Worms

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, destructiveHint=false. The description adds value by explaining the default free model, the BYO key mechanism for Anthropic, and the per-model return structure, exceeding what annotations provide.

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

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

The description is three sentences, front-loaded with the core action, and every sentence adds unique value. No fluff or redundant information.

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

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

Given no output schema, the description adequately explains return values (per-model {score, confidence, signals, raw_response} + combined view). All four parameters are described sufficiently for an agent to 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?

Schema description coverage is 100%, but the description adds meaning beyond the schema: it explains default values ('workers-ai' for models), the purpose of _apiKey (BYO key for Anthropic), and how context helps disambiguate, enriching semantic understanding.

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

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

The description specifies a clear verb ('Probe') and resource ('LLMs for what they know about a business / brand / product / topic'), and distinguishes from siblings by focusing on AI visibility scoring. It provides specific output details and 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?

The description explicitly states when to use the tool (AI-marketing audits, pre-launch brand checks, competitive monitoring) and provides context on default vs paid model probing. However, it does not mention when not to use it or directly contrast with sibling tools like scan_competitor_ai_presence.

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 a read-only, idempotent, non-destructive tool. The description adds valuable behavioral context: it routes to 5,528 tools, fills arguments, returns stable pipeworx:// citation URIs, and works on every tier with one fast call. This goes beyond the structured fields without contradicting them.

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

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

The description is lengthy but well-structured, starting with an emphatic usage directive and then covering scope, examples, and alternatives. While the trigger-phrase list is slightly redundant with the examples, the length is justified for a default entry-point tool that needs to steer agent behavior clearly.

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

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

The tool has a simple input schema but high conceptual complexity (routing across thousands of tools). The description thoroughly covers when to use it, what it returns (structured answers with citations), and how it relates to sibling tools. Despite the lack of an output schema, the description gives enough information for the agent to select 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?

Schema coverage is 100% with a clear description of the 'question' parameter and its aliases. The description itself doesn't add much parameter-level detail beyond examples, but the schema already documents the parameter adequately. The examples in the schema support natural language usage, so the 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 the tool's purpose: it routes factual questions to the appropriate tool among thousands of sources and returns a structured answer with citations. It differentiates itself from siblings like ask_pipeworx_grounded and deep_research, specifying when each should be used.

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 'PREFER OVER WEB SEARCH' guidance, lists trigger phrases ('what is', 'look up', 'find', 'get the latest', etc.), and provides concrete examples. It also describes when to step up to alternatives (ask_pipeworx_grounded for hallucination-resistant evidence, deep_research for broad/multi-part questions), making usage boundaries 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 readOnly, openWorld, idempotent, and non-destructive. The description adds context: that candidate routing improvements may be live, that no candidate is currently active, and that it 'falls back to nothing' — meaning it is a fully functional router, not a stub. This is useful 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 long and front-loaded with the core purpose. It explains the beta nature, current status, and usage guidance without padding. Every sentence contributes.

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 is a beta alias of ask_pipeworx, the description covers the essential context: identical behavior, experimental status, current equivalence, and that it is a full working router. No output schema exists, but the reference to same response shape as ask_pipeworx is sufficient for the high-level use case.

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

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

Schema coverage is 100% (all six parameters are aliases for the same question string). The description adds no parameter-specific detail, but the schema already fully documents the single semantic parameter. Baseline 3 is appropriate.

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

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

The description clearly states it is a beta version of ask_pipeworx, a universal router with the same 5,528 tools, same arguments, and same response shape. This distinguishes it from siblings like ask_pipeworx and ask_pipeworx_grounded by emphasizing the experimental routing improvements.

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 it 'exactly like ask_pipeworx when you want the newest routing' and explains that results are compared against the stable router. It also notes current equivalence due to no active candidate, but stops short of listing when not to use it or comparing to ask_pipeworx_grounded.

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 read-only/open-world/idempotent annotations, the description discloses important behavioral traits: the extra LLM call cost, the structured success/refusal responses with specific refusal reasons, and the constraint of using only the tool result. This enriches the agent's understanding far beyond the annotations alone.

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

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

The description is front-loaded with the core purpose, then details the mechanism, returns format, use cases, and cost/alternative advice. Every sentence adds value and there is no redundancy; the length is justified by the tool's complexity.

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

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

The description is comprehensive: it explains the routing behavior, the hallucination-resistant mechanism, the exact return structure on success and failure, specific refusal reasons, and cost/alternative guidance. With no output schema, the description fully carries the burden of explaining what the agent can expect, and it does so thoroughly.

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 provides 100% coverage by documenting all parameters as aliases for 'question'. The description adds no additional parameter-level meaning, so the baseline score of 3 applies. It does imply the question should be in natural language, which is already stated in the schema.

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

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

The description clearly identifies the tool as a 'hallucination-resistant answer mode' that extracts answers using only the tool result content. It distinguishes itself from the sibling 'ask_pipeworx' by emphasizing groundedness and explicit refusal behavior, making the purpose specific and unambiguous.

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

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

Explicitly states when to use (high-stakes reads, quoted/cited/acted-on answers) and when not to (prefer ask_pipeworx for casual lookups). Also implies the cost tradeoff, giving clear decision criteria for selecting this tool over 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?

The description richly details behavioral traits beyond annotations: resolver contract with match confidence, parent event extractor, news fallback handling, safety short-circuits for low-confidence and closed markets, liquidity warnings, cancellation rules, and response shapes. Annotations already declare readOnly, idempotent, openWorld, non-destructive, but the description adds extensive context that an agent needs to use the tool safely and correctly. 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 front-loaded with a clear summary sentence. Every section adds necessary detail for a complex tool. The structure could be improved with sections or bullet points, but it is still efficient and not wasteful. Minor point off for density.

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 input types, fan-out logic, response shapes, edge cases, safety mechanisms) and the absence of an output schema, the description is remarkably complete. It covers inputs, process, outputs, error states, and warnings. An agent can understand all important aspects without additional documentation.

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

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

All three parameters have descriptions in the schema (100% coverage). The description adds value by explaining input formats for the 'market' parameter, the trade-off for 'depth' with examples, and the purpose of 'include_raw' with guidance. This extra context justifies a score above the baseline of 3.

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

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

The description clearly states the tool's purpose: research a Polymarket bet by resolving, classifying, and fanning out to data sources. It uses specific verbs like 'research', 'pull', 'resolve', 'classify', and 'fan out'. However, it does not explicitly differentiate from sibling tools like 'polymarket_edges' or 'polymarket_arbitrage', so it loses a point.

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: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. It also gives examples of fan-outs for different bet categories. However, it does not specify when NOT to use this tool or suggest alternative tools for edge detection, so it is not a full 5.

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

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

Beyond annotations (readOnlyHint, etc.), the description reveals critical behaviors: correct handling of off-calendar fiscal years, sorting results by primary metric, and specific data pulled for each entity type. 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 a single dense paragraph that front-loads common query patterns and packs essential information (data sources, sorting, efficiency claim) without redundancies.

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 the response contains (paired data + citation URIs) and how results are ordered. It covers input, behavior, and output expectations comprehensively given the tool's complexity.

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

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

With 100% schema description coverage, the description adds significant meaning: it explains what data each 'type' value retrieves, the expected format for 'values' (tickers/CIKs for company, drug names for drug), and the min/max constraints.

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: side-by-side comparison of 2–5 companies or drugs in one parallel call. It lists common query patterns and specifies data sources (SEC EDGAR for companies, FAERS for drugs), making it distinct from sibling tools like 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?

The description explicitly advises to prefer this over sequential single-pack lookups when comparing entities, and quantifies the efficiency gain (replaces 8–15 sequential lookups). This provides clear when-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 mark the tool as read-only, open-world, idempotent, and non-destructive. The description goes far beyond by disclosing response structure (findings packet with evidence, confidence, source, fetched_at, gaps[], contradictions[]), citation resolvability rules, semantic excerpting behavior, hop behavior for depth levels, and time expectations. No contradictions with annotations exist.

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

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

The description is a long wall of text (≈300 words) but is front-loaded with the most critical operational constraint (account requirement and alternative). Each sentence carries substantive information, though some depth details duplicate the input schema. It could be tightened with bullet points, but it remains organized and purposeful.

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 and high operational complexity, the description fully compensates by detailing return values (verbatim evidence, confidence, source, fetched_at, citations, gaps[], contradictions[]), time bounds, account tiers, and the internal routing/decomposition process. It covers all critical aspects needed for an agent to use 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?

Schema coverage is 100% for both parameters, so the baseline is 3. The description adds meaningful semantic nuance beyond the schema by explaining how depth levels resolve multi-step questions in one call and clarifying that 'quick' is single-hop while 'standard' and 'thorough' add gap recovery and contradiction scans—details that reinforce but also extend the schema descriptions.

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

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

The description opens with a precise verb-resource pairing: 'Grounded multi-source research across Pipeworx's 1455 STRUCTURED data sources' and clearly explains the decomposition/parallel-routing mechanism. It distinguishes itself from siblings by explicitly contrasting with ask_pipeworx and noting it is NOT open-web search.

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

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

The description provides explicit when-to-use guidance: 'Best for broad/multi-part questions over structured data' and gives concrete alternatives: 'For a single lookup use ask_pipeworx' and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'. It also states the account prerequisite and tells non-signed-in users to use ask_pipeworx instead, covering exclusions and fallbacks.

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, destructiveHint=false. The description adds value by explaining the return format: 'Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' It also mentions limit behavior. There is 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 purpose, then usage, then return format. It is moderately concise; every sentence adds value. Could be slightly shorter but remains focused and well-structured.

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

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

Given the tool's complexity (6 parameters, mostly aliases) and the rich annotations, the description is fully complete. It explains purpose, usage, return format, and parameter semantics without needing an output schema. All aspects required for correct invocation are covered.

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. The description adds meaning by explaining the 'query' parameter as a 'natural language description' with concrete examples (e.g., 'analyze housing market trends') and lists aliases. The limit parameter is described with default and max values. This goes beyond the schema's bare 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: 'Find tools by describing the data or task.' It lists specific domains (SEC filings, FDA drugs, etc.) and explicitly distinguishes from siblings by advising 'Call this FIRST when you have many tools available and want to see the option set.' No ambiguity.

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 when-to-use guidance: 'Use when you need to browse, search, look up, or discover what tools exist' and advises calling it first when many tools are available. The list of example tasks (SEC filings, financials, etc.) clarifies the use case. While it doesn't explicitly state when not to use, the 'first' directive implies it's for discovery before direct calls, which is 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 declare readOnly, idempotent, not destructive. Description adds detailed behavioral traits: parallel fan-out across sources, specific return fields, patents API sunset and soft-fail, news fallback logic.

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 front-loaded with example queries and purpose. It is detailed but not overly verbose; every sentence adds value. Could be slightly more structured but effective.

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

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

Given complexity and no output schema, description fully covers return values (CIK, filings with URIs, fundamentals, patents, news, LEI), limitations (patents may fail, name format), and internal behavior (parallel calls, fallbacks).

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%. Description adds context beyond schema by explaining value format (zero-padded CIK), that names are unsupported, and the type enum limitation. This adds meaningful guidance.

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

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

The description clearly states it provides a full cross-source profile of a US public company in one call, lists example queries, and distinguishes from siblings by preferring over chaining 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 explicitly tells when to use (holistic view) and when not to (use resolve_entity for names), with clear alternatives.

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

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

The description states 'Delete', which matches the destructiveHint annotation. The idempotentHint is implied but not elaborated. The description adds minimal behavioral context beyond what annotations already provide.

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

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

The description is extremely concise with two sentences and a pairing hint. Every sentence adds value without unnecessary verbosity.

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

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

Given the simple tool (one param, no output schema), the description completely covers purpose, usage, and context. Nothing essential is missing.

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

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

With 100% schema coverage, the description adds little beyond 'by key'. The schema already describes 'Memory key to delete'. The 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 'Delete a previously stored memory by key', which is a specific verb and resource. However, it does not explicitly differentiate from sibling tools like 'remember' and 'recall', though it suggests pairing with them.

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

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

The description provides explicit use cases: 'when context is stale, the task is done, or you want to clear sensitive data.' It also suggests pairing with sibling tools. It lacks explicit when-not-to-use or alternative tool 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 read-only, idempotent, non-destructive behavior. The description adds that it fetches pages, extracts title/description/key links, and emits standard markdown format, providing useful behavioral 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?

The description is concise with four sentences, each serving a purpose: general capability, process description, output format, and use cases. Front-loaded with the primary action and no wasted words.

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

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

Given no output schema, the description fully explains the output as a single text blob ready for deployment. It covers the extraction elements and format, making the tool's functionality complete for 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%, so parameters are well-documented there. The description does not add significant new meaning beyond the schema, only implying the extraction of title/description/key links. Baseline 3 is appropriate.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb, resource, and output format. It also distinguishes itself by mentioning AI crawler indexing, which differentiates it from sibling tools like ai_visibility_check or scan_competitor_ai_presence.

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 lists use cases: getting a client's site indexed, drafting for own project, or auditing competitors. It implies when to use but does not explicitly state when not to use or provide alternatives, though the context is clear enough.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds value by specifying the ordered output array from root to tip with fields {rank, name, aphia_id}. The term 'Keyless' is somewhat ambiguous but still informative.

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

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

The description is concise (two sentences) and front-loaded with the main action and output. The second sentence 'Keyless' is terse but may be unclear; slightly more explanation could help.

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 parameter, no output schema, and strong annotations, the description provides all necessary context: what it does, how the result is structured, and the ordering. 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?

The input schema already fully describes the parameter 'aphia_id' with type and example. The description does not add additional semantic meaning beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states the action ('walk the full taxonomic lineage') and the specific resource (WoRMS AphiaID), with details on the output format. It distinguishes well from sibling tools like 'get_common_names' or 'search_species.'

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

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

No guidance on when to use this tool versus alternatives, such as 'resolve_entity' or other lineage tools. No context on prerequisites or exclusions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the description's mention of 'keyless' adds useful behavioral context about authentication. It does not contradict annotations. No additional behavioral details beyond that.

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

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

The description is extremely concise with one sentence that packs essential information: the action, resource, input, scope, and auth requirement. No wasted words.

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

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

Given the tool's simplicity (one parameter, no output schema, full annotations), the description is sufficient. It covers what the tool does, its input, and the keyless feature. No missing critical 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?

The schema already fully describes the single parameter (aphia_id with type and example), and the description adds context about 'across languages' but not parameter-level detail. Schema coverage is 100%, so 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 the purpose: fetching common names for a WoRMS AphiaID across languages. It uses a specific verb ('Get') and resource ('common names'), and distinguishes from siblings like 'search_species' or 'get_classification' by its unique focus on vernacular names and the 'keyless' mention.

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 adequate context for when to use the tool (when needing common names for an AphiaID), but does not explicitly exclude other scenarios or mention alternatives among siblings. However, the 'keyless' hint is useful.

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

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

Annotations already indicate read-only and non-destructive behavior. Description adds specific return fields, providing additional transparency beyond annotations.

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

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

Two sentences are concise, front-loading purpose and return fields, followed by usage guidance. No unnecessary text.

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?

Tool is simple with one optional parameter; description covers purpose, return fields, and usage context. No output schema 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?

Input schema covers the single parameter with full description. Description does not add extra 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 that the tool lists active subscriptions for the caller and specifies the fields returned. Distinguishes from siblings like 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?

Explicitly advises using this tool before adding more subscriptions to review what is monitored, or to find an id for cancellation.

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

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

Annotations are all false, providing no safety/behavior hints, so the description carries the full burden. It discloses the claim_token workflow (filing without an account returns a token that can be used to read status later), rate limiting (5 per identifier per day), quota independence, and team digest review. This goes well beyond the structured fields.

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 earns its place: purpose, triggers, exclusions, token mechanics, rate limits, and cost. It starts with the most important information and progressively details the workflow. No fluff 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?

With no output schema, the description must explain return values, and it does: 'Filing without an account returns a claim_token' and how to use it. It also covers rate limits, quota, and team response, making the tool fully self-contained. Given the parameter complexity and lack of output schema, this is complete.

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

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

Schema coverage is 100%, with thorough descriptions for each parameter. The tool description adds workflow context not present in the schema, such as how claim_token is obtained and used ('Filing without an account returns a claim_token; pass it back later...') and content guidance for message ('don't paste the end-user's prompt'). This enriches parameter understanding without redundancy.

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 direct, specific statement: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It immediately distinguishes the tool from siblings by scoping it to Pipeworx-served tools and enumerating exact use cases (bug, feature/data_gap, praise). This is a model of clear purpose.

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 when-to-use triggers: '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 an explicit exclusion: tools from other MCP servers. The guidance on identifying Pipeworx tools ('Pipeworx tool names are the ones this connection lists') and on not pasting end-user prompts rounds out the 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 readOnly, openWorld, idempotent, non-destructive. Description adds data source (CF analytics-engine), no PII, caching details (5min-1h), and output structure. No contradictions.

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

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

Four sentences, front-loaded with purpose and resource, then use cases, then technical details. No redundant or wasted text.

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

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

Given no output schema, description covers purpose, use cases, parameter, data provenance, privacy, and caching. Fully adequate for a simple tool with rich annotations.

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?

Single parameter 'window' with 100% schema coverage. Description adds context: shorter windows for hot trends, longer for steady-state, default is 24h. Enriches schema beyond enum values.

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

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

Description clearly states the tool returns top tools, top packs, and total call volume over a window. Verb 'Returns' and specific resources distinguish it from siblings like ask_pipeworx and discover_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?

Lists three explicit use cases and explains window selection semantics. Lacks explicit when-not-to-use guidance but the use cases effectively differentiate from 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 declare readOnlyHint=true and idempotentHint=true. The description goes far beyond by detailing the internal checks (monotonicity, partition sum, semantic anchor, placeholder filter, fill check against CLOB depth) and the exact conditions for a valid arbitrage signal. It discloses the real-world constraint that a trade should not be executed if the edge exists only at last-trade.

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

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

The description is front-loaded with the core purpose and then organized into clear sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). Every sentence provides necessary information for correct tool use; there is no fluff. Despite its length, it is well-structured and efficiently conveys complex logic.

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 complex arbitrage detection tool with no output schema, the description fully covers input modes, internal algorithms, response structure (opportunities[], partition_check, fill_check details), and even cross-references polymarket_fill_risk for custom sizing. It addresses limitations (placeholder filters, similarity thresholds) and provides complete guidance for an AI agent to use 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 has 100% description coverage for both parameters. The description adds significant context beyond the schema: it explains that event expects a Polymarket event slug or full URL, and topic expects a seed question. It gives concrete examples (e.g., 'fed-decision-may-2026', 'Strait of Hormuz traffic returns to normal') and clarifies the behavior of each mode, making parameter meaning much richer.

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 verb+resource: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It immediately distinguishes the tool's purpose and its two main modes (event/topic), providing a specific, actionable definition that differentiates it from sibling tools like polymarket_edges or polymarket_fill_risk.

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

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

Explicit guidance is given for when to use each parameter: 'Call with NO args for a trending_scan... pass event for the strongest per-event partition_check, or topic for a themed cross-event scan.' It recommends event mode for specific markets and warns against trading when the fill check shows no realizable edge. This covers when, when-not, and alternatives within the tool.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the tool is safe and idempotent. The description adds extensive behavioral details: model families, edge calculation (e.g., 'edge_pp_net after slippage'), Kelly sizing, liquidity filters, 24h-move warning, caching at KV level for 1 hour, and diagnostic counters. 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 very detailed but verbose, running several paragraphs. It is structured with high-level purpose first, then segments, then parameters, then response structure, and caching. However, the length may be excessive for an agent to parse quickly. Key information is present, but conciseness could be improved.

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 9 parameters and no output schema, the description thoroughly explains the response structure (by_segment with three categories, fed_candidates/fed_note, diagnostics) and key fields (edge_pp_net, kelly_fraction, etc.). It also notes caching and filter behavior. While it doesn't list all possible response fields formally, it provides sufficient contextual completeness for an agent to understand the 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?

All 9 parameters in the input schema have descriptions, achieving 100% schema coverage. The description adds extra context, such as explaining that 'min_kelly' skips small opportunities, 'slippage_pp' provides typical slippage values, and 'max_spread_pp' and 'min_liquidity' are tradeable-edge filters. 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?

The description clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It also specifies the use case ('what should I bet on today') and differentiates from siblings by mentioning Pipeworx data and the three segments (model_driven, structural_arbitrage, concentrated_longshot). This distinguishes it from sibling tools like polymarket_arbitrage or polymarket_edge_tracker.

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 the intended use ('what should I bet on today') and states it avoids paging hundreds of markets. However, it does not explicitly guide when NOT to use this tool or suggest alternatives among siblings. For example, it doesn't mention when to use polymarket_arbitrage instead. Usage guidelines are implied but not comprehensive.

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, destructiveHint=false, idempotentHint=true. The description adds behavioral details: it reads from daily snapshots, has a 60-day TTL, decay numbers from daily closes not intraday, and the response structure. No contradictions.

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

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

The description is dense but efficient; every sentence adds value. It is front-loaded with purpose, then examples, then args, then response. Slightly long but justified by complexity.

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

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

With no output schema, the description fully explains the return structure (tracked[], expired[], snapshot_dates[]) and their fields. It also notes limits (60-day TTL). Complete for a read-only information tool.

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

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

Schema description coverage is 100%. The description adds context (e.g., window as 'snapshot family') but does not provide significant additional meaning beyond the schema, which already explains default and enum values. The description's response structure details help understand parameter impact but are not parameter semantics.

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

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

The description clearly states the tool's function: 'edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers specific questions ('how long has this edge existed and is it shrinking?') and provides a comparative example distinguishing fresh vs aged edges. This differentiates it from sibling tools like polymarket_edges.

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

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

The description explains when to use this tool: to check edge persistence and decay, with a concrete example. It implies usage context but does not explicitly state when not to use it or mention alternatives like polymarket_edges for current edges only.

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 safe, read-only, idempotent behavior. The description adds rich behavioral details: walks the ladder, returns specific fields, warns about forced directional risk, and explains partial fill 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 comprehensive but verbose (nearly 300 words). While well-structured and front-loaded with purpose, it could be more concise by trimming redundant explanations. Still functional but not maximally efficient.

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

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

Given no output schema, the description fully explains return values for both modes, including risk indicators like thin_legs and forced_directional_risk. It covers all necessary context for an agent to correctly invoke and interpret results.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant value by explaining default values, clamp ranges, mode-dependent side behavior, and interpretation of size_usd (max spend vs target proceeds), justifying an above-baseline score.

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

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

The description clearly states it's a realizable-vs-theoretical edge check, specifies two modes (single-market and basket), and directly links its use to sibling tools like polymarket_arbitrage and polymarket_edges, differentiating it effectively.

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 required parameters (market or event), gives clear when-to-use instructions (before arbitrage signals or trades above $500), and explains consequences of not using it (theoretical overround uncapturable, partial fills create directional risk).

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

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

Adds extensive behavior beyond annotations: explains safety fields, compatibility_warning cases, temporal alignment, and leg comparison counters. Annotations already indicate read-only, idempotent, non-destructive, which are consistent.

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 section headers, but slightly verbose; could trim some repetitive warnings. Nonetheless, every sentence adds value.

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

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

Given no output schema, the description thoroughly documents response fields (leg prices, spread, compatibility_warning, temporal_alignment, counters), making the tool's output fully predictable.

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

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

Schema coverage is 100%, and the description adds value by listing the 10 topic shortcuts, explaining override logic for explicit tickers, and clarifying how parameters interact.

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

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

The description clearly states it computes cross-venue spread between Kalshi and Polymarket for the same resolving question, and distinguishes from sibling tools like arbitrage and edge trackers.

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 describes two modes (topic shortcuts and explicit tickers) and warns that most pre-mapped topics return compatibility warnings, guiding appropriate use.

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

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

Annotations already indicate read-only and idempotent behavior; description adds scoping details and pairing with remember/forget, adding value beyond annotations.

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

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

Concise single paragraph, front-loaded with main action, every sentence adds value.

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

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

Despite no output schema, description covers usage, scoping, and pairing; could mention return format but adequate given simplicity.

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

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

Schema coverage is 100%; description explains that omitting key lists all saved keys, adding meaning beyond the schema.

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

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

The description clearly states the tool retrieves a value saved via 'remember' or lists all keys, using specific verbs and distinguishing it from sibling tools like 'remember' and 'forget'.

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

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

Explicitly says 'Use to look up context the agent stored earlier' and explains scoping, but does not state explicit when-not-to-use scenarios.

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

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

The description contradicts the annotation readOnlyHint: true by stating that setting mark_read:true flags events as read, a write operation. While the description itself is transparent about the mutation, the annotation conflict makes the tool definition inconsistent.

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

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

The description is four sentences long, front-loads the main purpose, includes examples (e.g., 'sec_8k'), and avoids unnecessary details. Every sentence adds value.

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

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

The description covers return fields, filtering, polling, and the mark_read effect, but doesn't mention pagination beyond limit, ordering (beyond 'most recent'), or how unread_only works. The annotation contradiction reduces confidence in completeness.

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

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

The schema covers 100% of parameters with descriptions, so the baseline is 3. The description adds slight value by explaining the consequence of mark_read, but does not enhance other parameters like type, since, limit, or unread_only.

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 begins with 'Pull fired events from your subscription feed,' which clearly states the verb and resource. It also mentions the specific fields returned and distinguishes itself from sibling tools by focusing on alerts from the subscription feed.

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 usage context by explaining filtering options and the effect of mark_read, but lacks explicit comparisons to sibling tools like list_subscriptions or recent_changes. It does mention an alternative direct endpoint for scripts, which helps differentiate usage.

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

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

Discloses multi-source fan-out, GDELT→GNews fallback, USPTO soft-fail, return structure (changes[], total_changes, citation URIs), and since parameter behavior. Adds value beyond annotations.

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

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

Single dense paragraph but efficiently packs information. Could be slightly more structured with bullet points, but every sentence adds value.

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

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

Covers all important aspects: parameter formats, source behaviors, return structure, and sibling tool differentiation. No output schema exists, so description adequately explains what the agent receives.

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%, yet description adds practical guidance: suggests '30d' or '1m' for typical monitoring, clarifies ticker/CIK usage, and confirms only 'company' type supported.

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?

Uses specific verbs like 'change feed for a company' and clearly distinguishes from sibling entity_profile by stating when to use it instead. Examples of natural language queries make it easy to match.

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

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

Explicitly provides query examples ('What's new with X'), describes the single parallel call behavior, and directs to entity_profile for static needs. Includes fallback logic and soft-fail conditions.

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 provide idempotentHint and destructiveHint, but description adds context about scoping by identifier, persistence differences (authenticated vs anonymous), and storage mechanism. 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?

Five sentences, each adding value. Front-loaded with main purpose, then guidelines, then behavioral details, then pairing. No wasted words.

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

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

For a simple 2-param tool, description covers purpose, usage, behavior, and pairing. No output schema needed; return values are implied by the key-value storage.

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 are clear. The description adds examples like 'target_ticker' and 'user_preference', enriching semantics slightly beyond schema.

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

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

The description clearly states the tool saves data for reuse, with specific verb 'save' and resource 'data as key-value pair'. It distinguishes itself from siblings like recall and forget by explicitly mentioning them.

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

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

The description explicitly says when to use it: 'when you discover something worth carrying forward', and pairs with recall and forget. No need for alternatives beyond mention.

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

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

The description goes well beyond the annotations by disclosing several important behaviors: graceful degradation (if GLEIF/OpenFIGI is unavailable, EDGAR identifiers still return), explicit handling of unresolved identifiers (stated under `unresolved` rather than omitted), the cascading of multiple lookup endpoints, and source-labeling of identifiers. This adds significant context beyond the readOnly/idempotent 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 and information-dense. It starts with example queries, then a clear 'Use FIRST' directive, then detailed supported types with fallback behavior. Every sentence adds value, though it is somewhat verbose for a single tool description.

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 that there is no output schema, the description does an excellent job of conveying what the tool returns for both entity types, including fallback behavior and unresolved fields. It covers the complexity of the tool thoroughly, making it easy for an agent to know what to expect and when to use it.

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

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

The input schema already provides 100% coverage for both parameters (type and value), including examples. The description adds extra meaning by explaining what each entity type returns (e.g., company returns CIK+ticker+LEI+FIGI; drug returns RxCUI+ingredient+brand) and how `value` is interpreted (ticker, CIK, name, or brand/generic). This goes beyond the schema's basic field 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: resolving user-spoken names to canonical/official identifiers. It uses a specific verb ('resolve') and resource ('NAME to identifiers'), provides concrete example queries, and distinguishes itself from sibling tools like compare_entities or entity_profile by focusing on name-to-ID resolution.

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

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

The description explicitly says 'Use FIRST whenever you have a name but need an ID,' giving clear guidance on when to use this tool. However, it does not explicitly name alternative tools or provide when-not-to-use scenarios, so it falls short of a 5.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds that the tool probes each entity, ranks them, and returns a ranked list with score, confidence, and signal density. It also mentions the optional _apiKey for Anthropic, providing useful behavioral context.

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

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

The description is concise, consisting of three sentences that efficiently convey the tool's function, use case, and output. It is front-loaded with the core purpose and no wasted words.

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

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

Given the tool has no output schema, the description clearly describes the output format: 'ranked list with score, confidence, signal density per entity.' It also explains the workflow and parameter usage, making it complete 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%, so baseline is 3. The description adds context by stating that the first entity is treated as the 'subject' for narrative and that all probes use ai_visibility_check. This adds meaning beyond the schema's parameter descriptions.

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

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

The description clearly states the purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. This distinguishes it from the sibling ai_visibility_check which likely handles single entities.

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

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

The description provides usage context: 'useful for competitive AI-marketing audits' and gives an example question. However, it does not explicitly state when not to use this tool or mention alternatives like compare_entities. It lacks exclusion criteria.

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, and non-destructive. The description adds valuable behavioral context: composite nature, partial failure handling with timing information for bundlephobia, and graceful degradation (sources_failed included in response). 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?

Description is verbose but well-structured with clear sections for purpose, usage, return values, ecosystem, and edge cases. Every sentence adds value. Could be slightly more concise, but earned its length.

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

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

Given the tool's complexity (composite of two APIs), absence of output schema, and existing annotations, the description is exceptionally complete. It covers return fields, partial failures, ecosystem constraints, and timing caveats, leaving no gaps for an agent.

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

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

Schema coverage is 100% with both parameters described. The description adds no new semantic details about parameters beyond what the schema provides (e.g., version defaults to latest). 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 clearly states it's a composite check for npm packages, combining deps.dev and bundlephobia. The verb 'scan' and resource 'dependency' are specific. It distinguishes from siblings by focusing on npm package evaluation, not general research or AI visibility.

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 explicitly says to use when the question is about npm package safety, popularity, or size. Mentions NPM-only in v1 and refers to other ecosystems using deps.dev directly. Does not explicitly list sibling alternatives but context is clear.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds that the tool is keyless and relies on WoRMS as authoritative source, which is useful 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?

Two sentences that are front-loaded with the core action and output. Every sentence adds value without redundancy.

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

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

Given no output schema, the description clearly enumerates the return fields (AphiaID, accepted name, authority, status, rank, classification). It also specifies keyless access. Could mention pagination or multiple match handling, but overall sufficient for a lookup 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?

Input schema has 100% description coverage for all 3 parameters. The description does not add significant new meaning beyond what the schema provides, but it does list the return fields (AphiaID, accepted name, etc.) which are not parameters.

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

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

The description clearly states it resolves a marine species or genus name to WoRMS AphiaID and taxonomic details. It differentiates from siblings like get_common_names or get_classification by specifying the source (WoRMS) and the exact 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 implies usage for marine species lookups and notes it's keyless, but does not explicitly state when to use over siblings or when not to use (e.g., if the name is not marine). No alternative tools are mentioned.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral details: BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, 200K char cap with truncation flagging, and offsets for verification. 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?

Four sentences, front-loaded with purpose, then usage, then technical details. Every sentence adds value with no redundancy or fluff.

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

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

Although there is no output schema, description fully explains output: top-N passages with character offsets and similarity scores. Also covers input limits, embedding details, and windowing. No gaps for an agent to use 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?

Schema coverage is 100%, so baseline is 3. Description adds natural-language query examples (e.g., 'supply-chain risk') and reinforces the 200K char limit for 'text,' providing practical guidance 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 'semantic search INSIDE a fetched record,' specifying the verb (search) and resource (text of a fetched record). Distinguishes from siblings by mentioning it pairs with ask_pipeworx_grounded and is used when the record is too large for the prompt.

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

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

Explicitly provides when to use: 'Use when the record is too big to cram into the prompt.' Mentions alternative 'ask_pipeworx_grounded' and explains how they complement each other. Gives clear context and exclusions.

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

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

The description goes well beyond annotations by disclosing behavioral traits such as requiring OAuth, return of a subscription id, rate limits (10 SMS per day), webhook auto-disable after 10 failures, and the signed verification process. Combined with annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=false), it provides a comprehensive safety profile.

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-organized with clear separation of subscription types and delivery channels. It front-loads the core purpose and then provides detailed breakdowns. While slightly verbose, every sentence adds value, maintaining a good balance between completeness and readability.

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

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

Given the tool's complexity (3 parameters, nested objects, no output schema), the description thoroughly covers all necessary aspects: types, parameters, delivery options, authentication requirements, rate limits, and side effects (webhook auto-disable). It leaves no critical gaps for an agent to use 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?

Schema coverage is 100%, and the description adds significant meaning: it explains type-specific parameters with examples (e.g., items:['5.02'] for sec_8k, topic:'fed' for polymarket_edge), clarifies delivery channel behaviors (webhook signing secret, email validation, phone verification), and notes constraints (SMS must match verified phone, webhook auto-disable).

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 'Create a proactive monitoring subscription to a live-data event stream' and specifies that it returns the new subscription id. It distinguishes itself from sibling tools like list_subscriptions and unsubscribe by focusing on creation, setting it apart from listing or removal operations.

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

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

The description includes prerequisites ('Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions)') and details on when to use this tool (to set up proactive monitoring). It does not explicitly exclude cases or compare with siblings, but the context is clear enough for an agent to determine appropriateness.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context about the output format (category-bucketed example questions with exact tool calls) and that it draws from a live catalog, but does not introduce any new behavioral aspects beyond what annotations imply.

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, starting with alternative phrasings, then stating the purpose, output format, and usage. It is slightly long but every sentence adds value; no redundancy.

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

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

Given the tool's simplicity (one optional param, no output schema), the description fully explains what it does, how to use it, and what to expect. Combined with rich annotations, it is complete for an agent to decide 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?

The input schema has 100% coverage for the single parameter 'topic', with its description listing allowed values. The tool description adds context that omitting topic gives a cross-category spread, which is not in the schema. This provides meaningful extra guidance.

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

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

The description clearly states the tool as the onboarding entry point for an agent that just connected, returning example questions with tool calls. It distinguishes itself from siblings by explicitly recommending to use this first when you do not know what Pipeworx can do.

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 (first connection, learning capabilities) and how to call (no arguments for full spread, or topic for focus). It also mentions learning how to call meta-tools, offering clear context.

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

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

Annotations provide idempotentHint and destructiveHint, but the description adds critical behavioral details: ownership enforcement, deactivation instead of deletion, and that historical events remain accessible via recent_alerts. This goes beyond what annotations convey.

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, no redundancy, and front-loads the key action. Every sentence adds value, making it concise and well-structured.

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

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

Given the tool's simplicity (1 param, no output schema), the description is complete. It covers the operation, constraints, effect on data, and links to a related tool for historical data. No gaps identified.

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

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

The input schema already describes the 'id' parameter as 'Subscription id (uuid) returned by subscribe' (100% coverage). The description does not add additional meaning beyond that, so a 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 'Cancel a subscription by id,' providing a specific verb and resource. It distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' by specifying the operation (cancel) and referencing ownership enforcement.

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

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

The description mentions ownership enforcement ('you can only cancel your own subscriptions'), implying usage context, but does not explicitly state when to choose this over alternatives like 'subscribe' or 'recent_alerts'. The link to recent_alerts is helpful but not a clear guideline.

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, and destructiveHint. The description adds substantial behavioral context by explaining the two verification pipelines (SEC EDGAR/XBRL vs. grounded pipeline), the fallthrough behavior, verdict types, citation format, and performance claim. No contradiction with annotations.

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

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

The description is well-structured with trigger phrases upfront, followed by functionality, pipeline details, return values, and efficiency claim. It is slightly verbose but every sentence adds value.

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

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

Despite no output schema, the description clearly lists verdict types and mentions citation format. It covers the two pipelines and tolerance defaults. Minor gaps: no discussion of error handling or edge cases, but overall adequate 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?

The schema description coverage is 100%. The description adds beyond the schema by explaining the tolerance parameter's default behavior ('implied by wording, capped at 5') and providing concrete claim examples for the claim 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?

The description uses specific verbs like 'verify' and 'fact check', identifies the resource as natural-language claims against authoritative sources, and distinguishes from sibling tools by stating it replaces 4–6 sequential calls. It also details two distinct verification pathways.

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: 'whenever the agent needs to check whether something a user said is factually correct'. It provides trigger phrases but does not explicitly mention when not to use it or compare to specific sibling tools.

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