Urlscan Io

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

Beyond annotations (readOnly, idempotent, not destructive), the description adds valuable behavioral details: default model (Workers AI Llama-3.3-70b free), BYO key requirement for Anthropic, and the return structure (per-model score, confidence, signals, raw_response plus combined view). 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 two sentences that efficiently convey purpose, usage, parameters, and return value. Every sentence earns its place with 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?

For a 4-parameter, no-output-schema tool, the description provides examples of entity values, explains the default model and optional Anthropic probing, and outlines the return structure. It covers key usage scenarios without 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 coverage is 100%, so baseline is 3. The description adds context for the _apiKey parameter ('passed straight through to api.anthropic.com') and the context parameter ('helps disambiguate'), which adds 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?

Description clearly states the tool probes LLMs for knowledge about entities and returns visibility scores (0-100). Verb 'probe' and resource 'LLMs' are specific, and the tool distinguishes itself from siblings like ask_pipeworx or bet_research by focusing on AI visibility auditing.

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

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

Explicit use cases are provided ('AI-marketing audits, pre-launch brand checks, competitive monitoring'), and the description explains default vs. BYO key models. While it doesn't explicitly state when not to use, the context is clear enough for an AI 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: the tool picks the right data source, fills arguments, returns structured answers with stable citation URIs (pipeworx://). 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?

The description is relatively long but well-structured: the key directive 'PREFER OVER WEB SEARCH' is front-loaded, followed by explanation and examples. Every sentence adds value, though some trimming might be possible. It earns a 4 for being clear despite length.

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 the return format (structured answer with citations). It covers the scope of the tool and provides enough context for an AI to understand what types of questions are supported. Minor mention of error handling or limitations would improve 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 all parameters described as aliases for 'question'. The description adds examples and clarifies that the question can be in natural language, but does not add much beyond what the schema already provides. The examples are helpful for grounding 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 routes a natural language question to one of 3,756 tools across 887 verified sources and returns a structured answer with citations. It explicitly distinguishes itself from web search, providing a specific verb+resource: 'ask Pipeworx' for structured data queries.

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

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

The description explicitly says 'PREFER OVER WEB SEARCH' and provides a comprehensive list of when to use (SEC filings, FDA data, economic stats, news, etc.) and example queries. It gives clear context for use, contrasting with web search, and lists query triggers like 'what is', 'look up', 'find', etc.

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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds behavioral details: costs an extra LLM call, returns explicit refusals with specific reasons, explains extraction process. 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?

Well-structured and front-loaded, but slightly long. Every sentence adds value, though some pruning could tighten it. Still 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?

Despite no output schema, description fully explains return format for both success and refusal, including all possible refusal reasons. Covers trade-offs, routing complexity, and use cases comprehensively.

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

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

Schema coverage is 100% with all parameters described. Description adds no additional parameter-level meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states it's a hallucination-resistant answer mode for high-stakes reads, extracting answers only from tool results. It distinguishes from sibling ask_pipeworx by noting extra LLM cost and use case for factual integrity.

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

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

Explicitly says when to use: for answers to be quoted, cited, or acted on, and when not to: prefer ask_pipeworx for casual lookups. Provides examples like financial verdicts, legal claims.

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 the tool as read-only, idempotent, and non-destructive. The description adds extensive behavioral details: classifier list, fan-out examples per category, response shapes, resolver contract with confidence levels, parent event extraction, news field fallbacks, safety short-circuiting for low confidence and closed markets, wide spread handling, and resolution-rule risk parsing. No contradiction 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?

The description is long but well-structured, starting with purpose then moving to classifiers, examples, response shapes, and edge cases. Front-loading helps, but the density of information makes it less concise. Given the tool's complexity, the length is arguably justified, but it could be trimmed.

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

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

Despite no output schema, the description comprehensively documents return structure (result.market, result.analysis, result.evidence) and all edge cases (low-confidence matches, closed markets, wide spreads, cancellation rules, news fallbacks). No gaps remain 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 coverage is 100% with all three parameters described. The description adds value by explaining the market parameter with examples (slug, URL, question text), clarifying depth's quick vs thorough behavior, and detailing include_raw's default false with size implications and use cases. This enhances understanding beyond the schema.

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

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

The description clearly states that the tool researches a Polymarket bet by pulling Pipeworx data, accepting a slug, URL, or question text. It provides specific use cases ('should I bet on X', 'what does the data say about Y') and effectively distinguishes from siblings by focusing on a single bet research rather than broader edge finding.

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 the tool ('Use for...') and covers various scenarios through fan-out examples and edge cases. However, it does not explicitly state when not to use it or name alternative tools, though the context signals and sibling list imply differentiation.

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 declare read-only, idempotent, non-destructive. Description adds rich behavior: parallel call, fiscal year handling, sorted results, specific data sources (SEC EDGAR, FAERS), and citation URIs. 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?

Packs extensive information into a paragraph without wasted words. Example queries are front-loaded, and the structure flows logically from purpose to details. Could be slightly more organized (e.g., bulleted lists) but remains efficient.

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

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

Given no output schema, the description covers inputs, outputs (paired data + citations), behavior (parallel, sorting), and edge cases (off-calendar fiscal years). Missing explicit format of output but sufficient for agent decision-making.

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 meaning: examples of values (tickers for company, drug names), min/max items, and explains data retrieval per type. Goes beyond the schema's type 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 performs side-by-side comparisons of 2-5 companies or drugs, with specific example triggers and data sources. It distinguishes from siblings like 'entity_profile' by emphasizing parallel lookups.

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 prioritizes this tool over sequential lookups ('ALWAYS PREFER') and provides usage triggers like 'compare X and Y' or 'rank these companies'. It also explains what replaces (8-15 sequential lookups).

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 safe, read-only, idempotent behavior. The description adds critical behavioral details: parallel execution, pre-verified findings with evidence and citations, explicit gaps for unanswered facets, and the guarantee of never inventing information. No contradiction 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?

The description is comprehensive and well-structured, starting with purpose, then process, usage guidance, and prerequisites. While slightly verbose, every sentence adds meaningful information, and the structure aids readability.

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 thoroughly explains return values (findings packet with verbatim evidence, confidence, source, etc.), input parameters, prerequisites, and expected duration. Covers all aspects of tool usage comprehensively.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by explaining the depth enum mapping (quick=3, standard=5, thorough=8) and clarifying that the question parameter accepts broad/multi-part natural language queries.

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 core function: 'Grounded multi-source research in ONE call.' It details the process of decomposition and parallel routing to 3,756 tools, and distinguishes from sibling ask_pipeworx by noting that deep_research decomposes questions into sub-questions for broad or multi-part queries.

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

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

Explicitly provides when-to-use ('broad or multi-part questions') and when-not-to ('use ask_pipeworx for single lookups'). Also includes prerequisites (Pipeworx account, paid plan for 'thorough' depth) and expected time (15-60s).

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 declare readOnlyHint=true and idempotentHint=true. Description adds that returned tools include full schemas ready to call, eliminating schema lookups—useful behavioral detail 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?

Well-structured with purpose first, then usage, examples, and return details. Slightly verbose but every sentence adds value.

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

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

No output schema, but description fully explains what is returned (top-N tools with names, descriptions, schemas, examples) and that no second lookup is needed—complete for a discovery tool.

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

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

Schema coverage is 100% so baseline is 3. Description mentions query aliases and limit defaults but adds no meaning beyond what's in the schema 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 it finds tools by describing data or task, lists specific domains, and distinguishes itself from sibling tools by advising to call it first when many tools are available.

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

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

Explicitly says to use when browsing or discovering tools, with examples and a 'call this FIRST' directive. Lacks explicit guidance on when not to use it, but overall 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 declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds no behavioral context beyond this, but does not contradict annotations. With annotations present, the description's lack of additional detail results in a baseline score of 3.

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 short (3 words), which is concise but at the expense of clarity. It does not waste words, but it is under-specified for a tool with one parameter and no schema descriptions.

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 having an output schema and annotations, the description fails to explain what the tool returns or what 'domain' means in context. The tool is simple but the description is insufficient for an agent to understand its purpose and 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?

Schema description coverage is 0%, meaning the schema provides no description for the 'domain' parameter. The description does not add any meaning, format, or example beyond the schema's bare type 'string'. This fails to compensate for the missing 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 'Search by domain' is vague and does not specify what 'domain' refers to (e.g., internet domain, mathematical domain). It lacks differentiation from sibling tools like 'search' or 'discover_tools' that may also perform domain-related searches.

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

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

No guidance is provided on when to use this tool versus alternatives. With many similar sibling tools, the agent has no context to decide when 'search by domain' is appropriate.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds value by detailing the data sources (SEC EDGAR, XBRL, USPTO, news, GLEIF), the USPTO sunset soft-fail, and the return structure with CIK, filings URIs, fundamentals sorting, etc.

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 detailed and front-loaded with intent, but somewhat lengthy. Every sentence adds value, but it could be slightly more concise without losing clarity.

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 and lack of an output schema, the description comprehensively covers all return fields (CIK, filings, fundamentals, patents, news, LEI) and their ordering, ensuring the agent knows what to expect.

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%. The description explains the two parameters (type and value) with examples (ticker or CIK, not names) and clarifies that only 'company' is supported for type. This adds practical usage context beyond the schema.

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

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

The description clearly states the tool provides a full cross-source profile of a US public company, with example queries and a detailed list of data sources. It distinguishes itself from siblings like compare_entities and resolve_entity.

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 instructs to prefer this tool over chaining single-pack lookups for holistic views, and notes that names are not supported (use resolve_entity first). This provides clear when-to-use and when-not-to-use 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?

Annotations already declare destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data, which aligns with annotations and provides additional rationale for use.

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 action, no wasted words. Every sentence adds value.

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

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

For a simple destructive tool with one parameter and full schema coverage, the description covers purpose, usage guidance, and links to related tools. 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?

With 100% schema description coverage (the key property is described as 'Memory key to delete'), the description adds no extra meaning beyond what the schema provides.

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

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

The description clearly states the verb 'Delete' and the resource 'memory by key', distinguishing it from sibling tools like 'remember' and 'recall'.

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 tells when to use this tool: 'when context is stale, the task is done, or you want to clear sensitive data'. Also references sibling tools for pairing.

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 explains the process (fetches page, extracts title/description/key links), output format (standard llms.txt markdown), and usage details not covered by annotations (readOnlyHint, idempotentHint, etc.). 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?

Three sentences, front-loaded with purpose and audience, no wasted words. Succinct yet comprehensive.

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?

Covers input (URL and optional max_links), process (fetch, extract, format), output (single text blob), and use cases. Fully adequate given parameter count and lack of output schema.

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

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

Schema description coverage is 100%. Description adds meaningful example for 'url' parameter and clarifies output. Slightly improves upon schema, but 'max_links' remains as schema states.

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 it generates a production-ready llms.txt file for any URL, with specific verb and resource. Includes use cases that distinguish its purpose from sibling tools.

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

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

Explicitly lists three use cases (getting client's site indexed, drafting for own project, auditing competitor). Does not mention when not to use or alternatives, but guidance is clear and contextual.

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 declare readOnlyHint, idempotentHint, and destructiveHint=false, making the tool's safety profile clear. The description adds no additional behavioral context beyond this, but does not contradict the 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 extremely short and front-loaded, but it sacrifices informativeness for brevity. While concise, it does not adequately cover the tool's usage details.

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

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

Given the presence of an output schema and rich annotations, the description is too minimal. It does not explain what the tool returns, what kinds of IPs are accepted, or how results are structured.

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

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

Schema coverage is 0% and the description does not elaborate on the 'ip' parameter. No details about format (IPv4/IPv6), example values, or constraints are provided, leaving the parameter underspecified.

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 ('Search') and resource ('by IP'), making the purpose unambiguous. However, it does not differentiate from sibling tools like 'domain' beyond the resource type, missing a chance to clarify 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?

No guidance is provided on when to use this tool versus alternatives such as 'search' or 'domain'. The description does not mention context, prerequisites, or 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?

Annotations already declare readOnly, idempotent, non-destructive. Description adds return fields and mentions 'active subscriptions,' implying default filtering. It does not contradict annotations and adds useful 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 efficient sentences: first states function and output, second states use cases. No waste, 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 lists return fields. It covers purpose, usage, and parameter context. Could mention default active-only behavior explicitly, but overall complete for a simple list tool.

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

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

Schema coverage is 100%, so baseline is 3. Description does not add information about the parameter beyond what's in the schema; the parameter's effect is implied but not explicitly stated.

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 lists the caller's active subscriptions and specifies the returned fields (id, type, params, etc.). It distinguishes from siblings like subscribe/unsubscribe by being a list operation.

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

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

Explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells when to use it and contrasts with subscribe/unsubscribe.

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 indicate no special hints, but the description adds critical behavioral details not in annotations: rate-limited to 5 per identifier per day, free, and doesn't count against tool-call quota. 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?

The description is four sentences, well-structured, and front-loaded with purpose. Every sentence adds value: purpose, usage examples, constraints, and behavioral notes. No unnecessary words.

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

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

Given the tool's purpose as a feedback mechanism with no output schema, the description covers all necessary aspects: when to use, what to include, rate limits, and cost implications. It is complete for an agent to correctly invoke the tool.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the enum values (bug, feature, etc.) and providing context on how to write the message (be specific, 1-2 sentences, 2000 chars max). This goes beyond schema descriptions.

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

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

The description clearly states the tool's purpose: to send feedback about broken, missing, or needed features. It explicitly lists use cases (bug, feature/data_gap, praise) and distinguishes itself from sibling tools by being a feedback mechanism.

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 (for bugs, feature requests, data gaps, praise) and what not to do (don't paste end-user prompts). It also mentions that the team reads digests daily and that signal affects roadmap, helping the agent decide appropriateness.

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?

Adds valuable context beyond annotations: data source (CF analytics-engine), no PII, caching behavior (5min-1h). 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?

Concise, front-loaded with main function, then use cases, then data source details. No wasted sentences.

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 simplicity (one optional param, no output schema), the description covers all needed: returns, data source, caching, privacy. Complete without 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 coverage is 100% with one parameter. Description adds meaning by explaining the trade-off between short and long windows, enhancing the enumerated values.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a recent window. It uses specific verbs and resources, and is distinct from sibling tools like discover_tools or search.

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

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

Explicitly lists three concrete use cases (discovering hot data sources, confirming canonical tool, checking alignment). No explicit when-not-to-use or alternatives, but clear 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. The description adds rich behavioral context: requires one of event/topic, Jaccard similarity anchor, placeholder filter, fill check with theoretical vs realizable edge, and details on response structure. 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 lengthy but every sentence carries necessary detail for a complex arbitrage tool. It is front-loaded with the requirement and logically structured. Minor redundancy but acceptable given 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?

No output schema, but the description fully explains the response format (opportunities[], partition_check, fill_check). Covers both modes, edge cases (placeholder slugs, Jaccard similarity), and trade suggestions. Complete for a tool of 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%, baseline is 3, but the description adds substantial value: explains the semantic difference between event and topic, how to format slugs, and how each parameter drives the algorithm. This goes far beyond the schema's short descriptions.

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

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

The description clearly states it finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes between two modes (event and topic) and provides concrete examples, leaving no ambiguity about the tool's function.

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

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

Explicitly says when to use each mode: 'event' recommended for a specific market, 'topic' for cross-event scanning. Warns that calling with no args fails. Mentions alternatives like polymarket_fill_risk for custom sizing, and explains cross-event mode catches patterns single-event misses.

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 declare idempotentHint=true and destructiveHint=false. The description adds critical behavioral details: caching (KV-level 1h keyed on all knobs), the three segments with model descriptions, diagnostics for empty segments, and the 'Market moved' warning. There is 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 extremely detailed but overly verbose. It front-loads the purpose but then presents a dense block of text covering models, knobs, diagnostics, and caching. While information-rich, conciseness could be improved by breaking into sections or reducing redundant explanations.

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 (9 parameters, three segments, diagnostics, caching, edge calculations), the description covers all aspects thoroughly. It describes the response top-level structure even without an output schema. The only minor gap is the lack of explicit return type documentation, which is mitigated by the detailed description.

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

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

Schema coverage is 100%, so baseline is 3. The description dramatically enriches each parameter with usage context, defaults, and trade-off explanations (e.g., slippage_pp notes zero fees but bid/ask, min_partition_leg_kelly explains partition arbs have parent-level kelly=0). This adds significant 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 explicitly states the tool scans Polymarket markets to find opportunities where Pipeworx data disagrees with market price, built for 'what should I bet on today'. It clearly distinguishes its role from sibling tools like polymarket_arbitrage by focusing on edge detection across multiple model-driven segments.

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 extensive guidance on when to use the tool ('what should I bet on today') and explains the purpose of each parameter (e.g., min_liquidity, max_spread_pp for tradeable-edge filtering). It implies when not to use by mentioning that Fed bets are excluded from ranking. However, explicit references to sibling tools for alternative use cases could improve clarity.

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

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

Beyond annotations (readOnlyHint, etc.), the description adds significant behavioral context: history depth bounded by 60-day TTL, decay computed from daily closes (not intraday), and snapshot gaps from cache misses. No contradiction 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?

The description is well-structured: opens with a concise purpose statement, then details response fields, and ends with limitations. Every sentence adds value and is front-loaded with the key question. 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, the description comprehensively covers the response: three arrays (tracked, expired, snapshot_dates) with their fields, edge case explanations (gaps, TTL). It fully addresses 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?

With 100% schema coverage, baseline is 3. The description adds substantial meaning: explains 'days' as lookback with default and clamp range, 'window' as snapshot family with examples, and details the response structure including arrays and computed fields. This far exceeds schema-only info.

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

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

The description clearly states it provides edge persistence and decay telemetry from daily snapshots, answering a specific question about edge age and shrinkage. It distinguishes itself from siblings like polymarket_edges by focusing on time-series analysis.

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

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

The description explains when to use it (to differentiate fresh vs old edges) but does not explicitly state when not to use it or list alternatives. However, the context strongly implies that for current edge data, polymarket_edges is the appropriate 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?

Annotations already indicate safe, idempotent behavior (readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false). The description adds significant context: live order-book depth checking, ladder walking, and return values including slippage, fill details, and risk indicators like forced_directional_risk. No contradiction 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?

The description is well-structured with clear sections (SINGLE-MARKET, BASKET) and front-loaded purpose. While every sentence adds value, it is slightly verbose (e.g., repeating 'shares per leg; each share pays $1' could be condensed). Still, it remains effective and informative.

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

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

Given the tool's complexity (two modes, risk analysis) and lack of output schema, the description thoroughly explains return values, edge cases (thin books, partial fills), and usage context. It is complete for practical agent use.

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

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

Schema coverage is 100% (all 4 parameters described). The description clarifies parameter usage per mode (e.g., size_usd for basket interpreted as 'settlement notional S (shares per leg)'), default values, and behavior (e.g., side default auto in basket). This adds meaning beyond the schema.

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

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

The description clearly states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes, and differentiates from sibling tools like polymarket_arbitrage and 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?

Explicit usage guidance is provided: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains why this check is necessary (theoretical overround not capturable, partial fills cause directional risk).

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 goes beyond the annotations (readOnlyHint, idempotentHint) by detailing compatibility warnings for non-equivalent bet shapes, temporal alignment considerations, and the meaning of skipped cross-type/subtype counters. This gives the agent a clear understanding of edge cases and data reliability.

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

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

The description is verbose but well-structured with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS) and front-loaded with the core concept. Every sentence adds necessary detail, though it could be slightly trimmed without loss of clarity.

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 (cross-venue spread, compatibility checks, temporal alignment) and lack of output schema, the description comprehensively covers the response structure, safety fields, and edge cases. It leaves no major gaps for an agent to misinterpret.

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

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

With 100% schema coverage, the description adds extra value by explaining the two operational modes, listing all topic shortcuts, and clarifying that explicit parameters override the topic-mapped side. This enriches the schema's basic type descriptions and usage 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: computing cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies two distinct modes (topic shortcuts and explicit tickers), and the verb 'spread' plus the resource context differentiate it from sibling tools like polymarket_arbitrage, which likely focuses on single-venue arbitrage.

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 topic shortcuts vs explicit tickers and warns that pre-mapped topics often yield compatibility warnings, implying the tool is best for verifying actual equivalent bet shapes. However, it does not explicitly state when not to use this tool or suggest alternatives like polymarket_arbitrage or polymarket_edges for other use cases.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral details: scope by identifier and listing all keys when key is omitted, without contradicting annotations.

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

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

The description is concise with three sentences, front-loading the core action. It could be slightly more compact but is efficient.

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

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

Given only one parameter with full schema description, no output schema, and comprehensive annotations, the description fully covers the tool's behavior including scope and dual functionality.

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

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

Schema description coverage is 100% and already includes the information about omitting key to list all keys. The description adds minimal extra meaning beyond pairing and context 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's function: retrieve a value saved via remember or list all keys when omitted. It distinguishes itself from sibling tools like remember and forget by naming them explicitly.

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 guidance on when to use the tool: to look up stored context without re-deriving. It also mentions scoping and pairing with remember/forget, but does not explicitly state when not to use.

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

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

Annotations already indicate readOnly, idempotent, openWorld, and non-destructive. The description adds behavioral details: the effect of mark_read, the fields returned, and the polling compatibility, which goes beyond the annotations without contradicting them.

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 moderately concise at three sentences. It front-loads the core purpose and follows with details and an alternative usage note. Every sentence contributes value, though it could be slightly tighter without losing 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?

With 5 parameters (all optional), no output schema, the description covers what the tool returns, how parameters work, and provides an alternative access method. It is complete enough for the tool's relatively straightforward functionality.

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

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

Schema coverage is 100%, but the description enhances it by clarifying that 'type' filters by subscription type, 'since' expects an ISO timestamp, 'limit' range (1-200, default 50), and the effect of 'mark_read' and 'unread_only'. This adds practical meaning beyond the raw 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's purpose: 'Pull fired events from your subscription feed.' It specifies what it returns (alerts with source, citation_uri, raw payload) and distinguishes itself from siblings like list_subscriptions by focusing on retrieval of events rather than listing subscription types.

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 usage context: 'Polls work fine' and mentions an alternative endpoint for scripts/dashboards, indicating when to use the tool programmatically vs. direct HTTP access. However, it does not explicitly exclude other tools or state when not to use this tool.

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

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

The description adds significant behavioral context beyond annotations. It discloses the multi-source fan-out with fallback behavior (GDELT→GNews when rate-limited or 5xx), USPTO soft-fail due to API sunset, the `since` parameter accepting both ISO dates and relative shorthand, and the return structure (grouped changes, count, URIs). No contradictions with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false).

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 dense paragraph that efficiently conveys all necessary information without fluff. However, it could benefit from bullet points or clearer separation of concepts for improved readability. Still, every sentence adds value and there is 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 the tool's complexity (multiple sources, fallback logic, parameter options) and absence of an output schema, the description is remarkably complete. It specifies the return structure (changes grouped by source, total_changes, citation URIs), addresses edge cases (rate-limiting, API sunset), and explicitly differentiates from the sibling tool. No gaps remain.

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

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

Schema coverage is 100%, with descriptions for all three parameters. The description adds practical context: 'type' only supports 'company', 'since' accepts ISO ('2026-04-01') or relative ('7d', '30d', '3m', '1y') with recommended default, and 'value' can be ticker or CIK. While schema descriptions are adequate, the description enriches understanding with real-world usage 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 defines the tool's purpose: 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It lists concrete examples of queries ('What's new with X', 'latest on Y') and specifies the sources (SEC EDGAR, GDELT→GNews, USPTO), effectively distinguishing it from the sibling tool entity_profile, which is explicitly mentioned as the alternative for static profiles.

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 (e.g., 'What's new with X', 'latest on Y', 'updates on Acme') and provides an exclusion: 'Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.' This clearly guides the agent on tool selection.

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

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

Discloses scoping by identifier, persistence behavior (authenticated vs anonymous), and idempotent nature, going 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?

Three sentences, front-loaded with purpose, efficient usage guidance, and necessary behavioral details. 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?

Complete for a simple write tool: explains what, when, how long data persists, and how to use with siblings. 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?

Schema covers 100% of parameters with good descriptions; description adds no substantial new meaning beyond schema, earning baseline 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?

Clear verb+resource: 'Save data the agent will need to reuse later'. Distinguishes from siblings recall and forget explicitly.

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

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

Provides explicit guidance on when to use ('when you discover something worth carrying forward'), with examples and pairing instructions for recall/forget.

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 declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds significant context by explaining that each call cascades through several endpoints, replacing multiple manual lookups. This goes beyond annotations without 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: it starts with example queries, gives a clear usage instruction, then details each supported type. Every sentence adds value without redundancy. It is front-loaded with the core purpose.

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

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

Given the complexity of two entity types with different return formats and no output schema, the description thoroughly explains what each call returns (e.g., ticker+CIK+citation for company, RxCUI+ingredient+brand for drug). It also notes auto-disambiguation, making it complete for the 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%, so baseline is 3. The description adds extra meaning by clarifying that the 'value' parameter accepts ticker, CIK, or name for companies and brand/generic names for drugs, and mentions auto-disambiguation. This provides useful context 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 clearly states it resolves user-spoken names to canonical identifiers, provides example queries for each supported type, and distinguishes from sibling tools by indicating it is for when a name needs an ID. The verb 'resolve' accurately captures the action.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID.' This provides clear context for when to use. However, it does not mention when not to use it, such as when the ID is already known, but the instruction implies the condition. Could be slightly improved by adding 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?

Annotations already declare readOnly, idempotent, and non-destructive. The description adds only 'keyless', which is unclear. No additional behavioral context like return format or limitations.

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

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

Single sentence with no wasted words. However, it omits details that could be added without significant bloat.

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 presence of output schema and annotations, the description is minimally adequate. However, 'Full result' and 'keyless' remain unexplained, leaving room for confusion.

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

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

With 0% schema description coverage, the description adds crucial meaning: the uuid is a 'scan uuid'. This compensates for the schema gap, though it could still be more precise.

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 'Full result by scan uuid (keyless)' states the tool retrieves a result for a scan, but 'Full result' and 'keyless' are ambiguous. It differentiates from siblings only by the resource (scan) but lacks specificity.

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

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

No guidance is provided on when to use this tool versus siblings like 'search', 'recall', or 'domain'. No when/not or alternatives 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 (readOnlyHint, idempotentHint) indicate safe, non-destructive behavior. The description adds substantial context: it internally calls ai_visibility_check per entity, ranks results, and returns a structure with score, confidence, signal density. No contradiction 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?

Four well-structured sentences: summary, probing method, usage example, output description. No wasted words, each sentence earns its place.

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

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

Given no output schema, the description clearly states what is returned: 'ranked list with score, confidence, signal density per entity'. Annotations cover behavior. The description is complete for this 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 coverage is 100% with good descriptions. The description adds extra value by explaining that the first entity is treated as 'subject' for narrative and the rest as competitors, which is not in the schema. This goes beyond the baseline 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 uses specific verbs and resources: 'Compare AI visibility across multiple entities', 'Probes each entity with ai_visibility_check', 'ranks by score', 'surfaces which is most/least recognized'. This clearly distinguishes it from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison).

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 the use case: 'Useful for competitive AI-marketing audits', and gives a concrete example. However, it does not explicitly state when not to use this tool or name alternatives among siblings beyond referencing ai_visibility_check.

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 significant behavioral context beyond the annotations: it explains partial failure handling (graceful degradation, sources_failed list), the potential delay of bundlephobia's first measurement (5-30s), and the overall response structure. This complements the readOnlyHint, idempotentHint, and destructiveHint annotations without 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 concise for its complexity. It is front-loaded with the primary purpose, then covers usage, return format, limitations, and fallback behavior. Every sentence adds value, and the information is well-organized.

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 absence of an output schema, the description thoroughly details the return structure: summary block fields, per-advisory detail, links, and alternative versions. It also accounts for edge cases like partial failures and timeout, making it complete for agent decision-making.

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 has 100% coverage with descriptions for both parameters. The description adds extra context: scoped packages are accepted, and the version defaults to latest when omitted. This enriches the schema but does not reveal new parameters or constraints, so a high score is appropriate but not the maximum.

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 composite nature of the tool and its use case — a single call to evaluate whether to add an npm package. It lists specific checks (license, advisories, version history, bundle size) and distinguishes itself from any sibling tools by focusing on npm packages and combining deps.dev and bundlephobia.

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

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

Explicit usage guidance is provided: 'Use whenever an agent asks is X safe / popular / small or what does adding lodash cost me'. It also clearly states when not to use (non-npm ecosystems) and directs users to an alternative (deps.dev:version directly) for other package managers.

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, idempotent, non-destructive. Description adds 'keyless' context, but doesn't detail pagination or result behavior beyond what's implied by parameters.

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

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

The description is efficiently short, but lacks necessary detail about parameters and usage.

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 having output schema and rich annotations, the description is too minimal to fully guide an agent on parameter usage and result handling, especially with multiple sibling tools.

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

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

Schema description coverage is 0%, and the description provides no information about parameters. The description adds no value beyond the raw schema.

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

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

The description clearly states it searches past scans and is keyless, but does not differentiate from siblings like 'search_within' or 'recall'.

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

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

No guidance on when to use this tool versus alternatives, no context on prerequisites or 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?

Discloses internal mechanics: BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, 200K char cap with truncation and flagging. Annotations already indicate readOnly and idempotent, but description adds valuable context beyond them.

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

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

Concise (~150 words) and well-structured. Front-loaded with core purpose, then usage, then technical details. 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?

Given the complexity (semantic search with embeddings, chunking, truncation) and no output schema, the description is thoroughly complete. Covers limitations, output structure, and pairing with sibling tool.

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

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

Schema coverage is 100%, and description adds meaning: explains 'text' as document with max length, 'query' as natural-language with examples, 'limit' as max passages. Also describes output structure (offsets, scores) despite no output 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 'Semantic search INSIDE a fetched record,' specifying the verb (search) and resource (inside a record). It distinguishes from siblings like 'search' by emphasizing internal search and pairing with ask_pipeworx_grounded.

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 tells when to use: 'Use when the record is too big to cram into the prompt.' Provides an alternative workflow: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages.'

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 contradicts annotations: it describes a write operation ('submit ... scanning') while annotations declare readOnlyHint=true. Additionally, idempotentHint=true is questionable for a submission that likely creates duplicate scans. The description fails to disclose behavioral traits like result creation, authentication needs, or cost implications.

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

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

The description is a single sentence, but it is underspecified and lacks essential information. It does not effectively communicate the tool's functionality within a concise structure; it sacrifices completeness for brevity.

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

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

Given the tool has 7 parameters and is a create operation with authentication, the description is incomplete. It does not mention output format, error behavior, rate limits, or the fact that it creates a scan resource. Although an output schema exists, the description should provide context for the overall operation.

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 only 29% (2 of 7 parameters have descriptions). The description adds 'requires key' but does not specify which parameter holds the key (potentially _apiKey, which appears in examples but not properties). It provides no additional meaning for the other 5 undocumented parameters.

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

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

The description clearly states the verb 'Submit' and resource 'URL for scanning', with a prerequisite 'requires key'. However, it does not differentiate from sibling scanning tools like scan_competitor_ai_presence or scan_dependency, which could cause confusion.

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 only guideline is 'requires key', but there is no context on when to use this tool versus alternatives, when not to use it, or any prerequisites beyond the key. No exclusions or special cases 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?

Adds beyond annotations: returns subscription id, delivery channel details (email, sms with cap, webhook auto-disable), and account requirement. No contradiction 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?

Front-loaded purpose and efficient use of text; slightly long due to examples but no redundancy. Every sentence adds value.

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

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

Covers subscription types, params, delivery, and prerequisites. Missing return value details beyond subscription id, and no mention of the always-on feed retrieval, but adequate for a 3-parameter tool.

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

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

Schema coverage is 100%, so baseline is 3. Description adds examples for each subscription type and delivery options, enhancing understanding of parameters 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 'Create' and resource 'proactive monitoring subscription', distinguishing it from sibling tools like list_subscriptions and unsubscribe.

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

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

Explicit prerequisite (Pipeworx OAuth account) and supported subscription types with examples. Lacks explicit when-not-to-use or comparison to alternatives like recent_alerts, but provides clear 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds value by disclosing that the tool returns example questions (not actual data) with tool+argument shapes drawn from the live catalog. It doesn't mention any potential limitations or errors, but the added context is sufficient for safe behavior understanding.

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 longer but front-loaded with key purpose and usage. Every sentence adds value, listing categories, behavior, and parameter guidance. Minor redundancy in listing categories could be trimmed, but overall it is well-structured.

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

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

For a simple tool with one optional parameter and no output schema, the description is remarkably complete. It explains the return type, categories, and parameter usage, and even ties it to the broader tool ecosystem. No gaps remain for an agent to understand its function.

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 'topic' described. The description adds example values ('finance', 'pharma', 'betting') but doesn't significantly expand beyond what the schema already provides. Baseline 3 is appropriate.

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

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

The description uses specific verbs and resources: it is the 'onboarding entry point' that 'returns category-bucketed example questions' with exact tool+argument shapes. It clearly distinguishes itself from sibling tools by stating it should be used first when the agent doesn't know what Pipeworx can do, and it helps learn how to call meta-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 explicitly states when to use the tool: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also instructs on invocation: 'Call with no arguments for the full spread, or pass `topic` to focus.' This provides clear guidance on usage context and 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?

Annotations already note idempotent and non-destructive; description adds ownership enforcement and clarifies deactivation vs deletion, linking to recent_alerts. Adds significant value 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?

Two sentences, front-loaded with verb and resource, 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 single parameter, no output schema, description covers purpose, constraint, behavior, and reference to sibling. Fully adequate.

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

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

Schema covers id fully (100%), description adds that id comes from subscribe, linking tools. This extra context justifies above baseline.

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

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

States explicit action 'Cancel' on resource 'subscription by id', and distinguishes from siblings by noting ownership enforcement and deactivation behavior.

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 ownership constraint and consequence on historical data, but does not explicitly contrast with list_subscriptions or subscribe. Nonetheless, 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 indicate readOnly, openWorld, idempotent. Description adds behavioral context: returns verdict with structured form, value, citation, delta, and replaces multiple calls. 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 somewhat long but well-organized, starting with natural-language queries, then purpose, domain, and returns. Every sentence adds value, but could be slightly more concise.

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

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

Despite no output schema, description details the return structure (verdict, structured form, value, citation, delta) and notes limitations (v1 scope). Lacks error handling info, but sufficient for typical use.

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

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

Schema description coverage is 100% for the only parameter (claim). Description augments with examples and explains the type of input expected, adding meaning beyond the schema.

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

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

The description clearly defines the tool as a natural-language claim verifier against authoritative sources, with specific examples and domain (company-financial claims for US public companies). It distinguishes itself from siblings by its unique capability.

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?

States explicitly to use when checking factual correctness of a claim. Provides domain scope (company-financial, US public) but does not list when not to use or alternative tools, though the sibling list is extensive.

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