Met No

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. Description adds no extra behavioral context, such as data source or update frequency.

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

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

Extremely concise single sentence. Efficient but could include key details without being verbose.

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 output schema existing, the description lacks essential context: what the forecast includes (e.g., AQI, pollutants), coordinate constraints, or how 'areaclass' affects results. Incomplete for a simple 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?

Only 'areaclass' has a description in the schema. The tool description does not explain lat/lon usage or coordinate format. With low schema coverage (33%), the description should compensate but fails to.

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 provides air quality forecasts for Norway, distinguishing it from general forecast tools like 'forecast' and 'nowcast'. However, it could be more explicit about retrieving a forecast for a given location.

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 siblings like 'forecast' or 'nowcast'. No context on prerequisites or 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, idempotentHint, and destructiveHint. The description adds valuable behavioral context: default model (Workers AI Llama-3.3-70b), optional Anthropic probing with BYO key, and return structure (per-model score, confidence, signals, raw_response + combined view). No contradictions.

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

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

The description is three sentences, each serving a distinct purpose: first sentence states purpose and scoring, second adds model/cost options, third details return format. No 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 the tool's complexity (4 parameters, optional API key, multiple models) and lack of output schema, the description covers the main workflow, parameter roles, and return structure. It does not explain the scoring formula or confidence computation, but this is tolerable for an audit tool.

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

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

Schema coverage is 100%, but the description enriches parameter understanding: it explains that `models` defaults to workers-ai, `_apiKey` is only needed for Anthropic, and `context` aids disambiguation. This adds meaning beyond the schema descriptions.

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

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

The description clearly states the tool probes LLMs for knowledge about an entity and scores visibility (0-100). It specifies the verb 'probe' and resource 'LLMs', and distinguishes from sibling tools like 'ask_pipeworx' or 'scan_competitor_ai_presence' by focusing on visibility scoring across multiple models.

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: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to use the `_apiKey` parameter for Anthropic and notes the default free model. However, it does not mention when not to use this tool or compare to alternatives 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 readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds that it routes questions to the right tool, fills arguments, and returns structured answers with citation URIs, providing context beyond annotations. No annotation contradiction. Could mention any potential limitations like latency or token usage.

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 key purpose and usage, but it is somewhat lengthy. However, every sentence adds value, and it is well-structured with clear guidance and examples.

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 (no output schema, massive backend), the description is complete: it explains the tool's role, when to use it, how it works internally, and provides examples. It covers all necessary context for an agent to decide when to invoke this tool.

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

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

Schema describes 6 aliases for the 'question' parameter with 100% coverage. The description adds value by noting that it accepts natural language and providing examples of valid questions, which helps the agent understand what constitutes a valid input.

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

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

The description clearly states that this tool answers factual questions using structured data from 4,482 tools across 1,129 sources, with citation URIs. It also distinguishes itself from siblings like ask_pipeworx_grounded and deep_research, providing a specific verb+resource with scope.

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

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

The description explicitly tells when to use this tool (for most factual questions) and when to step up to alternatives (ask_pipeworx_grounded for grounded answers, deep_research for broad questions, web search for breaking news). It provides example queries and guidance like 'START HERE for most questions'.

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

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

Discloses key behaviors beyond annotations: uses only tool result, returns explicit refusal reasons (e.g., 'not_in_source'), and costs one extra LLM call. No contradiction with annotations (readOnlyHint, etc.).

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

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

The description is somewhat long but every sentence adds value (purpose, when to use, behavioral details, return format). It is front-loaded with the core purpose and well-organized, though slightly verbose.

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 (high-stakes, refusal logic) and no output schema, the description thoroughly covers return structure ({answer, evidence, confidence, source, fetched_at, refusal_reason}), parameter usage, and behavioral constraints, leaving no critical gaps.

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

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

Schema coverage is 100% with all parameters (aliases for 'question') documented. The description adds minimal semantic value beyond the schema, simply restating 'Your question in natural language' and listing aliases. Baseline score 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 defines the tool as a hallucination-resistant answer mode for high-stakes reads, specifying its internal process (routing, fetching, extracting answer only from tool result). It distinguishes from sibling 'ask_pipeworx' by highlighting the grounded extraction and extra LLM cost.

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

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

Explicitly states when to use ('whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') and when to prefer an alternative ('prefer ask_pipeworx for casual lookups'), offering clear decision 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 are readOnlyHint=true, idempotentHint=true, destructiveHint=false, openWorldHint=true. The description goes far beyond these annotations, detailing fan-out behavior, response shapes, resolver contract, parent event extractor, news fallback handling, safety mechanisms (low-confidence match, closed market), and resolution-rule risk. No contradiction with annotations.

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

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

The description is comprehensive but verbose (multiple dense paragraphs). While front-loaded with a clear summary, it includes extensive details that could be streamlined. Every sentence adds value, but the length may hinder quick parsing. A more structured format could improve conciseness.

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

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

Given the complexity (classifiers, fan-outs, edge cases) and no output schema, the description is remarkably complete. It covers market matching confidence, parent events, news fallbacks, safety, closed markets, wide spreads, and cancellation rules. An agent can fully understand the tool's behavior and edge cases.

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

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

Schema description coverage is 100% (all three parameters have descriptions). The description adds significant value by providing examples for market input, explaining depth choices ('quick = 2-3 evidence sources, thorough = full fan-out'), and clarifying include_raw's impact on response size. It enhances understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the verb (research), resource (Polymarket bet), and input types (slug, URL, question text). It distinguishes from siblings by listing specific use cases like 'should I bet on X' and mentioning sibling tools implicitly through fan-out examples.

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 for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It implies alternatives by listing sibling tools like polymarket_edges and polymarket_arbitrage in the context. However, it does not explicitly state when not to use this tool or name alternative tools directly.

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

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

The description adds behavioral context beyond annotations: it discloses that fiscal year handling is correct (e.g., AAPL Sep), results are sorted by primary metric, and returns paired data with citation URIs. No contradiction with readOnlyHint, openWorldHint, idempotentHint, or destructiveHint annotations.

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

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

The description is dense yet efficient, covering purpose, usage, and behavioral details in a single paragraph. No extraneous information; every sentence adds value.

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

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

Given no output schema, the description fully explains return data (revenue, net income, cash, debt for companies; adverse events, approval counts, trial counts for drugs) and mentions citation URIs. It is complete for an AI agent to select and invoke correctly.

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

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

Schema coverage is 100%, but the description adds meaning by clarifying that 'values' are tickers/CIKs for company and drug names, and explicitly states maxItems constraint (2-5). This enriches the schema information.

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

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

The description clearly states the tool performs side-by-side comparison of 2-5 companies or drugs in a single parallel call. It specifies the data pulled for each type (e.g., SEC filing for companies, FAERS for drugs) and uses explicit verbs like 'compare', which distinguishes it 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 says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use guidance. It implies not to use for single entities, effectively differentiating 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 and destructiveHint. Description adds significant behavioral context: decomposition into facets, parallel routing to 4,482 tools, expected runtime (15-60s), and return format with evidence, confidence, gaps, and citations.

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?

Dense single paragraph with critical info front-loaded (account requirement). Slightly dense but every sentence is valuable. Could benefit from bullet points for clarity.

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

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

Given tool complexity, description is remarkably complete: covers inputs, process, output structure, limitations, and alternatives. No output schema needed as return format is described. Fully prepares an agent for correct selection and invocation.

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

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

Schema coverage is 100%, but description adds extra meaning: specifies facet counts for depth levels (quick=3, standard=5, thorough=8) and advises that question should be broad/multi-part. Adds value beyond schema.

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

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

The description clearly states the tool's purpose: grounded multi-source research across structured data sources. It distinguishes from sibling ask_pipeworx (single lookup) and provides specific examples of use cases.

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

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

Explicit guidelines: when to use (broad/multi-part questions over structured data), when not to use (breaking news, current events), and alternatives (ask_pipeworx). Includes account requirements and depth limitations.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that results are 'ready to call directly, no second schema lookup needed.' It explains that it searches over many domains and returns curated examples, providing context beyond annotations.

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

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

The description is a single paragraph that efficiently conveys purpose, usage, and return value. It is front-loaded with the core purpose. While it is slightly long, every sentence adds value.

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

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

Given the tool's complexity (search over many domains, 6 parameters with aliases, no output schema) the description covers what it does, what it returns, and when to use it. It is complete enough for an agent to understand and invoke correctly.

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

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

Schema coverage is 100% with descriptions for each parameter. The description mentions aliases and provides examples (e.g., 'look up FDA drug approvals'), but does not add significant meaning beyond what the schema already provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists specific domains (SEC filings, financials, etc.) and explains the return value (top-N tools with full schemas). This distinguishes it from sibling tools, as it is a meta-discovery tool.

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

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

The description explicitly says 'Call this FIRST when you have many tools available and want to see the option set.' This provides clear when-to-use guidance, though it does not explicitly mention when not to use it (e.g., if you already know 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?

Description adds significant behavioral context beyond annotations: details multi-source fan-out, specific return fields, soft-fail for USPTO patents, news fallback chain. Annotations already indicate read-only, idempotent, non-destructive, and open-world; 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?

Highly informative yet structured: starts with example queries, gives priority guidance, then enumerates data sources and return fields. Every sentence adds unique value; no filler.

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

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

Despite no output schema, description thoroughly documents return values (CIK, filings with URIs, fundamentals, patents, news, LEI) and notes limitations (e.g., patent sunset, name requirement). Enough for an agent to decide and use 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 already describes both parameters fully. Description adds valuable context: example inputs, note that type is currently restricted to 'company', clarification that value can be ticker or CIK but not names. This enriches the schema.

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

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

Description clearly states the tool provides a full cross-source profile of a US public company, with specific examples of user queries and a list of returned data types. It distinguishes itself from siblings like resolve_entity by noting that for names you need to use that tool first.

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 ALWAYS PREFER over chaining single-source lookups for holistic views. Provides clear when-not-to-use by requiring ticker/CIK and directing name-only users to resolve_entity.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds useful context: 'compact' and lists the weather elements returned, which goes beyond 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?

Single sentence with 9 words, front-loads key purpose (compact 10-day forecast), no redundant information. Extremely 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 presence of output schema, full schema coverage, and annotations, the description sufficiently covers the tool's purpose and output. No missing critical information for a simple weather forecast 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 covers all 3 parameters with descriptions (100% coverage). Description adds no additional parameter meaning beyond stating the output scope, so baseline 3 is appropriate.

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

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

Description clearly states it returns a 10-day forecast with listed elements (temperature, precip, wind, cloud, humidity). Distinguishes from siblings like nowcast (current conditions) implicitly, but lacks explicit differentiation.

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 explicit when-to-use or alternatives are provided. Usage is implied from the description of a 10-day forecast, but no contrast with sibling tools like nowcast or airquality.

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

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

Annotations already declare destructiveHint=true, so the agent knows it's a destructive operation. The description adds useful context about what gets deleted (memories) and why (stale/sensitive data), which goes beyond the annotations.

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

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

Two sentences that front-load the action, then provide usage context and pairing guidance. 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?

For a one-parameter tool with full schema coverage and annotations, the description fully satisfies contextual needs: it explains the action, usage context, and relationship to sibling tools.

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

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

Schema coverage is 100% with a clear description of the 'key' parameter. The description's 'by key' aligns with the schema but adds no additional semantic meaning beyond what the schema already 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 verb 'Delete' and the resource 'previously stored memory by key', and explicitly distinguishes from siblings 'remember' and 'recall' by mentioning them as paired tools.

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

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

The description explicitly states when to use ('when context is stale, the task is done, or you want to clear sensitive data') and implicitly when not to use by pairing with 'remember' and 'recall' for other operations.

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 process details ('Fetches the page, extracts title/description/key links') and output format, complementing the annotations without contradiction.

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

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

The description is two sentences plus a bullet list of use cases. Every sentence adds value, no redundancy, and the core purpose 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?

Given the simplicity (2 params, no output schema, good annotations), the description covers purpose, process, output format, and use cases comprehensively. No gaps remain.

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

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

Schema covers both parameters with full descriptions. The description adds no new semantic value beyond what the schema provides, so baseline score of 3 is appropriate.

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

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

The description states exactly what the tool does: 'Generate a production-ready llms.txt file for any URL' with specific verb and resource. It distinguishes from siblings by focusing on llms.txt generation, which none of the sibling tools 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 use cases (e.g., 'getting a client's site indexed by AI, drafting llms.txt for your own project'), but does not explicitly state when not to use it or provide 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, idempotentHint, and no destructiveHint. The description adds that it returns active subscriptions by default, which aligns with readOnlyHint. It also lists the specific fields returned, 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 two sentences: the first states the core function and output, the second provides usage guidance. No wasted words, and the key information 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?

Given the tool has one optional parameter, no output schema, and rich annotations, the description covers the essentials: what it returns and when to use it. It could mention pagination or limits, but for a simple list tool, it is sufficiently 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?

The schema describes the only parameter (include_inactive) with 100% coverage, so the description adds no new meaning. Baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'List', the resource 'the caller's active subscriptions', and lists the returned fields. This distinguishes it from sibling tools, which have different purposes.

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 using this tool 'before adding more' subscriptions or 'to find an id to cancel', providing clear context for when to invoke it. It does not explicitly mention when not to use it or alternatives, but the guidance is strong.

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

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

The description adds important behavioral context beyond annotations: the 90-minute timeframe, geographic restriction, and sparse data outside Nordics. The annotations (readOnlyHint, idempotentHint) are consistent with a read-only nowcast operation.

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

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

The description is a single sentence that is front-loaded with the essential information: what the tool does, the time span, and the geographic constraint. 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 simplicity of the tool (two lat/lon params, clear annotations, and an output schema), the description is complete enough. It covers the purpose, key constraint, and data quality warning.

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 schema description coverage at 0%, the description does not explain what lat and lon represent or the expected format (e.g., decimal degrees). It simply reuses the param names without additional meaning, failing to compensate for the lack of schema documentation.

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

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

The description clearly states the tool provides a 90-minute precipitation nowcast, specifies the regional limitation (Nordics only), and mentions the sparse data behavior outside the region. This distinguishes it from sibling tools like 'forecast' or 'airquality'.

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 implicitly advises use for short-term precipitation just in the Nordics, and warns of sparse data elsewhere. It does not explicitly name alternatives, but the context is clear enough for an agent to decide when to use this tool.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds that the tool returns current, wave height, and sea temperature, which is useful context but does not contradict annotations. However, it lacks details on data sources, units, or update frequency.

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 concise (one short sentence) but at the cost of missing critical information. It is front-loaded with the purpose, yet would benefit from more details without becoming verbose.

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

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

With an output schema present, the description does not need to explain return values. However, the parameter semantics are severely lacking, and the description does not cover constraints or usage context. For a simple tool with 2 parameters, it is minimally adequate but incomplete.

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

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

Schema description coverage is 0%, meaning no parameter descriptions exist in the schema. The tool description does not explain the parameters (lat, lon) at all. It fails to clarify that these are latitude and longitude coordinates or specify valid ranges.

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

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

The description clearly states the tool provides ocean forecast including current, wave height, and sea temperature. It uses a specific verb ('forecast') and resource ('ocean conditions'). This distinguishes it from sibling tools like 'forecast' (general) and 'nowcast' (short-term).

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 'forecast' or 'nowcast'. There is no mention of prerequisites, limitations, or scenarios where another tool would be more appropriate.

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

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

Description adds value beyond annotations: mentions rate limit (5 per identifier per day), free usage (no quota impact), and that signals affect roadmap. No contradiction with annotations (destructiveHint false).

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

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

Very concise: four sentences covering purpose, when to use, how to provide feedback, and constraints. 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?

Comprehensive for a simple feedback tool: covers purpose, usage, rate limits, cost, and tips. No output schema needed. Annotations provide essential info.

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 usage nuance (e.g., don't paste end-user prompt) and clarifies that context is optional. Doesn't repeat schema details but provides helpful context.

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

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

Description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It lists specific feedback types (bug, feature, data_gap, praise) and uses a strong verb-resource combination. Distinct from sibling tools like ask_pipeworx or 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?

Explicitly tells when to use: for wrong/stale data, missing tool, or praise. Also provides guidance on describing issues in terms of tools/packs and not pasting prompts. Lacks explicit alternatives but context implies this is for feedback 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 already indicate read-only, idempotent, non-destructive behavior. The description adds valuable transparency: data source (CF analytics-engine), no PII, caching duration (5min-1h), and aggregation detail. 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 about six sentences, front-loading the core purpose then listing use cases and technical details. It is concise yet informative, though slightly longer than strictly necessary.

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

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

For a tool with one optional parameter and no output schema, the description fully explains inputs, behavior, use cases, caching, and privacy. It is complete for an agent to decide when to call and 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 covers parameter 'window' with enum and description. The description expands on semantics: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This adds meaningful 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?

The description clearly states the tool returns top tools, top packs, and total call volume over a window, with a specific verb ('Returns') and resource. It distinguishes itself from sibling tools like discover_tools and ask_pipeworx by focusing on aggregated usage trends.

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 three use cases (discovering hot data sources, confirming canonical tools, aligning with agent needs) and advises on window selection (shorter for hot, longer for steady-state). It does not mention when not to use, but the context is clear.

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

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

Annotations include readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds behavioral traits beyond annotations: requires one of event/topic, partition filter drops placeholder slugs, semantic anchor for similarity (≥0.30 Jaccard), fill check against CLOB depth. No contradiction.

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

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

The description is detailed and structured with clear sections, but could be slightly more concise given the length. However, every sentence adds value for the complex functionality.

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 fully explains return structures (opportunities[], partition_check, fill_check) and references sibling tools for custom sizing. Covers all key behaviors and constraints for a complex tool.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds significant meaning: explains event mode vs topic mode with examples, clarifies that full URLs are accepted for event, and describes the internal processing for topic.

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

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

The description clearly states it finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes between two modes (event and topic) with specific use cases, and the context from sibling tools shows no overlap with similar 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?

Provides explicit guidance: use `event` for a specific market slug, `topic` for cross-event scanning, and warns that calling with no args fails. Includes examples of when cross-event catches patterns single-event misses, and mentions fill check and when not to trade.

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?

Despite annotations already declaring readOnlyHint, openWorldHint, and idempotentHint, the description adds extensive behavioral details: three model families with formulas, filtering logic, caching behavior, diagnostic output, and edge cases like Fed bets. This far exceeds the burden typically placed on descriptions.

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

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

The description is long but well-organized with clear sections, bold headings for key terms, and numbered lists. It front-loads the purpose and efficiently explains complex logic. Minor redundancy could be trimmed, but overall structure earns 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 (9 parameters, no output schema, three model families), the description is remarkably complete. It explains response structure, filtering knobs, diagnostic fields, caching, and even edge cases like Fed bets. No gaps remain for an agent to misuse the tool.

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

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

Schema coverage is 100% with detailed descriptions for all 9 parameters. The tool description adds extra context beyond schema, such as labeling parameters as 'Tradeable-edge knobs' and explaining interactions like min_kelly vs min_partition_leg_kelly. This adds meaningful value, hence 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: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' This is a specific verb+resource combination that effectively distinguishes it from siblings like polymarket_arbitrage.

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

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

The description explicitly targets the use case: 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets.' This provides clear context, though it lacks explicit exclusions or alternative tools for when not to use this tool.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds significant behavioral detail: output structure (tracked, expired, snapshot_dates), decay computation from daily closes, limits due to TTL and snapshot start. 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 fairly long but well-structured: purpose first, then detailed output explanation, then limits. Every sentence adds value. Could be slightly more terse, but overall efficient.

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

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

Despite no output schema, the description thoroughly explains the return structure (tracked with time-series, first_seen, trend, decay; expired with lifespan; snapshot_dates). It also covers limits and data source. Very complete for the tool's complexity.

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

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

Schema covers 100% of parameters. Description adds extra context: default values, clamping for days (clamp 2-30), and enumerates window options (24hr, 1wk, 1mo) beyond schema descriptions. 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 the tool provides edge persistence and decay telemetry from daily snapshots, answering specifically how long an edge has existed and if it is shrinking. This distinguishes it from sibling tools like polymarket_edges which likely focus on current edges.

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

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

The description explicitly explains when to use this tool: to differentiate between fresh and old edges. It provides context on why that matters. It does not explicitly exclude other uses or contrast with alternatives, but the usage 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?

The description goes beyond annotations by explaining the tool's behavior: it checks against live order-book depth, walks the ladder, and returns metrics like top_of_book, vwap_fill_price, slippage, and a verdict. It also warns that partial basket fills can create unhedged directional risk, which is a critical behavioral trait. Annotations indicate read-only and idempotent, consistent with the description.

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

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

The description is well-structured with clear sections for single-market and basket modes, using ALL CAPS for key points and front-loading the core purpose. It is somewhat verbose but justified given the complexity of the tool. Every sentence adds value, though minor redundancy could be trimmed without loss.

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

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

Given the tool's complexity (two modes, 4 parameters, no output schema), the description is comprehensive. It covers purpose, parameter usage, default behaviors, return fields, and critical warnings about partial fills and directional risk. It also provides cross-tool context by linking to polymarket_arbitrage and polymarket_edges.

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 tool description adds significant value beyond the schema. It explains the dual meaning of size_usd (max spend on buys vs target proceeds on sells; for basket mode, it's settlement notional), default side auto behavior in basket mode, and clarifies that market and event are required despite not being marked as such in the schema. The description also details return fields, compensating for the lack of output schema.

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

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

The description clearly states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes and explicitly differentiates from sibling tools like polymarket_arbitrage and polymarket_edges by recommending usage before those actions.

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

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

The description provides explicit guidance on when to use the tool: before acting on polymarket_arbitrage signals or polymarket_edges trades above ~$500. It explains the reasoning (theoretical overround on thin books is not capturable, partial basket fills convert arb to unhedged directional position) and specifies required parameters ('REQUIRES one of market or event').

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 and idempotentHint, so the description carries less burden. It adds significant behavioral context: safety fields like compatibility_warning, temporal_alignment, and skipped_cross_type/subtype explain when spreads are meaningful. Could more fully detail the response structure, but given annotations, this is strong.

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

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

The description is longer than ideal but well-structured: it opens with the core purpose, then explains modes, response fields, and safety hints. Every sentence adds value, and the format makes key points easy to parse. Slight trimming could improve conciseness, but it's effective.

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

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

Despite lacking an output schema, the description adequately covers input options, response structure (leg-by-leg prices, top_spreads_pp, compatibility fields), and critical caveats (temporal alignment, shape equivalence). For a moderately complex tool, this is complete enough for an agent to make informed decisions.

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

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

Schema coverage is 100%, so the schema already documents all three optional parameters. The description adds value by explaining the interplay between topic, kalshi_event_ticker, and polymarket_event_slug, and by detailing the two operational modes. This provides meaning beyond the raw schema.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies two modes (topic shortcuts and explicit tickers), and the context distinguishes it from siblings like polymarket_arbitrage which likely handles intra-venue opportunities.

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 covers when to use each mode, including macro shortcuts vs. manual pairing. It warns that most pre-mapped topics currently return compatibility warnings and that pre-mapped does not imply tradeable, guiding the agent to look for real signals only when bet shapes are equivalent.

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. The description adds value by explaining the tool's scoping (anonymous IP, BYO key hash, account ID) and its relationship with remember/forget, which goes beyond the annotations.

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

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

The description is concise with three sentences, each serving a distinct purpose: defining action, providing usage context, and explaining scoping. No unnecessary words or 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 parameter, no output schema), the description covers all essential aspects: what it does, how to use it, scoping, and pairing with siblings. It is complete for an agent to correctly select and invoke this tool.

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

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

The input schema already has 100% coverage with a description for the key parameter that explains omitting it lists keys. The description repeats this information without adding further meaning, so the baseline of 3 is appropriate.

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

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

The description clearly states it retrieves a value saved via remember or lists all keys, with specific verbs and resource. It distinguishes itself from sibling tools like remember and forget by mentioning them explicitly.

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

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

The description explains when to use the tool (look up context stored earlier) and how to use it (omit key to list). It also mentions scoping by identifier. While it doesn't explicitly state when not to use it, the guidance is clear and sufficient given the tool's simplicity.

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

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

The description states that setting mark_read:true flags returned events as read, which is a state modification. However, the annotations declare readOnlyHint=true, indicating no state change. This is a direct contradiction, so the score is 1 per guidelines.

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, each adding value, with key information front-loaded (what the tool returns, filtering capability). No redundant or vague phrasing.

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 explains return fields (source, citation_uri, raw payload). It also covers the mark_read side effect. However, it does not elaborate on limit or unread_only parameters, though they are in the schema with descriptions. Overall, it's fairly complete for a list tool.

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

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

Schema coverage is 100%, but the description adds meaningful context: it specifies that type can be e.g. 'sec_8k', since is an ISO timestamp, and explains the effect of mark_read on subsequent calls. This goes beyond the schema descriptions.

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

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

The description clearly states it pulls fired events from a subscription feed, returns recent alerts with specific fields (source, citation_uri, raw event payload), and allows filtering by type and time. It distinguishes from sibling tools like list_subscriptions and subscribe by focusing on retrieving events rather than managing subscriptions.

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

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

The description mentions polling is fine and provides an alternative access method (GET endpoint for scripts/dashboards), giving context on when to use this tool versus other means. However, it does not explicitly state when not to use it or compare to other similar tools.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc., but the description adds significant behavioral context: fan-out to multiple sources, fallback logic, soft-fail for USPTO, and citation URI return. 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?

Description is a single dense paragraph but well-organized, front-loading example queries and then detailing behavior. Every sentence adds value, though could be slightly more structured with bullet points.

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 specifies the return structure (changes[] grouped by source, total_changes count, citation URIs). For a read-only tool with clear annotations, this is complete and actionable.

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

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

Schema coverage is 100%, and the description adds meaning beyond the schema: explains since formats (ISO date, relative shorthands like '7d', '30d'), provides usage suggestion ('Use 30d for typical monitoring'), and clarifies value as ticker or CIK with examples.

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

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

Description clearly states it provides a change feed for a company over a time window, listing specific data sources (SEC EDGAR, GDELT, USPTO) and explicitly distinguishes from sibling tool entity_profile for static profiles.

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

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

Provides explicit when-to-use examples (e.g., 'What's new with X') and when-not-to-use guidance ('Use entity_profile instead for static profile'). Also describes fallback behavior between GDELT and GNews.

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 idempotentHint=true and destructiveHint=false. The description adds value beyond annotations by specifying persistence behavior (permanent for authenticated users, 24-hour retention for anonymous), which is critical for agent decision-making.

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: purpose first, then usage context, then behavioral specifics, then pairing suggestions. It is slightly verbose but each sentence adds necessary information.

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

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

For a simple key-value store with two parameters and no output schema, the description covers purpose, usage cues, behavioral traits, and cross-tool integration. The absence of return value documentation is acceptable since the schema is complete and the tool is straightforward.

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?

Both parameters have schema descriptions that already define them well (key as string with examples, value as string). The description adds general context for usage patterns but does not materially expand on parameter semantics 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 opens with a specific verb 'Save' and resource 'data' and explicitly states the tool's purpose: retaining data across sessions. It clearly distinguishes from sibling tools like recall and forget, which are mentioned as companions.

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 ('when you discover something worth carrying forward'), gives concrete examples, and names alternatives ('Pair with recall to retrieve later, forget to delete').

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false, so the safety profile is clear. The description adds value by noting that 'Each call cascades through several lookup endpoints internally' and that the tool replaces 2-3 manual lookups, 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 moderately lengthy but well-structured: examples first, then purpose, then type details. Each sentence serves a purpose. Could be slightly more concise, but the level of detail is appropriate for the complexity of the tool's two modes.

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 two parameter types with full schema coverage, rich annotations, and no output schema, the description provides ample context about outputs and citation URIs. It covers both entity types thoroughly. Lacks explicit error handling details, but the completeness is adequate for a well-annotated read-only tool.

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

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

With 100% schema coverage, the baseline is 3. However, the description adds significant meaning by specifying what each combination of type and value returns (e.g., company returns ticker + CIK + company_name + citation URI; drug returns RxCUI + ingredient + brand + citation). This provides concrete usage examples that go well beyond the schema's generic 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 begins with concrete query examples that clearly illustrate the tool's purpose: resolving names to official identifiers. It explicitly states 'resolve a user-spoken NAME to the canonical/official identifier other tools require as input' and distinguishes it from siblings by positioning it as the first step when a name needs an ID.

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 'Use FIRST whenever you have a name but need an ID', giving a clear when-to-use rule. It does not explicitly state when not to use, but the instruction implies avoiding it if IDs are already known. The detailed breakdown of supported types provides context for choosing this tool over manual alternatives.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds beyond annotations: 'Probes each entity with ai_visibility_check, ranks by score, returns ranked list with score, confidence, signal density per entity.' No contradictions.

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

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

Two sentences, front-loaded with main purpose, no wasted words. Every sentence adds unique value.

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

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

No output schema exists, but description adequately explains return format (ranked list with score, confidence, signal density). Parameters are fully documented. Annotations cover safety. Complete for a tool of this complexity.

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

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

Schema coverage is 100%. Description adds extra meaning: entities parameter notes first entry as subject, rest as competitors; models and _apiKey are explained in context. Adds value beyond schema.

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

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

The description clearly states 'Compare AI visibility across multiple entities side-by-side,' specifies the probe mechanism and output ranking. It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (general comparison).

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

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

Explicitly states 'Useful for competitive AI-marketing audits' with example query. Does not explicitly state when not to use, but the context and sibling tools make it 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?

Discloses partial failures (sources_failed field, bundlephobia 5-30s delay), NPM-only scope, and graceful degradation. Annotations already indicate readonly/idempotent, so description adds value beyond those.

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

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

Two efficient sentences covering purpose, usage, output, scope, and edge cases. No redundancy, information-dense yet clear.

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

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

Given no output schema, the description thoroughly enumerates return fields, links, and alternative versions. Also covers failure modes and ecosystem limitations. Complete for a composite tool.

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

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

Schema coverage is 100% so baseline is 3. Description adds meaning by explaining optional version defaults to latest and that scoped packages are accepted, slightly enriching 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 composite check for npm packages across deps.dev and bundlephobia, with specific verb 'scan' and resource 'dependency'. It distinguishes from sibling tools by noting non-NPM ecosystems fall under a different direct tool.

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

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

Explicitly says 'Use whenever an agent asks...' and provides specific example questions. It also specifies what not to use for (PyPI, Maven, etc.) and points to alternative approach.

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

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

Description adds substantial behavioral details beyond rich annotations: embedding model (BGE-base-en), window size, similarity metric, character offsets, truncation behavior (200K chars flagged). No contradictions.

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

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

Two sentences with no wasted words. First sentence provides core purpose and value, second adds technical details. Front-loaded and 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?

Despite no output schema, description explains return format (passages with offsets and similarity scores), covers input limits and truncation flag. Complete for a search tool.

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

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

Schema coverage is 100% and description adds marginal value: examples for query, repeats max chars for text, mentions default for limit. 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 does 'semantic search INSIDE a fetched record', with specific examples like SEC 10-K body and article. It distinguishes from sibling 'ask_pipeworx_grounded' by explaining the pairing.

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

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

Explicitly says when to use: 'when the record is too big to cram into the prompt'. Describes benefits (saves context, returns relevant passages with offsets). Mentions alternative/paired tool 'ask_pipeworx_grounded'.

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

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

Annotations already indicate write operation, idempotent, non-destructive. Description adds that it returns subscription ID, requires authentication, and details delivery constraints (SMS cap, webhook signing).

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

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

Description is dense but well-structured with two paragraphs. Front-loads purpose. Could benefit from bullet points but not overly long. 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 account requirement, type-specific params with examples, delivery channels with constraints. No output schema exists but description states return value (subscription ID). Very comprehensive.

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, description adds rich examples for each type's params and delivery options, providing substantial context beyond the schema.

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

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

The description uses specific verb 'Create a proactive monitoring subscription' and resource 'live-data event stream'. It clearly distinguishes from sibling tools like list_subscriptions and unsubscribe.

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

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

Adds clear prerequisites (Pipeworx OAuth account) and supported types/delivery channels. Does not explicitly mention when not to use, but context 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?

The description adds behavioral context beyond annotations: it returns live example questions drawn from the catalog, and explains the effect of the topic parameter. Annotations already indicate read-only, idempotent, open-world; description reinforces and adds detail.

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: starts with natural language queries, then explains purpose, output, usage, and parameter. Every sentence adds value, and it is appropriately sized for the tool's complexity.

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

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

Given the simple input (one optional parameter), rich annotations, and clear output described, the description is fully complete. It covers what the tool does, when to use it, how to use it, and 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 description already lists possible values and behavior. The tool description repeats this information without adding new meaning, so it meets but does not exceed 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 clearly states the tool's purpose: it is an onboarding entry point that returns category-bucketed example questions with exact tool and argument shapes. It distinguishes itself from siblings like discover_tools and ask_pipeworx by being the discovery tool for newcomers.

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

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

The description explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you', providing clear when-to-use guidance. It implies when not to use but does not explicitly name alternative tools.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, indicating a safe read operation. The description adds that the tool returns sun/moon events, which is consistent and adds context beyond the annotations.

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

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

The description is a single, clear sentence that is front-loaded with the key purpose. No unnecessary words or 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 low complexity and the presence of an output schema, the description captures the essential purpose. However, it could be slightly more complete by noting that lat/lon are required and the default date behavior.

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 4 parameters with only 50% coverage (date and offset have descriptions). The tool description does not add any parameter details, missing the chance to explain lat/lon or clarify format expectations.

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

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

The description clearly states the tool provides sunrise, sunset, and moon events for a date. It uses a specific verb ('get' implied) and specifies the resource (celestial events). It is distinct from sibling tools like 'airquality' or 'forecast'.

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 a date and location but provides no explicit guidance on when to use this tool versus alternatives, nor does it mention when not to use it. Since there are no clear sibling tools with overlapping functionality, the lack of explicit guidelines is acceptable but not ideal.

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 idempotentHint=true and destructiveHint=false. The description adds important behavioral details: ownership enforcement and that the row is deactivated (not deleted), preserving historical events. This goes beyond annotations, but could be more explicit about error conditions.

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

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

Two sentences, no redundancy. The first sentence is action-oriented and front-loaded, the second provides necessary nuance. Every word earns its place.

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

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

For a simple tool with one parameter and no output schema, the description covers the main use case, effect on data, and ownership constraints. It is slightly lacking in specifying error handling or id not found, but remains adequate.

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

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

The schema has 100% coverage with a clear description for the 'id' parameter. The tool description merely references 'by id' without adding new semantic detail. Therefore 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 the verb 'Cancel' and the resource 'subscription by id', which differentiates it from sibling tools like 'subscribe' and 'list_subscriptions'. The purpose is specific and unambiguous.

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

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

The description implies when to use (to cancel a subscription) and notes ownership enforcement, which guides usage. However, it does not explicitly mention alternatives or when not to use, but the context from sibling tool names 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 already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is clear. The description adds value by detailing the return format (verdict, structured form, actual value, citation, delta) and the authoritative sources used (SEC EDGAR + XBRL). 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 somewhat verbose but well-structured. It front-loads the purpose with example triggers, then states the usage context, scope, return values, and efficiency benefit. Every sentence contributes useful information, but minor trimming could improve conciseness.

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 low complexity (one parameter, no output schema), the description adequately covers input, output, and usage context. It explains the return values and the domain limitation. The tool's purpose and behavior are fully described for an AI agent.

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

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

Schema description coverage is 100% for the single parameter 'claim'. The description provides examples of acceptable claims but does not add essential new information beyond the schema. With high coverage, a baseline of 3 is appropriate.

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

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

The description clearly specifies the tool's purpose: verifying natural-language factual claims against authoritative sources. It uses specific verbs like 'validate', 'verify', and 'check', and distinguishes itself from siblings by focusing on claim verification and replacing multiple sequential calls. The domain (company-financial claims via SEC EDGAR and XBRL) is explicitly stated.

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 using the tool 'whenever the agent needs to check whether something a user said is factually correct'. It notes the domain limitation ('v1 supports company-financial claims'), implying when not to use it. While no direct alternative sibling is named, the context is clear and the guidance is actionable.

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