Who Gho

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

Adds model details, key passing, payment model, and return format beyond readOnly/idempotent annotations. 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?

Two sentences with clear front-loading of purpose. No 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-param tool with no output schema, description covers return format, optionality, and disambiguation context. 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?

Adds meaning to all 4 parameters: default for models, API key explanation, context purpose. Schema coverage is 100%, so baseline is 3, but description adds significant value.

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

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

Description uses specific verb 'probe' and resource 'LLMs for what they know about a business/brand/product/topic', clearly distinguishing from siblings like scan_competitor_ai_presence and entity_profile.

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

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

States use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and notes default model vs Anthropic with BYO key. Does not explicitly mention when not to use among siblings.

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

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description discloses how the tool behaves internally: it routes to one of 5,699 tools across 1,491 verified sources, fills arguments automatically, and returns structured answers with stable pipeworx:// citation URIs. It also notes 'works on every tier, one fast call', which is useful operational context. 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 front-loaded with the key directive and is dense with useful routing information. It is long, but nearly every sentence earns its place by preventing misuse or improving tool selection. The example list is somewhat lengthy but concretely demonstrates the breadth of acceptable questions.

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 one-required-parameter tool with no output schema, the description is remarkably complete: it covers scope, citation behavior, relationship to siblings, fallback guidance, and example queries. The agent has everything needed to decide when to invoke it and what to pass.

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

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

Schema description coverage is 100%, so the schema already documents the question parameter and its aliases. The description adds illustrative examples of valid questions, which aids understanding, but it does not add parameter-level semantics beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description opens with a strong directive ('PREFER OVER WEB SEARCH') and immediately enumerates concrete domains: SEC filings, FDA drug data, FRED/BLS, patents, real estate, weather, clinical trials, news, stocks, and more. It clearly names the routing behavior and differentiates from siblings like ask_pipeworx_grounded and deep_research.

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?

Usage guidance is explicit and actionable: use whenever the user asks 'what is', 'look up', 'find', 'get the latest', etc., and even when web search could answer. It also states when to step up to ask_pipeworx_grounded and deep_research, and when not to step up — for breaking news, ask_pipeworx already handles it. This is exemplary routing 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 mark it read-only and idempotent, and the description adds valuable conditional runtime behavior: candidate routing improvements are 'enabled live whenever one is under test,' currently no candidate is active since a specific date, and 'results are compared against the stable router.' It also warns 'Falls back to nothing' to prevent it from being mistaken for a stub. 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 densely packs beta status, current parity with ask_pipeworx, usage instruction, and the non-stub caveat into a few substantive sentences. Details like the exact retirement date are extra but informative, making the description slightly long yet still well-structured and front-loaded.

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

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

For an alias of a known sibling with one required natural-language parameter, the description is complete: same arguments, same response shape, current behavioral state, and exact usage. No output schema exists, but referencing ask_pipeworx's response shape fills that gap as long as the sibling definition is visible to 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?

All six parameters are aliases for question and schema description coverage is 100%, so the schema already carries the semantics. The description adds 'same arguments' and 'same response shape' but no parameter-specific detail beyond that, 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?

Description identifies it as the beta version of ask_pipeworx, an 'identical universal router' over 5,699 tools, and clarifies that it is a 'full working router, just the experimental edge.' This makes the ask-a-question routing purpose clear and distinguishes it from stable ask_pipeworx by beta status, though it does not contrast 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?

It gives explicit use direction: 'Use it exactly like ask_pipeworx when you want the newest routing' and explains the current state (no candidate active) means identical behavior. It doesn't name exclusions or the grounded alternative, but the conditional is sufficient for an agent to decide when to invoke this beta variant.

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, but the description adds substantial behavioral context: it only extracts answers from the tool result, returns verbatim evidence, and offers a structured refusal contract with enumerated refusal reasons. It also discloses the extra LLM call cost and the grounding constraint, going well beyond 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 dense but every sentence earns its place: it covers the core behavior, routing, return contract, refusal reasons, usage scenarios, and cost trade-off. It is front-loaded with the most important purpose and differentiator, then flows logically into when and how to use it.

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?

There is no output schema, so the description correctly takes on the burden of explaining the return shape, including the success object and the explicit refusal object with possible refusal_reason values. It also covers when to use the tool, how it differs from the sibling, and its cost, making it complete for an agent to invoke it correctly.

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

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

The input schema has 100% description coverage, including the primary 'question' parameter and all aliases. The description does not add parameter-level semantics beyond 'natural language question', but the schema already fully documents the parameters, so a baseline of 3 is appropriate.

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

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

The description clearly identifies this as a hallucination-resistant answer mode and distinguishes it from ask_pipeworx by emphasizing evidence-grounded extraction and explicit refusal behavior. It names the sibling alternative and explains the differentiator, so an agent can select the right tool without opening schemas.

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

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

It explicitly states when to use: whenever answers will be quoted, cited, or acted on, and the agent must not invent facts, with concrete examples like financial verdicts and medical lookups. It also says to prefer ask_pipeworx for casual lookups and notes the extra LLM call cost, providing 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 indicate safe, read-only behavior. The description goes far beyond by detailing fan-out logic, classifiers, response shapes, resolver contract, parent event extraction, news fallback, safety short-circuits, closed market handling, illiquid spreads, and cancellation rule risks. 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 well-structured with clear section headings (CLASSIFIERS, FAN-OUT EXAMPLES, etc.), but it is quite long. Every sentence adds value, but it could be more concise without losing critical 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?

Despite the complexity of the tool and lack of output schema, the description covers all essential behaviors, edge cases, and response shapes, making it highly complete for agentic 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% with clear descriptions for all three parameters. The description provides examples of market input but does not add significant new semantic meaning beyond the schema. Baseline of 3 is appropriate.

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

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

The description clearly states it researches Polymarket bets by pulling Pipeworx data. It gives specific use cases ('should I bet on X') and differentiates itself from siblings by focusing on betting research and integrating multiple data sources.

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

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

The description provides explicit use cases and input formats (slug, URL, question text). It does not explicitly contrast with alternatives like ask_pipeworx or polymarket_edges, but the purpose is clear enough to guide 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?

Annotations already declare readOnly and idempotent. The description adds meaningful behavioral context: data sources (EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and citation URI inclusion. 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 moderately concise and well-structured, starting with example phrases. Every sentence adds unique information, though a minor redundancy like 'one parallel call' is present. Could be slightly tighter.

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

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

For a tool with no output schema, the description adequately sets expectations: data fields for each type, sorting, citations, and replacement of multiple lookups. It could specify the output structure more precisely but is largely complete.

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

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

Schema description coverage is 100%. The description adds value by providing concrete examples (tickers, drug names) and explaining what each entity type pulls, complementing the schema effectively.

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 comparison of 2–5 entities (companies or drugs), with specific data sources and examples. It distinguishes itself from siblings like entity_profile by explicitly recommending it over sequential 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?

The description provides explicit example queries ('which is bigger', 'rank these companies') and states 'ALWAYS PREFER over sequential single-pack lookups', giving clear when-to-use guidance and naming 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?

Beyond annotations, it discloses account/payment requirements, parallel tool delegation, gaps[] behavior with 'never invented', depth-specific re-iteration, contradictions[], citation_uri fetchability guarantees, semantic excerpting, and latency expectations. The openWorldHint is consistent with broad external data access; no contradiction exists.

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 densely packed; nearly every sentence carries unique operational value, from prerequisites to output packet details to latency. It is front-loaded with the most critical operational constraint (account/sign-in) and organized so an agent can extract usage rules quickly.

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

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

Given the tool's complexity—5,699 internal tools, parallel routing, multiple depth modes, output fields, citation behavior, and failure semantics—the description covers all decision-relevant aspects. There is no output schema, and the description compensates by specifying the findings packet's fields and gap behavior in enough detail for correct invocation and interpretation.

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 both params are self-documenting, but the description adds practical nuance: 'quick', 'standard', and 'thorough' behaviors are expanded with hop/gap-recovery semantics, and 'question' is clarified as accepting broad multi-part natural language ('decomposition is the point'). This goes beyond the schema's own descriptions.

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

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

The description uses a specific verb+resource: 'Grounded multi-source research across Pipeworx's 1491 STRUCTURED data sources... in ONE call.' It explicitly contrasts itself with open-web search and distinguishes its behavior from ask_pipeworx, making its role among siblings unmistakable.

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

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

The description gives explicit when-to-use and when-not-to-use guidance: 'Best for broad/multi-part questions over structured data', 'For a single lookup use ask_pipeworx', and 'For BREAKING... prefer ask_pipeworx.' It even covers the not-signed-in fallback, leaving no ambiguity about alternative 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by stating that results are 'ready to call directly, no second schema lookup needed', which is useful behavioral detail beyond 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 approximately six sentences, front-loaded with the core purpose, and efficiently covers key details like scope and output format. It is concise but could trim redundant alias mentions already in the schema.

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

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

Despite lacking an output schema, the description fully explains the return format (top-N tools with names, descriptions, schemas, and examples). It covers input, behavior, and output completely for the tool's complexity.

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

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

Schema description coverage is 100% with all six parameters described. The description adds minimal extra value beyond the schema (e.g., examples of queries). Baseline 3 is appropriate as the schema already does the heavy lifting.

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

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

The description explicitly states 'Find tools by describing the data or task' and details the return format (names, descriptions, schemas). It distinguishes this meta-tool from siblings that perform specific data operations, making the purpose unmistakable.

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 advises 'Call this FIRST when you have many tools available and want to see the option set', providing clear usage context. While it does not list when not to use it, the guidance is strong and practical.

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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds behavioral context: parallel call, fan-out across sources, patent soft-fail. It does not contradict 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 a single dense paragraph but front-loads common queries and explains functionality. No fluff, though bullet points could improve readability. Still efficient for an agent.

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

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

Given tool complexity (multiple sources, no output schema), the description lists returned fields and mentions patent limitation. Annotations cover safety. It is complete enough for an agent to use correctly.

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

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

Schema coverage is 100% with descriptions. The description adds clarification: type only 'company' for now, value must be ticker or zero-padded CIK, names unsupported. This reinforces schema and adds extra context.

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 in one parallel call, listing specific data sources and returned fields. It explicitly distinguishes from sibling tools by stating to prefer over chaining single-pack lookups for holistic views.

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

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

The description says when to use (holistic view of a US public company) and when not (if only a name, use resolve_entity first). It mentions limitations (patents API sunset, names not supported), but could be more explicit about alternatives for non-holistic queries.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true, so the description adds context by clarifying the reason for deletion ('clear sensitive data'). No contradictions, but the description doesn't add much beyond annotations beyond the 'why'.

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, front-loaded with purpose, and every word earns its place. No wasted information.

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

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

Given the tool's simplicity (one parameter, no output schema), the description covers what it does, when to use it, and suggests related tools. It is complete for an agent to select and invoke correctly.

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

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

Schema coverage is 100% and the description does not add meaning beyond what the schema provides. The 'by key' is already in the parameter description. Baseline score 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 the specific verb 'Delete' and identifies the resource as 'a previously stored memory by key', which is clear and distinguishes this tool from siblings like 'remember' (store) and 'recall' (retrieve).

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

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

The description explicitly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. It also mentions pairing with 'remember' and 'recall', providing context for 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 indicate safe, non-destructive operation. Description adds process details (fetches page, extracts title/description/links, emits standard markdown format) and output format, enriching behavioral understanding 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?

Two sentences: first defines core functionality, second lists practical uses. No extraneous content; efficiently conveys essential information.

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

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

For a simple tool with 2 parameters and no output schema, the description covers purpose, behavior, and use cases comprehensively, enabling correct selection and invocation.

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

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

Schema coverage is 100%, with clear parameter descriptions. The description does not add meaning beyond the schema, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb (generate), resource (llms.txt), and scope. It distinguishes from siblings like scan_competitor_ai_presence by focusing on file generation rather than 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?

Provides explicit use cases (client site indexing, own project drafting, competitor auditing) but lacks when-not-to-use or alternative tool mentions. Clear enough for selection given sibling 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?

While annotations already declare readOnly, openWorld, idempotent, and non-destructive hints, the description adds significant behavioral context: it discloses the name-resolution process, states that the response echoes what it resolved to, and details the year-fallback behavior with a flag. These are not present in the annotations, making the tool's runtime behavior more predictable.

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

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

The description is three sentences long, front-loaded with the tool's core purpose, then input details, then fallback behavior. Every sentence carries useful information without fluff or repetition, achieving high information density in a compact structure.

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 an output schema, rich annotations, and a detailed description covering name resolution and year fallback, the tool is well-specified. A minor gap exists: the schema marks only 'indicator' as required, but the description implies a country and year are always used without explaining behavior when they are omitted. This slight ambiguity prevents a perfect score.

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

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

The schema already provides 100% coverage with descriptions for all three parameters, so the baseline is 3. The description adds extra value by clarifying that plain names are accepted and resolved (e.g., 'measles immunization coverage' → a GHO code) and that the response says what it resolved to, which goes beyond the schema's simple 'name or code' statements.

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 opens with a specific verb-resource pair: 'Get World Health Organization health statistics for a country and year,' and enumerates the types of statistics (life expectancy, immunization coverage, etc.). This clearly distinguishes it from sibling tools like get_indicators or list_countries, which would list available indicators or countries rather than fetch actual data.

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

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

The description provides clear usage context by explaining that plain indicator and country names are accepted and resolved to WHO codes, and that a year fallback occurs when no data exists. It does not explicitly name alternative tools or state when not to use it, but the cited input patterns and fallback behavior strongly imply the intended use case.

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 destructiveHint=false. Description adds that it returns codes and names, and that omitting query lists all indicators, providing context 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?

Description is three sentences, front-loaded with the main purpose, and no wasted words. It efficiently conveys search/list behavior, return type, and relationship to get_data.

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 only one optional parameter and an output schema present, the description covers the tool's functionality fully: search/list, return of codes/names, and guidance to use get_data. No additional context is needed.

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 description and examples for the single parameter. The tool description reinforces the schema by showing example calls and explaining the omit-for-all behavior, but does not add new parameter constraints beyond what the schema already provides.

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

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

Description clearly states the tool searches or lists WHO Global Health Observatory indicators and returns codes and names. It distinguishes itself from sibling get_data by explaining that the indicator code is used with get_data to retrieve actual values.

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

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

Description provides clear context on usage: optional query for filtering, listing all if omitted, and directs to use get_data for values. However, it does not explicitly exclude other siblings 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds value by specifying the output fields and the relationship to get_data, but does not contradict 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: first defines purpose and output fields, second provides usage guidance. Efficient, front-loaded, no fluff.

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

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

Given no parameters and presence of an output schema, the description fully covers what the tool does and how to use its results. More than 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?

No parameters exist (schema has no properties, coverage 100%). Baseline 4 is appropriate; no need for description to add param 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 the tool returns WHO-recognized countries with specific fields (ISO code, display name, parent region code). It distinguishes itself from sibling tools by linking its output to the get_data tool.

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

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

Explicitly tells the agent to use the ISO codes as the country parameter in get_data, providing examples. Lacks explicit exclusion of when not to use, but the positive guidance is strong and sufficient.

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. Description adds that only active subscriptions are listed by default, which is consistent and adds context. No contradictions.

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

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

Two sentences, front-loaded with purpose and return fields, followed by usage guidance. No wasteful content.

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

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

Complete for a simple list tool with one optional parameter and rich annotations. Describes purpose, return fields, and usage scenario. No missing information.

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 clear description for the sole parameter. Description does not add additional meaning beyond the schema.

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

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

Clearly states the tool lists the caller's active subscriptions and specifies returned fields. Distinguishes from sibling tools subscribe 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?

States exactly when to use: to review monitored subscriptions before adding more or to find an ID for cancellation. Implicitly suggests not using it for other purposes.

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 only set readOnlyHint=false, offering little behavioral info. The description compensates richly by disclosing the claim_token flow, rate limit ('5 per identifier per day'), cost ('Free; doesn't count against your tool-call quota'), and processing cadence ('reads digests daily'). 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?

Though lengthy, every sentence carries unique information: purpose, usage criteria, exclusions, token mechanics, quota, and cost. It is front-loaded with the core action and efficiently structured, with no filler or repetition.

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 no output schema, the description fully covers the tool's behavior: what to report, how to report, how to retrieve status, constraints, and intended use. The optional nested context object is addressed indirectly via 'Describe the issue in terms of Pipeworx tools/packs,' which is sufficient for a feedback 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 the schema already documents each parameter. The description adds value beyond the schema by explaining the claim_token lifecycle ('pass it back later... to read whether it was fixed'), clarifying type semantics (bug/feature/data_gap/praise), and advising users to describe issues in terms of Pipeworx tools/packs rather than pasting prompts.

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 opens with a clear, specific action: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It enumerates feedback categories (bug, feature, data_gap, praise) and explicitly contrasts with sibling research tools, making the tool's purpose unmistakable.

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 when-to-use guidance: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' It also gives a clear exclusion: 'if the tool came from a different MCP server... file it with that server instead,' preventing misuse.

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. Description adds value by explaining data source (CF analytics-engine), no PII, and caching behavior (5min-1h). 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?

Roughly 100 words, well-structured with bullet points for use cases. Front-loaded with the return value and window options. Every sentence adds meaningful information without redundancy.

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

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

For a simple tool with one optional parameter and no output schema, the description fully explains what it returns, use cases, data source, caching, and privacy. 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 already covers the single optional parameter with enum and description. The main description adds nuance: shorter windows for hot trends, longer for steady-state demand, providing extra 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?

Clearly states the verb 'returns' and specifies the resource: top tools, top packs, and total call volume over a window. Distinguishes from siblings by focusing on trend discovery rather than general Q&A or research.

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 (discovering hot data sources, confirming canonical tools, aligning use cases). Does not mention when not to use or alternatives like discover_tools, but the use cases provide clear guidance.

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

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

Annotations declare readOnlyHint, idempotentHint, destructiveHint, and the description adds rich behavioral context: scanning process, partition filter, semantic similarity check, fill check via polymarket_fill_risk, and response structure. 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 thorough and well-structured, starting with the main purpose and then detailing each mode. It is somewhat lengthy but each sentence adds value. Minor verbosity prevents a perfect score.

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 modes, filters, fill checks), the description is remarkably complete. It explains algorithms, response fields, edge cases (e.g., placeholder fraction), and references a related tool for custom sizing. No output schema, but description covers return values adequately.

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

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

Schema coverage is 100%, and the description adds significant meaning beyond the schema, including example event slugs, seed questions, and explanations of how each parameter triggers different modes. It enhances understanding beyond the schema's property 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: finding arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It differentiates three modes (no args, event, topic) with specific use cases, making the purpose unmistakable.

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 each mode: no args for trending scan, event for single-market partition check, topic for cross-event scanning. It also explains the semantic anchor and partition filter, though it doesn't explicitly state when not to use the tool. Clear context but lacks 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 indicate read-only, idempotent, non-destructive behavior, and the description adds valuable behavioral context: 1-hour KV-level caching, detailed response structure (by_segment, diagnostics), and explanation of edge calculations including slippage and Kelly fractions. 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: purpose first, then model families, response format, parameter knobs, and diagnostics. Every sentence provides necessary detail. Could be slightly more concise, but the structure aids comprehension.

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

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

The description covers the tool's purpose, behavior, parameter usage, response structure, caching policy, and diagnostic information. Despite lacking an output schema, it describes all key response fields (by_segment, fed_candidates, diagnostics) and edge attributes. 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 coverage is 100%, but the description adds significant meaning beyond the schema: explains why min_kelly does not filter partition arbs and how min_partition_leg_kelly works, provides practical advice for max_spread_pp and min_liquidity, and clarifies default behaviors. This transforms the schema from a mere specification into a usable guide.

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

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

The description clearly states the tool scans top Polymarket markets and returns opportunities where Pipeworx data disagrees with market price, with a specific use case 'what should I bet on today'. It distinguishes from siblings like polymarket_arbitrage by focusing on disagreement-driven opportunities and providing detailed model families.

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

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

The description provides clear context on when to use the tool ('what should I bet on today') and suggests parameter knobs to filter tradeable edges. However, it does not explicitly state when not to use it or compare with alternative tools like polymarket_arbitrage.

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 substantial behavioral detail beyond the annotations (readOnlyHint, etc.), explaining the response structure (tracked, expired, snapshot_dates), computation of trend and decay, and the cache-miss behavior causing gaps. This enriches the agent's understanding effectively.

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 dense and informative, starting with a leading question and then explaining response structure. While slightly long, every sentence adds value, though it could be slightly more streamlined.

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 compensates by detailing the response objects (tracked, expired, snapshot_dates) and computation details. It also covers limitations, making it fairly complete for an agent to understand the tool's output.

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

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

Schema coverage is 100%, so baseline is 3. The description adds default values and constraint details (max 30 for days, default window) that are not fully covered in the schema, providing extra context for parameter 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 uses specific verbs ('edge persistence and decay telemetry') and clearly answers 'how long has this edge existed and is it shrinking?', effectively distinguishing it from sibling tools like polymarket_edges by focusing on time-series analysis rather than just current edges.

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

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

The description provides clear context for when to use this tool, contrasting fresh vs. old wide edges, and includes LIMITS about history depth. However, it does not explicitly name alternative tools or state when not to use it.

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

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

Annotations indicate read-only, idempotent, non-destructive. The description adds substantial behavioral context: explains risks (partial fill → unhedged position), output fields (top_of_book, slippage, verdict), and mode-specific behaviors. 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?

Well-structured with clear headings (SINGLE-MARKET, BASKET) and front-loaded main purpose. Slightly verbose but justified by complexity of two modes. Every sentence adds value, though some detail could be condensed.

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, so description must detail return fields. It does so comprehensively for both modes (verdict, capture_ratio, thin_legs, etc.). Covers parameters, risks, and usage context fully, making the tool self-contained for an agent.

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

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

Schema description coverage is 100%, baseline 3. The description adds context beyond schema: explains side defaults for basket (auto based on partition sum), size_usd interpretation for basket ('settlement notional — shares per leg, each paying $1 at resolution'). These add meaningful value.

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

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

The description clearly states the tool checks 'realizable-vs-theoretical edge against live CLOB order-book depth' and distinguishes between single-market and basket modes. It specifies exact use cases (buy_yes, sell_yes, etc.) and explicitly ties to sibling tools like polymarket_arbitrage. The verb 'check' and resource 'edge against order-book depth' are specific and differentiated.

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: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It warns about thin books and partial basket fills converting arb to directional risk. Alternative tools are named, and context for when to use is clearly stated.

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, openWorld, idempotent, non-destructive. Description adds substantial context: response structure, safety fields (compatibility_warning, temporal_alignment), and interpretation of skipped counters. This goes well 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?

Description is dense and front-loaded with purpose. Every sentence adds value, though it is lengthy. Could benefit from bullet points for modes and safety fields, but overall 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?

No output schema exists, but description compensates with detailed explanation of response fields (leg-by-leg prices, matched spread, temporal_alignment, compatibility_warning). Covers both modes, error/warning cases, and interpretation. Fully 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% and descriptions of parameters are clear. Description adds value by explaining mode precedence (explicit overrides topic), providing example values, and detailing response interpretation. This enhances understanding beyond the schema alone.

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

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

Description explicitly states it computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes itself from single-venue siblings like polymarket_arbitrage by focusing on cross-venue comparison. The verb 'spread' and resource are specific.

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

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

Description provides two modes (topic and explicit) and warns that most pre-mapped topics return compatibility warnings, guiding the agent to not assume tradeability. However, it does not explicitly state when to use this tool versus alternatives, though the unique cross-venue nature makes it implicit.

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

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

Annotations already declare read-only, idempotent, non-destructive. Description adds scope details (anonymous IP, BYO key hash, account ID) and pairing with remember/forget, enhancing transparency.

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

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

Two sentences, front-loaded with main action, no wasted words. Efficient and clear.

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?

Low complexity tool (1 param, no output schema). Description covers purpose, usage, scope, and pairing 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 already covers the single optional parameter with clear description. Description adds context about keys being previously saved via remember, but no new semantic detail beyond schema.

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

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

Clearly states verb (retrieve/list) and resource (saved values/keys). Distinguishes from sibling tools like remember and forget. Provides concrete examples of use cases.

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?

Describes when to use (looking up previously stored context) and gives examples. Lacks explicit when-not-to-use or alternatives beyond pairing with remember/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?

The description states that setting mark_read:true flags events as read, which is a mutation of state. This contradicts the annotation readOnlyHint:true, which indicates no mutations. The contradiction is clear: the tool modifies data despite the read-only hint.

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 (four sentences) and front-loaded with the primary purpose. Every sentence adds relevant information without redundancy. No fluff.

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

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

Given the absence of an output schema, the description adequately covers the tool's behavior: what is returned (source, citation_uri, payload), filtering options, and the mark_read side effect. It is sufficient for an AI agent to understand and invoke the tool correctly.

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

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

The input schema already describes all five parameters with 100% coverage. The description adds value by explaining the effect of mark_read (flagging returned events read) and linking polling behavior to the since parameter. This goes beyond the schema's descriptions.

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

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

The description clearly states that the tool pulls fired events from the subscription feed, specifying key fields (source, citation_uri, payload) and the ability to filter by type and since. This distinguishes it from siblings like list_subscriptions, which list subscriptions rather than events.

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

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

The description provides context on when to use the tool (polls work fine) and mentions an alternative endpoint (registry.pipeworx.io/alerts.json) for scripts/dashboards. However, it doesn't explicitly state when not to use this tool versus other alert-related tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as true/false. The description adds valuable behavioral details: fans out to multiple sources, fallback logic (GDELT preferred, GNews when rate-limited), soft-fail for USPTO due to API sunset, and return structure. 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 a single, well-structured paragraph that efficiently conveys purpose, usage, sources, parameters, fallbacks, and return value. Every sentence adds essential information without redundancy or filler.

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

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

Given the tool's complexity (multiple sources, fallback, output schema missing), the description covers return structure (changes[] grouped by source, total_changes count, citation URIs), parameter details, and the alternative tool for static profiles. It is complete enough for an agent to invoke correctly.

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

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

Schema coverage is 100%, but the description enhances the schema by explaining the 'type' parameter's limitation to 'company', giving relative shorthand examples for 'since' (e.g., '7d', '30d', '3m', '1y'), and suggesting '30d' or '1m' for typical monitoring. It also clarifies that 'value' can be a ticker or CIK.

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 change feed for a company from multiple sources (SEC, GDELT/GNews, USPTO) in a single parallel call. It also distinguishes itself from the sibling 'entity_profile' tool by explicitly stating when to use that instead.

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

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

The description gives concrete example queries ('What's new with X', 'latest on Y', etc.) and provides clear guidance on when to use this tool versus 'entity_profile'. It also explains the fallback behavior between GDELT and GNews, helping agents decide when to expect each source.

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 idempotent and non-destructive. The description adds useful behavioral context: scoping by identifier, persistence differences (auth vs anonymous), and storage format (key-value). Does not explicitly state overwriting behavior, but idempotent hint implies it.

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

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

Four well-structured sentences with no redundancy. The main purpose is front-loaded, and each sentence adds incremental value. Ideal conciseness for a simple tool.

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 memory tool with two string parameters and no output schema, the description covers scope, persistence, and usage cycle. It could briefly mention overwriting behavior, but the overall completeness is high.

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 already describes parameters with examples. The description adds the pairing context but does not significantly enhance parameter understanding beyond the schema. Baseline of 3 is appropriate.

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

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

The description clearly identifies the tool's verb ('Save') and resource ('data'), provides concrete examples (ticker, address, preference), and distinguishes it from sibling tools by mentioning the memory ecosystem (recall, forget). This meets the highest standard.

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 ('when you discover something worth carrying forward') and pairs it with recall/forget for future actions. It lacks an explicit 'when not to use' statement, but the guidance is still strong.

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

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

Annotations already mark the tool as read-only, open-world, and idempotent, but the description adds significant behavioral detail: it cascades through multiple lookup endpoints, degrades gracefully when GLEIF/OpenFIGI are unavailable, labels identifiers with their source, and explicitly states unresolved identifiers under 'unresolved' rather than omitting them. This goes well beyond the annotation baseline.

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 information-dense. It front-loads example queries, then gives a clear purpose and usage directive, followed by structured type-specific details and caveats. While it could be tightened, each sentence adds substantive value, and the structure makes it easy to scan.

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 no output schema, the description fully covers return behavior: for companies, identifiers are labelled with source and unresolved ones are shown; for drugs, it returns RxCUI + ingredient + brand + citation. It also notes graceful degradation and the internal multi-source cascade, giving the agent a complete picture of expectations.

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

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

Schema description coverage is 100% for both parameters, each with useful descriptions (type enum, value formats). The tool description adds concrete examples (AAPL, CIK, ISIN, drug names) and the ISIN-to-LEI nuance for non-US issuers, which enriches the semantics beyond the schema alone.

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 opens with concrete user queries and explicitly states its core function: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It clearly distinguishes itself from siblings by advising 'Use FIRST whenever you have a name but need an ID,' and it enumerates supported types (company, drug) with specific identifier outputs.

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

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

The description gives explicit guidance on when to use the tool ('Use FIRST whenever you have a name but need an ID') and offers detailed context on accepted input forms (ticker, CIK, ISIN, company name; brand/generic for drugs). While it does not explicitly name alternative sibling tools or state when not to use it, the 'use first for name-to-ID' directive strongly implies the appropriate 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 provide readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral details: it probes each entity, ranks by score, surfaces most/least recognized, and returns scores, confidence, and signal density per entity. This provides context beyond 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 three sentences, each adding value. It is front-loaded with the core action and uses bullet-style listing for return values. 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 no output schema, the description adequately explains what is returned (ranked list with score, confidence, signal density). It covers purpose, process, and use case. Missing details like error handling or limits, but the schema handles constraints (2-8 entities). Overall sufficient.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds no new semantic details beyond what is already in the schema parameter descriptions. It references 'your brand + N competitors' which maps to entities, but adds no new information.

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

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

The description clearly states the tool compares AI visibility across multiple entities, uses ai_visibility_check internally, ranks by score, and surfaces most/least recognized. It specifically mentions competitive AI-marketing audits and distinguishes from the sibling ai_visibility_check by focusing on side-by-side 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 it is useful for competitive AI-marketing audits and gives an example question. It implies multi-entity comparison without explicitly contrasting with single-entity tools, but the context is clear enough for an agent to infer when to use it.

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

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

Beyond annotations (readOnly, idempotent), describes external fan-out, partial failures, and bundlephobia delay. Adds valuable behavioral context.

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

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

Description is comprehensive but every sentence adds value. Slightly verbose but well-structured with logical flow.

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

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

For a tool with no output schema and 2 params, the description thoroughly explains output format, behavior, failure modes, and scope. Complete and actionable.

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 3. Description adds minor nuance (scoped packages accepted, version defaults to latest) but doesn't significantly extend schema.

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

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

Clearly states it's a composite check for npm package feasibility, combining deps.dev and bundlephobia data. Specific verb and resource, distinct 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 says when to use (agent questions about safety/popularity/size), and notes limitations (NPM only, alternatives via deps.dev:version).

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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds technical details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flagging. This goes well beyond annotation information.

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

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

Single paragraph, well-structured: purpose first, then usage guidance, pairing, and technical details. Every sentence adds value, no fluff. 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?

Despite no output schema, the description explains return format (passages with character offsets and similarity scores), technical constraints (embedding model, window size, char cap), and integration with sibling tools. Sufficient for an agent to understand and invoke correctly.

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

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

Schema coverage is 100% with clear descriptions. The description reinforces parameter usage: 'pass the text you already pulled' for text, 'natural-language query' for query, and implies limit via 'top-N passages'. It adds real-world context but doesn't deeply extend 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 'Semantic search INSIDE a fetched record' and explains the core function: pass text and query, get back top-N passages with offsets and scores. It distinguishes itself from sibling tools by focusing on searching within already-fetched content, contrasting with tools that fetch or search externally.

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

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

Explicitly states when to use: 'Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter.' Also pairs with ask_pipeworx_grounded for a recommended workflow. No explicit when-not-to-use, but the context is strong.

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

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

Adds rich context beyond annotations: account requirement, phone verification, SMS cap (10/day), webhook auto-disable after 10 failures, signing secret handled once. No annotation contradictions found.

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 front-loaded purpose, then requirements, types, delivery. Each sentence adds value; slightly long but 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?

Covers requirements, all types with examples, delivery channels with caveats, failure modes. No output schema, but return value stated. Complete for a complex tool.

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

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

Schema coverage is 100%, but description adds extra meaning: examples for each type, delivery channel details, and limitations. Provides context beyond schema.

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

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

Specific verb+resource: 'Create a proactive monitoring subscription'. Distinguishes from siblings like list_subscriptions and unsubscribe by being the creation tool.

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

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

Clear context: when to use for proactive monitoring, requires Pipeworx OAuth account, supported types. No explicit when-not-to-use but implicit from sibling tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true and destructiveHint as false. The description adds context about returning example questions with tool/argument shapes and the effect of the topic parameter. 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 fairly long but packs essential information efficiently. It front-loads aliases and purpose, but could be slightly more concise. However, no trivial or redundant 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?

Despite no output schema, the description explains what is returned (category-bucketed example questions) and covers the single parameter thoroughly. It also provides usage guidance and behavioral context, making it complete for a simple, read-only 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 describes the optional topic parameter with specific values. The description adds meaning by explaining how omitting vs providing topic changes the output, and gives real examples like 'finance', 'pharma', 'betting'.

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: it returns category-bucketed example questions about Pipeworx's capabilities, serving as an onboarding entry point. It distinguishes itself from siblings like discover_tools and ask_pipeworx by its specific role in helping users learn what to ask.

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 the tool: first when you don't know what Pipeworx can do, or to learn how to call meta-tools. It also implies alternatives like ask_pipeworx for actual questions.

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 ownership enforcement and deactivation behavior beyond annotations. No contradiction; annotations align with description.

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 with no filler; front-loaded with the action and key constraints.

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 tool with one parameter and no output schema; addresses persistence of historical data.

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

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

Schema coverage is 100%, and description does not add new meaning beyond what schema provides for the single parameter.

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

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

Clearly states the verb 'cancel', the resource 'subscription by id', and includes ownership enforcement and deactivation behavior. Distinguishes from sibling tools like subscribe and list_subscriptions.

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

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

Explicitly states when to use (cancel own subscription) and mentions historical availability via recent_alerts. Does not explicitly state when not to use, but context is clear.

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

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

Annotations already declare the tool non-destructive, read-only, idempotent, and open-world. The description goes well beyond this by explaining the routing logic (SEC EDGAR fast path vs. grounded pipeline), the meaning of each verdict, the crucial distinction between 'could_not_verify' and 'unsupported', and the inclusion of verification_error details. This is rich behavioral context that annotations alone do not provide. 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 long, but every sentence adds meaningful detail: trigger examples, routing behavior, verdict definitions, error semantics, and a cost comparison. It is front-loaded with clear intent and then details. Slightly longer than necessary, but well-organized and without redundant fluff.

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

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

With no output schema, the description carries the full responsibility for explaining return values. It enumerates all possible verdicts, distinguishes error conditions (could_not_verify vs. unsupported), mentions evidence and citations, and explains the fallback routing across claim types. This is comprehensive and leaves no major ambiguity for an agent invoking the tool.

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

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

The schema already fully documents both parameters (claim and tolerance_pct) with examples and ranges. The description adds valuable semantics by explaining how tolerance_pct overrides implied wording, its intended use for hallucination detection, and the default cap of 5. This enhances what the schema provides, though the schema already carries much of the burden.

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

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

The description clearly states this is a natural-language claim verification tool with a specific verb ('verify') and resource ('factual claims'). It distinguishes itself from sibling research tools by specifying the claim-checking use case and by noting it replaces 4–6 sequential calls, making its purpose unmistakable.

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 'Use whenever the agent needs to check whether something a user said is factually correct' and provides natural-language trigger examples. It does not explicitly name alternative tools to avoid, but the context is clear enough that an agent could choose between this and similar research tools. Lacks explicit when-not guidance, so not a 5.

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