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 declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond that: routing behavior across thousands of tools, automatic argument filling, structured answers with stable citation URIs, and operational traits like 'one fast call' and 'works on every tier'. 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 dense and every section earns its place: primary directive, domain list, trigger phrases, concrete examples, default-entry positioning, and step-up alternatives. The most important guidance is front-loaded ('PREFER OVER WEB SEARCH') and the structure flows from when to use to how to escalate.

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 default entry-point tool with no output schema, the description is complete: it explains the domain coverage, query form, routing behavior, output format with citation URIs, tier availability, and relationships to sibling tools. An agent has enough context to invoke it correctly and know what to expect.

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

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

Schema coverage is 100% and the schema fully documents the 'question' parameter plus its aliases, so the baseline is 3. The description adds useful examples ('current US unemployment rate', 'Apple's latest 10-K', 'patents Tesla was granted last month') that clarify the intended natural-language query style, elevating it above the baseline.

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

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

The description names a specific verb and resource (routes a question to the best of 5,724 tools across 1,497 sources) and explicitly distinguishes itself from siblings like ask_pipeworx_grounded and deep_research. It clearly states what the tool returns: a structured answer with stable pipeworx:// citation URIs.

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

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

The description gives explicit when-to-use guidance: 'PREFER OVER WEB SEARCH' for lists of domains, 'START HERE for most questions', and concrete trigger phrases like 'what is', 'look up', 'find', and 'get the latest'. It also states when NOT to use it and names alternatives: ask_pipeworx_grounded for hallucination-resistant single answers and deep_research for broad/multi-part fan-out.

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 explicitly discloses the beta behavior: candidate routing improvements are enabled live, no candidate is active right now, current behavior exactly matches ask_pipeworx, and there is no degraded fallback. This adds real context beyond the readOnly/openWorld/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 information-dense, with the beta identity front-loaded and the current candidate state, usage instruction, and fallback behavior each earning their place. The retired-candidate date is a small extra detail but not padding.

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 this is expected to behave identically to ask_pipeworx, the description covers the experimental status, current equivalence, routing comparison purpose, and full functionality. No output schema exists, but response shape is tied to the sibling tool, and the remaining invocation contract is fully captured by schema and 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?

Schema description coverage is 100% and the question parameter plus all five aliases are documented. The description mentions 'same arguments' but adds no new meaning beyond the schema, so the 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?

Clearly identifies itself as the beta variant of ask_pipeworx and as a universal router with the same 5,724 tools, arguments, and response shape, while distinguishing itself from ask_pipeworx via experimental routing. The verb, resource, and relationship to its sibling are all explicit.

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?

Gives a direct instruction: use it exactly like ask_pipeworx when the newest routing is desired. It also clarifies that results are compared against the stable router, that it currently matches ask_pipeworx, and that it is not a fallback stub but a fully working router.

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, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description goes far beyond: it discloses the full refusal contract with five explicit refusal_reason codes, the exact success return shape ({answer, evidence, confidence, source, fetched_at, refusal_reason:null}), and the constraint that answers are extracted 'using ONLY what the tool result contains.'

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

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

Roughly 140 words carry the value proposition, routing mechanism, both return contracts, five refusal reasons, when-to-use guidance, and the cost tradeoff against ask_pipeworx. Every sentence earns its place and the key differentiator ('Hallucination-resistant') is front-loaded.

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

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

With no output schema, the description carries full responsibility for return-value disclosure — and it does so completely for both the success and refusal paths, including the refusal_reason enumeration. Combined with annotations covering the safety profile and a single required parameter documented in the schema, nothing an agent needs to call this correctly 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?

Schema description coverage is 100% — the schema already documents `question` and all five aliases (q, text, input, query, prompt) with descriptions. The description adds no parameter-level detail beyond the fact that the input is a natural-language question, so the high-coverage baseline of 3 applies.

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

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

States a specific value proposition: 'Hallucination-resistant answer mode for high-stakes reads.' It names the mechanism ('picks the right tool from 5,724 across 1497 sources... then EXTRACTS the answer using ONLY what the tool result contains') and differentiates itself from the sibling ask_pipeworx ('Same routing as ask_pipeworx'), so an agent can tell them apart without opening schemas.

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

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

Explicitly instructs when to use: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements).' It also names the alternative and the condition that selects it: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.'

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

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

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?

The description discloses far more than annotations offer: account and paid-plan gating, parallel execution across 5,724 tools, 15–90s latency, explicit gaps[] rather than invented answers, resolvable citation URIs, hop fields, contradictions on standard/thorough, and semantic excerpting. The readOnlyHint and idempotentHint annotations are consistent with this read-only research behavior.

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

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

Though long, the description is dense and front-loaded with the most critical decision factors: account requirement, alternative tool, and the structured-data scope. Every sentence carries actionable information about usage, output, failure behavior, or latency; nothing is filler for a tool this complex.

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 covers return semantics: findings packet fields, verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, gaps[], contradictions[], and hop. It also explains expected latency and the main failure mode (empty gaps for non-structured topics), so an agent knows exactly what to expect even before invoking it.

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

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

Schema coverage is 100%, so the baseline is 3. The description does add operational color around depth ('standard' re-angles gaps; 'thorough' chases leads; multi-step questions resolve in one call) and latency, but most of that is already present in the input schema's depth description. The added value over the schema is modest rather than transformative.

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 names a specific action ('Grounded multi-source research') and a specific resource ('Pipeworx's 1497 STRUCTURED data sources'), then explains the mechanism: decomposition, parallel routing, and a findings packet. It explicitly distinguishes itself from ask_pipeworx, so an agent can select it correctly.

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

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

The description gives explicit when-to-use guidance ('Best for broad/multi-part questions over structured data'), explicit alternatives ('For a single lookup use ask_pipeworx'), and even an account prerequisite fallback ('If you are not signed in, use ask_pipeworx instead'). It also warns against using this tool for breaking or current-news topics.

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?

Beyond the annotations, the description discloses substantial behavioral context: matching is keyword/word-overlap based and can be 'pairing_unverified,' Kalshi fees are not modeled so spreads are gross, temporal_alignment null means uncomputed rather than aligned, and unclassified legs are never paired. None of this contradicts the readOnlyHint/openWorldHint/idempotentHint 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 and dense, but the length is earned given the tool's complexity and the absence of an output schema. It is front-loaded with purpose and modes, then organized into labeled safety fields, codes, and skipped-counter disclosures. It could be slightly more scannable, but each sentence adds a caveat or clarifying detail.

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 the return shape and safety semantics, and it does thoroughly: venue leg prices, top_spreads_pp, compatibility codes, per-entry flags, temporal_alignment, fees_note, and skipped counters. The agent is equipped to understand both expected outputs and edge-case failure modes.

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 covers all three parameters at 100%, so the baseline is 3. The description adds real value by explaining the mode semantics: topic auto-fetches 10 pre-mapped shortcuts, explicit ticker/slug parameters enable custom pairings, and both modes run the identical token-overlap matcher. This goes beyond the schema's terse descriptions, though not drastically.

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

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

The description opens with a specific verb and resource: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It distinguishes the tool's two modes and clearly defines what it computes, leaving no ambiguity about the tool's function.

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

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

The description gives strong usage guidance: it explains the topic-shortcut mode versus the explicit pairing mode, warns that 'pre-mapped ≠ tradeable,' and tells the agent to read compatibility fields even when matched pairs exist. However, it never explicitly contrasts this tool with sibling tools like polymarket_arbitrage or polymarket_edges, so when-to-use-this-versus-that is only implied.

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

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

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds meaningful behavioral detail beyond annotations: graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` labeling, source-labeled identifiers, and internal multi-endpoint cascading. 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 information-dense but is one long, run-on paragraph with deeply nested parentheticals and repeated clauses. It front-loads useful examples and use-first guidance, but the structure makes it harder to parse than necessary. It earns its content but not its formatting.

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

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

Given there is no output schema, the description carries the full burden of explaining return behavior. It does so thoroughly: supported types, accepted input forms, returned identifiers, source labels, unresolved handling, graceful degradation, and internal cascading. An agent has enough context 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?

The input schema already has 100% parameter description coverage, so the baseline is 3. The description adds value beyond the schema by explicitly listing ISIN as an accepted company input, giving canonical examples like 'CH0038863350', and clarifying that drugs accept brand or generic names. This pushes it above baseline.

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

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

The description opens with concrete trigger phrases and states the core function: 'resolve a user-spoken NAME to the canonical/official identifiers'. It names supported types ('company', 'drug') and specific output identifiers (CIK, ticker, LEI, FIGI, RxCUI), making it clearly distinguishable from profile or comparison tools like entity_profile and compare_entities.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID', which is strong when-to-use guidance. However, it does not name alternative sibling tools or state when NOT to use it, so it stops short of full alternative routing.

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?

The description goes far beyond annotations by explaining the meaning of each verdict, especially the critical 'could_not_verify' vs 'unsupported' distinction. It discloses the internal routing (SEC EDGAR/XBRL fast path vs grounded pipeline) and the behavior of returning verbatim evidence with citations. Annotations already indicate read-only, idempotent, open-world, and non-destructive, and the description complements rather than contradicts them.

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

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

The description is moderately long but every sentence carries meaningful information: trigger phrases, routing logic, verdict meanings, and a replacement note. It is front-loaded with the purpose and clearly structured, though it could be trimmed slightly by removing redundant trigger examples. Overall, it is efficient and well-organized.

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

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

With no output schema and only two parameters, the description fully explains the return values and their semantics, including edge cases and error conditions. It also covers the tool's scope and limitations, making it complete for an agent to use correctly. The mention that it replaces multiple sequential calls adds operational context that rounds out the picture.

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 'claim' and 'tolerance_pct' fully described in the schema. The description adds context about tolerance being capped at 5 and its use in grading, but this is marginal beyond the schema's own 'Overrides the tolerance implied by the claim wording' explanation. As such, it meets the baseline for high schema coverage without adding significant new parameter-level 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 purpose as natural-language claim verification against authoritative sources, with explicit trigger phrases and examples. It distinguishes itself from sibling tools by focusing on fact-checking and by noting it replaces a multi-step pipeline, making its role unambiguous.

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

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

The description gives explicit guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It differentiates between company-financial claims and other factual claims, routing behavior, but does not explicitly contrast with specific sibling tools like ask_pipeworx_grounded. This is clear context but lacks formal exclusions.

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