Weather Gc Ca

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

Description adds behavioral context beyond annotations: 'Keyless, official ECCC data', indicates what is returned (alert name, type, etc.), and optional filtering behavior. 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?

Three sentences are compact and front-loaded with purpose, then details. Every sentence adds value without redundancy.

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

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

No output schema, but description lists return fields (name, type, risk colour, etc.), effectively covering return value. For a simple read tool with 3 optional params, description is sufficient.

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

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

Schema coverage is 100%, but description adds value: explains the ±2° box filtering for lat/lon, default/max for limit, and provides an example with coordinates.

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

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

Description clearly states the tool returns active Environment Canada weather alerts with specific fields, and optionally filtered by location. It distinguishes from sibling 'recent_alerts' by specifying 'active' alerts.

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 example usage with coordinates but lacks explicit guidance on when to use this vs. siblings like 'recent_alerts' or other weather tools. No mention of when not to use.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds significant behavioral context: probing mechanism, scoring details, free default model, BYO Anthropic key, and return structure. No contradictions.

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

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

Two sentences, each earning its place: first sentence states core purpose and output, second covers optional parameters and use cases. Front-loaded, no fluff.

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

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

Despite no output schema, the description explains return format and includes use cases. With 1 required param and clear schema, the description is 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 description coverage is 100%, baseline 3. The description adds meaning beyond schema by noting default model, when _apiKey is needed, and output structure (per-model score, confidence, signals, raw_response). This adds clear value.

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

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

The description clearly states the verb (probe), resource (LLMs), and outcome (score visibility 0-100 per model). It specifies default model and optional Anthropic probe, distinguishing it from sibling tools like scan_competitor_ai_presence.

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

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

The description explicitly suggests use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to provide _apiKey. It does not mention when not to use or compare with alternative sibling tools, but provides sufficient context.

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

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

Annotations cover safety (readOnlyHint, destructiveHint). Description adds context of routing to 3,745 tools and returning citation URIs, but doesn't disclose token limits, latency, or error handling.

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

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

Front-loaded with key guidance, every sentence adds value (domains, examples). Slightly long but earns its length; could be a touch more concise.

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

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

Given the tool's complexity (routing to many sources), the description adequately explains purpose, usage, and output format (citation URIs). No output schema but the description suffices for a QA 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 all aliases for 'question'. Description adds meaning by specifying what types of questions are suitable and providing concrete examples, compensating for the simple schema.

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

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

The description clearly states it routes factual questions to authoritative sources with citations, distinguishes from web search, and lists many example domains. It's specific with verb+resource.

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

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

Explicitly says to prefer over web search and provides detailed when-to-use examples like 'current US unemployment rate'. Lacks explicit when-not-to-use but implies for factual queries 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 readOnlyHint=true, destructiveHint=false. The description adds rich behavioral context: it uses only tool result data, returns structured refusals with specific reasons, and costs an extra LLM call. 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 detailed but front-loaded with the key purpose. It efficiently conveys behavior and use cases in a single paragraph. Minor improvement could be made by breaking into shorter sentences, but it remains clear and concise.

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

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

Despite no output schema, the description explicitly defines the return structure with fields like answer, evidence, confidence, source, fetched_at, and refusal_reason. It covers behavior, refusal scenarios, and cost tradeoffs, making it complete for high-complexity 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 clear descriptions for each alias. The description does not add additional meaning to the parameters beyond what the schema provides, such as formatting or syntax guidance. 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 identifies the tool as a 'hallucination-resistant answer mode for high-stakes reads' and distinguishes it from its sibling ask_pipeworx by noting the extra cost and use case. It explicitly contrasts with ask_pipeworx, making the purpose 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 provides explicit guidance on when to use this tool ('whenever an answer will be quoted, cited, or acted on') and when to prefer the alternative ('prefer ask_pipeworx for casual lookups'). It also lists high-stakes domains like financial verdicts and legal claims.

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

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

Description richly discloses behavioral traits beyond annotations: fan-out parallelism, response shapes, safety short-circuits for low confidence and closed markets, resolution-rule risk with cancellation rules, and tradeability warnings. Annotations already indicate readOnly, idempotent, and non-destructive, and description fully aligns with 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?

Description is very long but well-structured with labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.) and front-loaded with main purpose. The verbosity earns its place by covering many edge cases, but conciseness is sacrificed for completeness.

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 exhaustively documents return fields (result.market, analysis, evidence, parent_event, news fields), edge cases (low confidence, closed markets, wide spreads, cancellation rules), and safety mechanisms. This fully compensates for missing output schema and covers complexity of 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 descriptions for all 3 parameters. Description adds value by explaining practical context: depth defaults to thorough, include_raw controls response size (~20KB vs 50-500KB), and market input types with examples. This exceeds the baseline 3 expected from full schema coverage.

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

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

Description explicitly states it researches a Polymarket bet by pulling Pipeworx data, with specific verb 'Research' and resource 'Polymarket bet / Pipeworx data'. It clearly distinguishes from siblings by specifying use cases like 'should I bet on X' and providing unique classifiers and fan-out patterns not seen in sibling tools such as polymarket_edges or 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?

Description provides explicit when-to-use examples ('Use for ...') and detailed behavioral notes like checking market_match_confidence before trusting analysis. However, it lacks explicit when-not-to-use or direct alternatives among siblings, though the context implies differentiation.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destructiveness. The description adds 'Keyless, official ECCC data' but no further behavioral details beyond what annotations imply. Given the safety annotations, a 3 is appropriate as the description adds minimal extra transparency.

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

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

The description is extremely concise: two sentences (three clauses) that cover purpose, data fields, usage examples, and data source. Front-loaded with the core action. No extraneous words.

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

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

For a simple 3-parameter tool with no output schema, the description fully covers what data is returned (temperature, precipitation, snow with units) and how to specify the station. The 50-day limit is documented in the schema parameter description, not needed in the tool description. Complete and self-sufficient.

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

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

With 100% schema coverage, baseline is 3. The description adds value by providing concrete climate_id examples and noting the source collection, which helps the agent understand how to obtain a valid identifier. This goes beyond the schema's brief description.

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 'retrieve' (implied by 'Daily climate records'), the resource (daily climate records for Environment Canada stations), and the specific data fields (max/min/mean temperature, precipitation, snow). It distinguishes from siblings like current_observations by specifying daily historical data.

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

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

The description provides explicit use guidance: it gives example climate IDs and notes that station IDs come from the climate-stations collection. It also states the data is keyless and official. It does not explicitly list when not to use, but the context is clear enough for selection among siblings.

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

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

Annotations (readOnlyHint, idempotentHint, destructiveHint) already indicate safe, read-only behavior. The description adds behavioral details: the tool performs a single parallel call, sorts results by primary metric, and returns paired data with citation URIs. No contradictions with annotations.

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

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

The description is relatively long but every sentence adds value: example queries, usage advice, data source details, and output format. It is front-loaded with natural language triggers. Minor improvement could be more structured formatting, but it is efficient overall.

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 the return format (paired data with citation URIs) and how results are sorted. For a comparison tool, it covers sources, entity types, and constrains input size (2–5). It is sufficiently complete for an AI agent to understand inputs and expected outputs.

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

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

The input schema has 100% coverage (both parameters described). The description adds significant meaning beyond the schema: it explains that 'company' pulls financial metrics from SEC EDGAR/XBRL with proper handling of off-calendar fiscal years, and 'drug' pulls adverse-event counts, FDA approvals, and trial counts. This enhances parameter understanding.

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

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

The description explicitly states that the tool performs side-by-side comparisons of 2–5 companies or drugs in a single parallel call. It provides example queries ('Compare X and Y', 'X vs Y') that immediately clarify the purpose, and distinguishes it from sibling tools by advising preference over sequential single-pack lookups.

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

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

The description clearly advises when to use this tool (comparing entities) and explicitly recommends it over sequential lookups. It details the data sources for each entity type (SEC EDGAR for companies, FAERS for drugs) and how results are sorted, though it does not explicitly state when not to use it (e.g., for non-comparative queries).

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable context about data source (Environment Canada), keyless access, and returned fields, which enhances behavioral understanding without contradiction.

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

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

The description is a single, efficient paragraph that includes essential information: purpose, returned fields, example, and key characteristics. Every sentence earns its place with no verbosity.

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

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

Given no output schema, the description lists returned fields (temperature, dewpoint, humidity, etc.) and provides an example. It is complete for a simple weather tool, though it could mention whether results are sorted or paginated.

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 reinforces the latitude/longitude example and mentions the limit, but adds limited new 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 clearly states it returns 'Latest real-time surface weather observations' and lists specific fields. It distinguishes from siblings like 'climate_daily' by emphasizing 'real-time' and 'keyless, official ECCC data'.

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

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

The description implies usage for current conditions with 'real-time' and gives an example, but no explicit guidance on when to use this tool versus alternatives, nor does it specify when not to use it.

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

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

Annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) are supplemented by description details: tool 'never invented', returns 'explicit gaps[]' for unanswered facets, includes 'verbatim evidence, confidence, source, fetched_at, pipeworx:// citation', and expects 15-60s response time. No contradiction with annotations.

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

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

The description is front-loaded with main purpose and key features, then details prerequisites and behavior. While slightly verbose, every sentence provides useful information for agent decision-making. Could be tightened but overall 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?

Despite no output schema, the description fully explains the return format: 'structured findings packet' with 'verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, and explicit gaps[]'. It also covers runtime expectation, account requirements, and error avoidance (never invented). Complete for a complex research tool.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds extra value by explaining the depth parameter's facet counts (quick=3, standard=5, thorough=8) and requiring a paid plan for 'thorough'. It also clarifies the question parameter accepts natural language and broad/multi-part queries.

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

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

The description clearly states the tool's purpose: 'Grounded multi-source research in ONE call.' It explains the decomposition and parallel routing across many sources, and explicitly distinguishes from sibling tool 'ask_pipeworx' for single lookups. The verb 'research' and resource 'multi-source grounded answers' are specific.

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

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

The description provides explicit when-to-use guidance: 'Use for broad or multi-part questions... use ask_pipeworx for single lookups.' It also mentions prerequisites (Pipeworx account, paid plan for 'thorough'), which helps the agent determine applicability and constraints.

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. The description adds that results include full input schemas with curated examples and are ready to call directly—no second lookup needed. This provides operational detail beyond annotations.

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

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

The description is front-loaded with purpose and usage, then lists domains and output details. Though slightly long, every sentence serves a function—no fluff. Well-structured for readability.

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

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

For a tool with 6 parameters and no output schema, the description covers purpose, when to use, parameter behavior (aliases, limit), and output format (top-N tools with schemas). Lacks ranking or error info but is sufficient 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 coverage is 100%, so baseline is 3. The description adds that 'query' accepts aliases (task, q, description, search) and provides natural language examples, but these are also in the schema descriptions. Marginal added value.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It specifies the verb and resource, and distinguishes from siblings (which are specific domain tools) by positioning itself as a discovery layer.

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

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

Explicit guidance: 'Use when you need to browse, search, look up, or discover what tools exist…' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This tells when and why to use it over other 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 indicate read-only, open-world, idempotent, non-destructive. Description adds behavioral context: fans out across multiple sources, patents 'soft-fails until reactivated', and returns specific fields. 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 thorough but slightly verbose; however, every sentence contributes value. It starts with examples and then details the capabilities. Could be more concise, but still 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 no output schema, the description fully specifies what the tool returns: CIK, company_name, recent_filings (with URIs), fundamentals, patents, news, LEI. It also notes limitations (only company type, patents sunset). This is 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 coverage is 100%, and the description adds meaningful context beyond the schema: explains that 'value' accepts ticker or zero-padded CIK, and that 'names not supported'. This helps the agent understand parameter usage.

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

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

The description explicitly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call'. It lists example queries and details the outputs, clearly distinguishing it from sibling tools like resolve_entity.

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

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

Provides explicit guidance: 'ALWAYS PREFER over chaining single-pack ... lookups when the user asks for a holistic view'. Also states what not to pass ('names not supported') and recommends using resolve_entity first if only a name is given.

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

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

Annotations already provide destructiveHint=true. Description adds context about why to delete (sensitive data, stale context) but no additional behavioral details beyond what annotations indicate.

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

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

Three efficient sentences front-loading purpose, then usage conditions, then related tools. 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?

Fully adequate for a simple single-parameter delete tool with good annotations. Pairing advice with remember/recall adds helpful context.

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 'key' as 'Memory key to delete' at 100% coverage. Description adds no extra parameter meaning.

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

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

Clearly states 'Delete a previously stored memory by key.' Verb+resource is specific. Distinguishes from siblings by noting pairing with 'remember' and 'recall'.

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

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

Explicitly lists use cases: stale context, task done, clearing sensitive data. Mentions related tools, giving 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 provide readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral details: fetching the page, extracting title/description/key links, and emitting standard markdown format. This adds value beyond annotations without contradiction.

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

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

The description is two sentences: first describes what the tool does and its output, second lists use cases. It is concise, front-loaded, and free of unnecessary words.

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

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

No output schema is provided, but the description states the output is a single text blob in standard llms.txt markdown, which is adequate. The tool is simple, and annotations cover safety and idempotency. Minor gap: does not specify if boilerplate is included.

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

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

Input schema has 100% coverage for both parameters (url and max_links) with descriptions. The tool description does not add significant extra meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool generates a 'production-ready llms.txt file' for AI crawlers, specifying the verb (generate), resource (llms.txt file), and context (for sites to be indexed). It distinguishes from siblings by mentioning specific AI crawlers and the standard format.

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

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

The description explicitly lists use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' This provides clear guidance on when to use it, though it does not mention when not to use or alternatives among siblings.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds useful behavior (returns specific fields, default active subscriptions, parameter for inactive) without contradicting annotations.

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

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

Two sentences with no wasted words. First sentence states purpose and return fields, second provides usage guidance. 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?

For a simple tool with one optional parameter and no output schema, the description covers purpose, return fields, and usage context adequately. Could mention error conditions but not critical.

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

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

Schema coverage is 100% for the single parameter, so the description adds little beyond restating default behavior. 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 'List the caller's active subscriptions' with a specific verb and resource. It lists the return fields and distinguishes itself from sibling tools like subscribe, unsubscribe, and active_alerts.

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 this to review what you're monitoring before adding more or to find an id to cancel,' providing clear context for when to use the tool, though it doesn't exclude alternatives explicitly.

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

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

Annotations are all false, so description carries burden. It adds rate limiting and quota-free info, which are behavioral details. It could mention if feedback is anonymous or what happens after submission, but the given info is sufficient for safe use.

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

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

The description is concise at 4-5 sentences, front-loaded with purpose, then usage, then tips, then constraints. Every sentence adds value, no redundancy.

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

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

Given the tool's simplicity (3 params, no output schema), the description covers purpose, usage, and constraints well. It lacks explicit mention of the output format or confirmation, but that is not critical for a feedback tool.

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

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

Schema has 100% coverage with descriptions for all parameters. The description adds valuable guidance for the 'message' parameter, advising users to describe issues in terms of Pipeworx tools and not paste prompts. This exceeds the schema's plain description.

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 is for sending feedback to the Pipeworx team about bugs, missing features, or praise. It distinguishes itself from sibling tools like ask_pipeworx by focusing on feedback rather than queries.

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

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

The description provides explicit when-to-use scenarios (bug, feature/data_gap, praise) and what not to do (don't paste end-user prompts). It also mentions rate limits (5 per day) and quota-free usage, giving clear guidance on usage constraints.

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, open-world, idempotent, non-destructive behavior. The description adds value by disclosing caching (5min-1h), data source (CF analytics-engine), and absence of PII. This goes beyond the annotations and provides practical usage details.

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

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

The description is three sentences long, front-loaded with the core purpose, followed by use cases and technical details. Every sentence adds value. Slightly verbose due to the list of use cases, but still 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?

For a simple tool with one optional parameter and no output schema, the description explains the return structure (top tools, top packs, call volume) and caching behavior. It is complete for the tool's complexity, though an explicit note about the response format could be slightly more helpful.

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

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

The schema covers the one parameter with 100% coverage, including an enum and description. The description adds semantic value: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This contextual nuance helps the agent choose the appropriate window.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a recent window. It uses specific verbs ('calling on', 'returns') and distinguishes from siblings like 'discover_tools' and 'ask_pipeworx' by focusing on trending data.

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

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

The description lists three concrete use cases (discovering hot data sources, confirming canonical tool, checking alignment). It implies when to use but does not explicitly state when not to use or mention alternatives beyond the listed use cases. However, it provides good context.

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

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

Beyond annotations (readOnlyHint, etc.), the description details how the tool processes data, including semantic anchor Jaccard similarity, partition filter, fill check against live CLOB depth, and response structure. No contradictions with annotations.

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

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

The description is lengthy but well-structured, starting with the most critical requirement and logically progressing through modes, filters, and response. Every sentence adds value, though minor condensation 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 of arbitrage detection and the absence of an output schema, the description thoroughly covers inputs, processing logic (including edge cases like placeholder filters and low similarity), output structure, and the fill check. It provides complete guidance for correct 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 descriptions cover both parameters, but the tool description adds practical examples (e.g., event slug format, topic seed question), explains the behavior of each mode, and clarifies the requirement to provide one of them. This adds significant meaning 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 finds arbitrage opportunities on Polymarket using monotonicity violations and partition-sum checks. It distinguishes itself from sibling tools like polymarket_fill_risk by specifying its own function and mentioning the sibling for custom sizing.

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 that one of `event` or `topic` is required, and explains when to use each mode. It also directs users to `polymarket_fill_risk` for custom sizing, providing clear alternatives and context.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint; description adds caching behavior, diagnostics for empty segments, and threshold knobs. 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?

Very detailed but overly verbose; front-loaded purpose but long technical paragraphs reduce scannability.

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?

Extremely complete: covers all three segments, internal logic, edge cases (Fed bets), diagnostics, and parameter knobs, compensating for lack of output schema.

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

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

100% schema coverage with descriptions that add concrete examples (e.g., min_kelly 0.005, slippage 20-50bp) and explain interactions between parameters.

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

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

Description states it scans Polymarket markets and returns opportunities where Pipeworx data disagrees with market price, with three model families. Clearly distinct 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?

Implicitly suggests use for daily betting decisions, mentions knobs to filter, but lacks explicit when-not-to-use or comparison to alternatives like polymarket_edge_tracker.

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

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

Annotations already declare read-only, idempotent, and not destructive. Description adds specific behavioral details: 60-day snapshot TTL, decay from daily closes not intraday, and data freshness gaps due to cache misses. No contradiction with annotations.

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

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

Description is a single paragraph containing all necessary information but is somewhat verbose. It front-loads purpose and response structure but could be more concise or 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?

Tool has no output schema, so description fully documents return values: tracked[], expired[], snapshot_dates[]. It also covers limits, data source, and behavior of each field, making it complete for agent usage.

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

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

Schema covers both parameters with descriptions (100% coverage). Description adds context about default values, max lookback, and explains that gaps in snapshot dates indicate no scan that day, adding meaning 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?

Description clearly states it tracks edge persistence and decay over time, using daily snapshots. It compares fresh vs old wide edges, differentiating from sibling `polymarket_edges` which likely provides 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 explains when to use: to answer how long an edge has existed and if it's shrinking. It implies context for trade decisions but does not explicitly list when not to use or name alternatives beyond implied comparison.

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

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

Annotations declare readOnlyHint, idempotentHint, etc. Description adds behavioral details: walks the ladder, returns specific metrics like top_of_book, vwap_fill_price, slippage_pp, verdict, and warns about forced directional risk. 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?

Description is fairly lengthy and includes some redundancy (e.g., repeated mention of defaults). However, it is well-structured with clear headings and bullet points, aiding readability. Could be more concise.

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

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

Given the tool's complexity (two modes, many return fields) and lack of output schema, the description is comprehensive. It covers all return fields, explains logic for both modes, and warns about edge cases like thin legs and directional risk.

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

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

Schema coverage is 100%. Description adds context for size_usd: explained differently for single-market vs basket. Side defaults are clarified (esp. basket auto-detection). This adds meaning 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 performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes. It specifies the return fields, making it highly specific and differentiated from siblings like polymarket_arbitrage and polymarket_edges.

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

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

Explicitly advises 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' Explains the risks of theoretical overround and partial basket fills, providing clear when-to-use and why.

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

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

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses crucial behaviors: compatibility_warning conditions, temporal alignment, skipped cross-type/subtype counters, and realistic expectations about most mappings being non-tradeable. This gives the agent a thorough understanding of the tool's limitations and outputs.

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

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

The description is front-loaded with the core purpose and then details. It is somewhat lengthy due to the complexity of the tool, but every sentence adds necessary context (modes, response, safety fields). Could be slightly more concise, but appropriate for the domain.

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

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

Given no output schema, the description fully explains the response structure (leg-by-leg prices, top spreads, safety fields) and edge cases (compatibility warnings, temporal alignment). It provides complete guidance for an agent to understand what to expect and how to interpret results.

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

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

Schema description coverage is 100%, so parameters are already well-defined. The description adds extra value by explaining the two modes (topic vs explicit), the override behavior, and the fact that topic shortcuts may rarely yield results. This goes beyond the schema's syntactic 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 computes cross-venue spread between Kalshi and Polymarket for the same question. It distinguishes itself from siblings like polymarket_arbitrage by its unique cross-venue focus. The purpose is specific and actionable.

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 two modes (topic shortcuts vs explicit pairing) and provides clear context on when to use each. It also warns that pre-mapped topics often yield no tradeable spreads. However, it does not explicitly contrast with alternative tools like polymarket_arbitrage, leaving some inference to the agent.

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 cover safety (readOnly, idempotent, not destructive). Description adds context: retrieves from 'remember', lists keys, scoped to identifier. No contradictions.

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

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

Four sentences, front-loaded with purpose. Every sentence adds value without redundancy. Efficient and clear.

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

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

Given low complexity (one optional param, no output schema), the description fully covers purpose, usage, scope, and relationships with sibling tools. No gaps.

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

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

Schema coverage is 100% and already includes the behavior of omitting the key to list all keys. Description adds no new parameter info beyond what schema provides.

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

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

The description clearly states the verb (retrieve/list) and resource (saved values/keys). It explicitly distinguishes from siblings like 'remember' and 'forget' by mentioning them.

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

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

Provides context on when to use (look up previously stored context) and how to list all keys (omit key argument). Mentions scoping to user identifier. Could add explicit when-not to use, but still clear.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds valuable behaviors: mark_read flag effects, polling suitability, and same feed at a GET URL.

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

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

Four concise sentences with front-loaded main purpose. No superfluous 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?

Explains return fields and provides alternative access, but does not detail result ordering or behavior with combined filters. Acceptable for a tool with no output schema.

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

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

Schema coverage is 100%, so baseline 3. Description adds context like e.g. 'sec_8k' for type and clarifies mark_read semantics, providing extra 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?

Clearly states it pulls fired events from subscription feed with specific return fields and filtering options. Differentiates by mentioning alternative access via GET endpoint.

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 context like 'Polls work fine' and mentions alternative use for scripts/dashboards, but does not explicitly contrast with sibling tools like active_alerts.

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

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

Beyond annotations (readOnly, idempotent), description details fan-out logic, fallback behavior between GDELT and GNews, soft-failure for USPTO, and output structure. No contradiction with annotations.

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

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

Description is well-structured with examples first, then details, then alternatives. Each sentence adds value, but could be slightly more concise for a direct answer.

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

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

Given the complexity of the tool (multiple sources, fallback, output shape), the description covers all essential aspects: input parameter behavior, output fields, 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?

Even though schema coverage is 100%, the description adds meaning: explains relative shorthand for 'since', clarifies 'type' only accepts 'company', and shows input formats for 'value'. Provides typical usage recommendation.

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

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

Description explicitly states the tool's purpose as a change feed for a company, provides example queries, and differentiates 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 guidance on when to use (e.g., what's new, latest) and directs to use entity_profile instead for static profiles. Implicitly covers when not to use by offering an alternative.

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

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

Annotations provide readOnlyHint=false, idempotentHint=true, destructiveHint=false. Description adds scoping by identifier, persistence differences (authenticated vs anonymous). No contradictions.

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

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

Four sentences, all informative. Front-loads purpose, then usage, then details. 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 (save/retrieve), the description fully covers purpose, parameters, usage, and pairing. No output schema needed; behavior 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?

Schema covers all parameters (key, value) with descriptions. The description adds usage examples for key and clarifies value as any text, providing context beyond the schema.

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

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

The description clearly states the tool saves data for later reuse, specifies the resource (key-value pair) and action (save). It distinguishes from siblings like 'recall' and 'forget' by mentioning them.

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

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

It explicitly describes when to use (discovering something worth carrying forward) and mentions alternatives ('Pair with recall to retrieve later, forget to delete'). It also notes scope and persistence differences.

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 it's a safe, read-only, idempotent operation. Description adds that it cascades through multiple endpoints internally, replacing 2-3 manual lookups, offering useful behavioral insight beyond annotations.

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

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

Description is well-structured, starting with examples, then purpose, then details. Every sentence adds value, though it is slightly lengthy. Overall efficient and clear.

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

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

Given no output schema, the description thoroughly explains return values for both entity types, including specific fields and citation URIs. It covers input formats and internal behavior, making it self-contained and complete.

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

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

Schema coverage is 100%, but description adds rich detail: for company, it returns ticker, CIK, company_name, and citation URI; for drug, it returns RxCUI, ingredient, brand, and citation. It also clarifies accepted input formats, significantly enhancing parameter understanding.

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

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

Description clearly states the tool resolves a user-spoken name to a canonical identifier, with specific examples and a directive to use first when needing an ID. It distinguishes itself from siblings by being the primary entity resolver.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID', providing clear context. It lists supported types but does not explicitly exclude scenarios or mention alternatives, though the tool's purpose is self-evident.

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

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

Adds context beyond annotations: probes each entity with ai_visibility_check, provides ranked list with score, confidence, signal density. No contradictions. Annotations already declare readOnly/ idempotent/ non-destructive, and description aligns.

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 concise sentences: first states function, second adds usage context and output description. No superfluous content. Front-loaded with key purpose.

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

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

Covers purpose, process (probe, rank), example use, and output fields (score, confidence, signal density). Without output schema, description adequately explains return value structure. Sufficient for given 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?

All parameters described in schema (100% coverage). Description adds meaning: 'First entry treated as subject', 'context disambiguates common names', default model is workers-ai. Goes beyond 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?

Clearly states it compares AI visibility across multiple entities side-by-side, ranks by score, and identifies most/least recognized. Distinguished from sibling 'ai_visibility_check' by being multi-entity comparative.

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 suggests use for competitive AI-marketing audits with example question. Does not explicitly state when not to use but implies comparative context. Lacks mention of alternatives, though sibling differentiation provides implicit guidance.

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

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

Annotations already indicate readOnly, idempotent, openWorld, and non-destructive. The description adds that bundlephobia measurements can take 5-30s on first use, partial failures degrade gracefully, and a sources_failed field is included—valuable 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 relatively long but every sentence adds value. It is front-loaded with the core composite purpose, then usage, output details, and limitations. Slightly verbose but well-structured for the information density.

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

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

Given no output schema, the description thoroughly covers return values: summary block fields (is_latest, license, etc.), per-advisory detail, links, alternative versions, and failure modes. For a composite tool with partial failure, this is complete.

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

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

Input schema has 100% description coverage. The description reinforces that 'package' is required and accepts scoped names, and 'version' defaults to latest, but adds little beyond the schema definitions.

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 is a composite check for npm packages covering license, advisories, version history, and bundle size. It uses specific verbs ('fans out', 'returns') and distinguishes from sibling tools like 'deps.dev:version' for other ecosystems.

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: when an agent asks about safety, popularity, or size of an npm package. Also specifies the NPM-only scope, directing other ecosystems to alternative tools, providing clear when-not guidance.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds significant behavioral details: uses BGE-base-en embeddings + cosine similarity over 500-char overlapping windows, has a 200K char limit with truncation and flagging. 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 three sentences, each serving a distinct purpose: 1) core function, 2) usage guidance, 3) technical details. No wasted words. Front-loaded with the essential action.

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 specifies return values: top-N passages with character offsets and similarity scores. It also covers pairing, embedding model, window size, and truncation. For a tool with 3 parameters and no output schema, this is complete and informative.

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

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

Schema coverage is 100% with clear descriptions for each parameter (text, limit, query). The description does not add new semantics beyond the schema; it re-emphasizes the character cap and mentions the natural-language nature of query, but the schema already provides examples. Baseline 3 is appropriate given high schema coverage.

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?

Name 'search_within' and title 'Search Within a Source' are clear. Description states 'Semantic search INSIDE a fetched record' with concrete examples (SEC 10-K body, article, tool result). Distinguishes from sibling 'ask_pipeworx_grounded' by mentioning pairing. 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?

Explicit when-to-use: 'Use when the record is too big to cram into the prompt' and why it's beneficial: saves context, returns only relevant passages with offsets for verification. Also provides a usage pattern: 'Pairs with ask_pipeworx_grounded'. Clear guidance on when to choose this tool over alternatives.

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

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

The description reveals key behavioral traits beyond annotations: phone verification requirement, 10/day SMS cap, webhook auto-disable after 10 failures, and that the feed is always on. Annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=false) are consistent and the description adds valuable context.

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

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

The description is well-structured with a clear opening sentence, then requirements, type examples, and delivery details. It is slightly dense but every sentence adds value. Could be slightly more concise by relying more on schema descriptions, but it is effective.

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

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

Given 3 parameters, no output schema, and the tool's complexity, the description covers input requirements thoroughly, including type-specific parameter shapes. It lacks mention of idempotency (though annotated) and does not detail the output beyond an id, but these are minor gaps for a tool with good annotations.

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

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

Schema coverage is 100%, but the description adds substantial meaning: for each type, it gives real-world examples (e.g., sec_8k with items codes, polymarket_edge with topic:'fed') and explains delivery channel constraints. This goes far beyond the schema's basic 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 verb 'create', the resource 'proactive monitoring subscription to a live-data event stream', and the output 'Returns the new subscription id'. It distinguishes from sibling tools like list_subscriptions and unsubscribe by focusing on creation and including supported event types.

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

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

The description provides explicit prerequisites (Pipeworx OAuth account) and type-specific examples. It details delivery channels and constraints (phone verification, SMS cap, webhook auto-disable). It does not explicitly state when not to use, but the context and sibling tools like recent_alerts imply when to use subscriptions vs. pulling alerts.

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, openWorldHint, idempotentHint, and destructiveHint false. The description adds behavioral context by explaining that the tool returns dynamically drawn examples from the live catalog and does not perform actions beyond suggestion. It aligns with annotations and provides useful extra context about the return format and dynamic nature.

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 structured with initial common questions, then purpose, output details, usage instructions, and when to use. While it is relatively long, each sentence serves a purpose and the information is front-loaded with the key intent. It could be slightly more concise, but overall it's well-organized and comprehensive.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description fully covers what an AI agent needs: purpose, output content, parameter usage, and relationship to sibling tools. It provides sufficient context for correct invocation and selection, 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?

The input schema defines one optional parameter 'topic' with a description listing possible values. The description adds value by providing concrete examples (finance, pharma, betting) and explaining the effect of omitting the parameter (full spread). This enriches the semantic understanding beyond the schema alone.

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

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

The description clearly states the tool's purpose as an onboarding entry point for new users to discover what to ask Pipeworx. It specifies that it returns category-bucketed example questions with exact tool and argument shapes, and distinguishes itself from sibling tools like ask_pipeworx and entity_profile by noting it should be used first when learning about the platform.

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 to use this tool first when you don't know what Pipeworx can do, and to learn how to call meta-tools. It also lists example topics for focused usage and mentions when to omit the topic parameter. This provides clear guidance on when and how to use the tool versus 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?

Adds key behavioral details beyond annotations: ownership enforcement, deactivation (not deletion), and link to recent_alerts for history. Annotations already indicate non-destructive and idempotent; description enriches context.

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

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

Two focused sentences, no fluff. Every sentence contributes essential information about action, constraint, and effect.

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 single-parameter tool with no output schema, the description provides necessary context: ownership, data lifecycle, and tie to recent_alerts. Complete for agent usage.

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

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

Schema covers the 'id' parameter fully, and description adds no new meaning beyond what is already in the schema description. 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?

States 'Cancel a subscription by id' with specific verb and resource. Differentiates from subscribe, list_subscriptions, and recent_alerts by detailing deactivation vs deletion and ownership enforcement.

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

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

Explicitly says ownership is enforced, so only the subscription owner can cancel. Mentions that historical events remain available via recent_alerts, guiding when to use that alternative.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable context: the domain limitation (company-financial, US public companies), supported metrics (revenue, net income, cash position), data source (EDGAR + XBRL), and return types (verdicts, citations, delta). It also notes the efficiency gain over multiple calls. This enriches beyond annotations without contradiction.

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

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

The description is front-loaded with trigger phrases, then states purpose, limitations, and output. Each sentence serves a purpose. It could be slightly more concise, but it remains well-organized and avoids 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 single parameter and presence of annotations, the description covers the tool's functionality, usage context, input domain, output format, and rationale. It lacks details on error handling or edge cases, but the richness of the remainder makes it adequate for an agent to select and use the tool correctly.

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

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

Schema coverage is 100% with a good description. The tool description further clarifies what kinds of claims are acceptable (company-financial) and provides natural language examples, adding domain-specific constraints beyond the schema. This helps the agent form correct inputs.

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 natural-language claim verification against authoritative sources, specifically company-financial claims using SEC EDGAR + XBRL. It provides trigger phrases and a verb+resource structure, making the purpose unmistakable. While it doesn't explicitly compare to sibling tools, its highly specific scope naturally distinguishes it.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and immediately constrains to company-financial claims. It gives examples of queries. It does not name alternative tools for other claim types, but the scope is clear enough to guide appropriate use.

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