides the agent on appropriate invocation.

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 behavior: retrieving by key or listing all. It does not specify return format or error handling, but for a simple key-value retrieval, the description is adequate.

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

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

Two sentences, no fluff. Each sentence adds distinct information: retrieval method and usage context. Efficiently 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 (one optional parameter, no output schema, no nested objects), the description is complete enough. It covers the core functionality and usage hint. Minor gap: no mention of what happens if key doesn't exist.

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 describes the 'key' parameter with 100% coverage. The description adds context that omitting the key lists all memories, which is a key behavioral insight not in the schema. This adds 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 verb 'Retrieve' and the resource 'memory by key', and distinguishes between retrieving a specific key and listing all memories. This effectively differentiates it from sibling tools like 'remember' (store) and 'forget' (delete).

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

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

The description explicitly says when to use it ('to retrieve context you saved earlier') and provides the alternative action (omit key to list all). It also implies when not to use it (e.g., for storing, use 'remember'). This gives clear 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?

With no annotations, the description fully covers behavioral traits: parallel fan-out to multiple sources for company type, return format (structured changes, count, URIs), and the safe read-only nature implied by 'list changes'. 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?

Three concise sentences: purpose, detailed behavior (fan-out, date formats, return), and usage context. No filler, front-loaded with the core action.

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

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

Given no output schema, the description adequately outlines the return structure. It covers parameters, sources, and use cases. Minor gap: no details on the 'structured changes' schema, but sufficient for agent 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?

Adds meaning beyond schema by clarifying date formats (ISO/relative), providing examples, and explaining the value parameter can be ticker or CIK. Schema already covers basic descriptions, so the description enhances usability.

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

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

The description clearly states the verb ('what's new') and resource ('entity'), specifies the entity type and data sources, and distinguishes from siblings like entity_profile and compare_entities by focusing on changes over time.

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 usage examples ('brief me on what happened with X' and 'change-monitoring workflows'), guiding the agent on when to invoke. Does not explicitly list when not to use 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 provided, so description carries full burden. It discloses persistence behavior (authenticated vs 24-hour anonymous) and implies non-destructive nature. No contradiction since annotations absent.

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

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

Two concise sentences: first explains function, second gives usage guidance. Every sentence adds value with no fluff.

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

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

Given simple tool with no output schema and only two parameters, description covers purpose, usage, and persistence behavior. Minor gap: does not mention if value is overwritten on same key, but schema hints at that.

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 description does not add parameter details beyond the schema's descriptions. Baseline 3 is appropriate as schema does the work.

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 stores a key-value pair in session memory, specifying what it saves (intermediate findings, user preferences, context) and differentiates from siblings like 'recall' and 'forget'.

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 it (save context across tool calls) and notes persistence differences (authenticated vs anonymous). However, it doesn't explicitly mention when not to use it or alternatives like 'recall' for retrieval.

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 must carry behavioral info. It describes the return format (ticker, CIK, company name, resource URIs) and states it's a read-like operation. It does not disclose auth requirements, rate limits, or error conditions, but adds some value over 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 three sentences, each adding value. Purpose is front-loaded with clear action and scope. No redundant or irrelevant phrases. 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 has only two parameters and no output schema, the description covers the purpose, input formats, output contents, and benefit. It is self-contained for an agent to understand invocation and expectations.

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 100% coverage for both parameters. The description adds value by explaining the value parameter can be ticker, CIK, or name, with examples. It clarifies the enum for type (v1 supports company). This goes beyond the schema's basic 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 resolves entities to canonical IDs across Pipeworx data sources. It specifies the verb (resolve), resource (entity), and scope (single call). It distinguishes itself from sibling tools like ask_pipeworx and ct_* tools by focusing on entity resolution.

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: for resolving a company entity using ticker, CIK, or name in a single call. It highlights benefit (replaces 2-3 lookup calls). It lacks explicit exclusions or 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?

Annotations already mark it as read-only, open-world, idempotent, non-destructive. Description adds behavioral details: probes each entity, ranks by score, surfaces most/least recognized, and outputs score/confidence/signal density. 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?

Two sentences, front-loaded with purpose, followed by additional detail. 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 no output schema, description explains return values (ranked list with score, confidence, signal density). Also mentions relation to sibling ai_visibility_check. Complete for its 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 has 100% coverage, so baseline is 3. Description adds valuable context: first entity is treated as subject for narrative, and models parameter requires _apiKey for anthropic. This goes beyond 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 compares AI visibility across multiple entities side-by-side, uses specific verb 'compare', and distinguishes from sibling ai_visibility_check which checks single 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?

Explicitly states it's useful for competitive AI-marketing audits and gives an example question. Mentions probing each entity with ai_visibility_check, providing clear usage context.

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

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

No annotations provided, so description carries full burden. It clearly describes input type, supported domains, and return values (verdict, structured form, actual value with citation, percent delta). However, it lacks details on potential limitations like case sensitivity 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?

Single paragraph of ~60 words, highly information-dense with no redundancy. Every sentence adds value: scope, domain, output, and efficiency comparison.

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 one parameter, no output schema, and a specific domain, the description fully covers what an agent needs to know: domain, input format, output details, and use case distinction. No 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?

Only one parameter 'claim' with 100% schema description coverage, so baseline is 3. Description adds value by clarifying supported claim formats and the output it generates, enhancing 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?

Description clearly states the tool fact-checks natural-language claims against authoritative sources, specifically company-financial claims via SEC EDGAR+XBRL. It lists supported metrics and output types, distinguishing it from sibling tools that are not fact-checking focused.

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 it supports company-financial claims for public US companies, indicating when to use. Also mentions it replaces 4-6 sequential agent calls, providing context for efficiency and suggesting when it might be preferable to alternatives.

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