Inegi

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

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds valuable details: it returns per-model score, confidence, signals, raw_response plus a combined view. It also discloses cost implications (free default, BYO key for Anthropic), which is beyond what annotations provide.

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

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

The description is concise yet complete, front-loaded with the primary action and output. Every sentence adds unique value: probing, scoring, default model, optional key, return format, use cases. No redundancy or fluff.

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

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

Despite lacking an output schema, the description details the return structure (per-model fields + combined view). It covers all parameters, mention of default/free option, and use cases. For a probe tool with 4 parameters and 1 required, this is fully adequate.

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

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

Schema description coverage is 100%. The description adds context beyond the schema by explaining the default model and the role of _apiKey (BYO key, pay Anthropic directly). It also clarifies the 'context' parameter's purpose for disambiguation.

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

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

The description uses specific verbs ('Probe', 'score') and clearly states the resource (LLMs) and the output (visibility 0-100 per model). It also lists concrete use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. This distinguishes it from sibling tools, though some overlap may exist with '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 provides explicit guidance on when to use the tool (AI-marketing audits, brand checks, competitive monitoring) and explains the default vs. paid model options. However, it does not explicitly mention when not to use it or contrast it directly with similar sibling tools like 'scan_competitor_ai_presence'.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. The description adds value by stating it returns structured answers with stable citation URIs, and that it routes to many sources. No contradictions.

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

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

The description is lengthy but well-organized: front-loads the key preference, lists domains, explains behavior, and gives examples. Every sentence contributes, and it's structured logically. Could be slightly more concise, but still 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?

For a complex routing tool without output schema, the description adequately explains the tool's reach (thousands of sources) and output format (citations). It provides examples. It could detail more about error handling or fallibility, but given annotations, it's largely complete.

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

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

Schema coverage is 100% and parameters are well-documented as aliases for the question. The description adds examples and context but does not significantly enhance parameter understanding 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 explicitly states the tool routes questions to thousands of tools for authoritative structured data with citations. It distinguishes itself from web search and provides specific examples of use cases (SEC filings, FDA data, stocks, etc.), making its purpose very clear.

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

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

The description gives explicit guidance to prefer this tool over web search for factual questions, lists domains, and provides example queries. It could be improved by noting when to use siblings like 'deep_research' or 'validate_claim', but overall it strongly aids agent decision-making.

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

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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description adds significant behavioral context: the refusal reasons (not_in_source, no_tool_match, etc.), the exact return fields (answer, evidence, confidence, etc.), and the guarantee that it only uses the tool result. 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 efficiently structured with the key purpose front-loaded. While it is relatively long (multiple sentences), each sentence provides necessary context about behavior, use cases, and tradeoffs. No wasted words, but could be slightly 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 (hallucination-resistance, dynamic routing, refusal behavior) and lack of output schema, the description fully covers all essential aspects: routing mechanism, output structure, refusal reasons, cost, and appropriate use cases. The agent has a complete mental model.

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

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

Schema coverage is 100%, so the schema already documents all parameters thoroughly (e.g., 'question' and its aliases). The description adds minimal additional semantic value beyond the schema, mentioning only that the question is natural language. Baseline 3 is appropriate.

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

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

The description clearly states it's a 'Hallucination-resistant answer mode for high-stakes reads' and explains the core mechanism (picks the right tool, extracts answer only from tool result). It effectively distinguishes from the sibling 'ask_pipeworx' by highlighting the additional LLM call cost and the guarantee of no invention.

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 answer will be quoted, cited, or acted on') and when to prefer the alternative ('prefer ask_pipeworx for casual lookups'). Also mentions the cost tradeoff, providing clear guidance for decision-making.

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 is extremely detailed about behavioral traits, far beyond what annotations provide. It explains fan-out examples, classifier categories, response shapes, resolver contract, parent_event extractor, news fields with fallback handling, safety mechanisms (low-confidence match short-circuit, closed/inactive market handling), wide-spread market warnings, and resolution-rule risk. Annotations indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, and the description aligns 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 long but well-structured with clear sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). It is front-loaded with the main purpose. However, some details (e.g., extensive fan-out examples) could be streamlined without losing clarity.

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

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

Given the complexity of the tool (3 params, no output schema), the description is remarkably complete. It covers status codes, resolution confidence, parent events, news fallback, safety mechanisms, and cancellation rules. Without an output schema, the response shape details are essential and well-provided.

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 value by providing examples for market_input, explaining the depth enum (quick vs thorough), and detailing the implications of include_raw (summarized vs full payloads). It goes beyond the schema's bare descriptions.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the verb (research), resource (Polymarket bet), and action (pull Pipeworx data). It distinguishes itself from sibling tools like polymarket_edges and polymarket_arbitrage by focusing on comprehensive research rather than edge tracking or arbitrage.

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

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

The description explicitly states use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It provides clear context for when to invoke the tool. While it doesn't explicitly list when not to use it, the context signals and sibling list imply alternatives exist (e.g., polymarket_edges for edge tracking).

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 key behavioral context: pulls latest 10-K from SEC EDGAR/XBRL, handles off-calendar fiscal years, returns paired data + citation URIs, sorts by primary metric. 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 front-loaded with examples and well-organized. Every sentence adds value, though slightly verbose. Could be trimmed without losing clarity, but still 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?

No output schema, so description must explain results. It mentions paired data, citation URIs, and sorting by primary metric. This is sufficient for a comparison tool; additional detail on response structure would push to 5, but current level is adequate.

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

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

Schema covers 100% of parameters with descriptions. Description adds meaning beyond schema: for 'type' enum, explains data sources (SEC EDGAR for company, FAERS/FDA for drug); for 'values' array, specifies tickers/CIKs for company and drug names. Enhances usability.

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 verb 'compare' + resource 'entities' (companies/drugs). Provides example queries and distinguishes from single-entity lookups like entity_profile by explicitly saying 'ALWAYS PREFER over sequential single-pack lookups.' Strong differentiation.

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

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

Explicit when-to-use guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Also gives concrete query examples. Implicitly indicates not for single entities. No misleading advice.

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, etc. Description adds that it never invents data, returns explicit gaps[], and cites sources with verbatim evidence and timestamps. It adds substantial behavioral context beyond annotations.

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

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

Description is front-loaded with core purpose and is well-organized. Slightly long but every sentence adds unique value; could be trimmed slightly but remains efficient.

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

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

Despite no output schema, the description thoroughly explains the return format (structured findings with evidence, confidence, source, gaps). Combined with rich annotations and full parameter coverage, it fully specifies the tool's behavior.

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

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

Schema coverage is 100%, so baseline is 3. Description adds meaning: for 'question' says 'broad/multi-part is fine', for 'depth' notes that 'thorough' needs a paid plan, which enriches the schema descriptions.

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

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

The description clearly states the tool does 'grounded multi-source research in ONE call' by decomposing questions into sub-questions and routing them in parallel across many tools. It distinguishes from sibling 'ask_pipeworx' by specifying that tool is for single 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?

Explicitly says to use for broad/multi-part questions and to use ask_pipeworx for single lookups. Also mentions depth options and that 'thorough' requires a paid plan, guiding appropriate use.

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

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

Annotations already indicate safe read-only, idempotent behavior. Description adds that it returns top-N tools with schemas and examples, ready to call directly, and that no second schema lookup is needed. 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?

Single paragraph is dense but well-structured, front-loaded with purpose. Includes examples and domain lists, but could be slightly more concise. Earns its length.

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

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

Despite no output schema and multiple parameters, the description fully explains what the tool returns (top-N tools with names, descriptions, schemas, examples) and that results are actionable. Covers all necessary context for an agent.

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

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

Schema coverage is 100% with all parameters described. Description adds examples for the 'query' parameter and clarifies it accepts multiple aliases, 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 'Find tools by describing the data or task' and explains the tool's purpose: browsing, searching, looking up tools. It distinguishes itself from sibling tools like deep_research or search_within by focusing on tool discovery rather than data retrieval.

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 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' Lists example domains. Lacks explicit when-not-to-use or alternatives, but provides clear context for 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 true, destructiveHint false. The description adds details like parallel execution, soft-fail for patents (USPTO API sunset), and specific data sources, providing significant behavioral context beyond annotations.

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

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

Description is slightly long but front-loaded with typical user queries. Every sentence is informative; could be trimmed slightly but remains efficient 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 explains the return fields (cik, company_name, filings, fundamentals, patents, news, LEI) and notes the patent soft-fail. It is complete for a complex tool, though minor details like number of filings could be clarified.

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 important constraints: 'names not supported' and instruction to use resolve_entity first, which is not in schema. This adds meaning beyond the schema.

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

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

The description uses specific verbs like 'profile' and 'research' and clearly states the tool creates a 'full cross-source profile of a US public company in ONE parallel call.' It distinguishes from siblings by recommending this tool over chaining single-pack lookups, and lists multiple data sources.

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

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

The description explicitly says to use when the user asks for a holistic view and when not to use (if only a name, use resolve_entity first). It does not list other alternatives but implies the 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 already indicate destructiveHint=true and idempotentHint=true. The description adds 'Delete a previously stored memory' which matches but does not provide additional behavioral context beyond annotations.

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

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

The description is three sentences, each earning its place: purpose, usage context, and sibling reference. No waste, front-loaded.

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

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

For a simple tool with one required parameter and no output schema, the description fully covers purpose, usage, and context. No gaps.

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

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

Schema coverage is 100% and the description does not add meaning beyond the schema's parameter description 'Memory key to delete'. 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 deletes a memory by key, using specific verb 'Delete' and resource 'memory'. It distinguishes itself from sibling tools like 'remember' and 'recall' by explicitly naming them.

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

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

The description explicitly lists when to use the tool: 'when context is stale, the task is done, or you want to clear sensitive data'. It also mentions pairing with 'remember' and 'recall', providing guidance on 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 indicate a safe read operation (readOnlyHint, idempotentHint), and the description adds behavioral details: fetches the page, extracts title/description/key links, emits standard format, output is a single text blob. This goes 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, each purposeful: first explains function and output, second lists use cases. No extraneous words, front-loaded with the core 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?

For a simple read-only tool with two parameters and no output schema, the description covers purpose, usage, behavior, and parameters comprehensively. The output format is described sufficiently.

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 value by giving an example for the 'url' parameter ('e.g. "https://example.com" or a specific landing page'), which clarifies usage beyond the schema 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 it generates a production-ready llms.txt file for any URL, specifying the verb 'Generate', the resource 'llms.txt file', and the context for AI crawlers. It distinguishes itself from siblings like 'ai_visibility_check' and 'scan_competitor_ai_presence' by focusing on generating the specific file.

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 explicit use cases: getting a client's site indexed, drafting for own project, auditing competitor's AI visibility. However, it does not explicitly state when not to use this tool or mention 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 indicate read-only, idempotent, non-destructive behavior. The description adds context about return options (latest or full history), data banks (BISE/BIE), and the optional API key. It does not contradict annotations and provides additional behavioral 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 concise with no filler sentences. Each sentence adds value: core purpose, how to find IDs, alternative source recommendation. Perfectly 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?

Given the tool's complexity (5 parameters, external API, multiple geographic levels), the description covers purpose, ID discovery, alternatives, return options, and API key. It lacks output format details, but that is acceptable without an output schema. Overall, it provides sufficient context 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 description coverage is 100%, so the baseline is 3. The description adds some contextual value (e.g., mentioning geographic levels and examples like GDP), but most parameter semantics are already well-documented in the schema.

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

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

The description clearly specifies the action ('Fetch any INEGI indicator'), the resource ('by its numeric id, at a chosen geographic level'), and the scope ('escape hatch for the full Banco de Indicadores'). It distinguishes itself from the sibling 'inegi_population' by covering a broader set of indicators.

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 an alternative ('banxico pack' for inflation, interest rates, exchange rates) and how to find indicator IDs via INEGI's Constructor de consultas. While it doesn't explicitly state when not to use the tool, the alternative recommendation is clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as true/true/false, indicating a safe read operation. The description adds that it returns the value with its reference year, but doesn't elaborate on data freshness or error handling. With annotations covering safety, this is adequate.

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

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

Extremely concise: three sentences covering purpose, usage guidance, and output. No unnecessary words, front-loaded with the core 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?

For a simple lookup tool with no output schema, the description covers purpose, usage, and output nature (value + reference year). Lacks details on return format or error scenarios, but sufficient for basic understanding.

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

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

Schema coverage is 100%, so parameters are fully documented. The description paraphrases the 'area' parameter (national, state, municipality) but adds no new meaning beyond the schema. The '_apiKey' parameter is not mentioned. Baseline score for 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?

Clearly states the tool returns total population of Mexico at national, state, or municipality level using INEGI data. Distinguishes itself from sibling tools like 'inegi_indicator' and web search by specifying geographic detail.

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 preferring this tool over web search for population queries about Mexico, Mexico City, Jalisco, etc. However, it does not mention when not to use it (e.g., for non-population demographic data).

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

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

Annotations already indicate read-only and idempotent. Description adds that by default only active subscriptions are listed, and defines return fields. 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: first covers purpose and return fields, second provides usage guidance. No filler, front-loaded with key info.

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 simple tool with one optional param and full annotations, description covers purpose, return fields, default behavior, and usage context. No missing elements.

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

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

Schema coverage is 100%, so baseline is 3. Description implicitly references active vs inactive but does not add new meaning beyond schema. Consistent.

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 'List the caller's active subscriptions' with specific verb and resource, names return fields, and distinguishes from sibling tools 'subscribe' and 'unsubscribe'.

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

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

Explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' Provides clear usage context and purpose relative to 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 annotations indicate no specific behavioral traits beyond read/write. The description adds valuable context: it's free, doesn't count against quota, rate-limited to 5 per day, and team reads digests daily. No contradictions with annotations.

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

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

The description is a concise paragraph that front-loads the purpose, then covers usage scenarios, specific instructions, and operational details. Every sentence adds value, though it could be slightly more structured with bullet points.

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

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

Given the high schema coverage and no output schema, the description adequately explains what happens (team reads, roadmap impact). It covers rate limits and quota. Sibling tools are diverse but none are feedback, so no confusion.

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

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

Schema coverage is 100%, so the schema already documents all parameters. The description adds value by providing examples for 'type' and guidance for 'message' (be specific, length limits). It also explains 'context' is optional structured context.

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

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

The description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It lists specific use cases (bug, feature/data_gap, praise) and distinguishes itself from sibling tools, none of which are feedback-related.

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

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

The description provides explicit guidance on when to use the tool (bug, feature/data_gap, praise) and what to avoid (don't paste end-user prompt). It also mentions rate limits and that it's free, but does not explicitly state when not to use it, though the context makes it clear.

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

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

Annotations already cover readOnly, openWorld, idempotent, non-destructive. Description adds value: self-aggregating, CF analytics-engine, no PII, cache details (5min-1h).

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

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

Single paragraph with bullet-like uses, no fluff. Could be slightly more structured but 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?

With one parameter and no output schema, description fully explains output concept and use cases, sufficient for an agent to use correctly.

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

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

Schema coverage is 100%. Description adds context: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.'

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 returns 'top tools, top packs, and total call volume' over windows, distinguishing it from siblings like 'discover_tools' and 'ask_pipeworx'.

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

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

Explicitly lists three concrete use cases (e.g., discovering hot data sources, confirming canonical choice), though no explicit when-not or alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds significant behavioral details beyond annotations: error conditions (no args fail), Jaccard similarity threshold, placeholder filtering, and fill_check with live CLOB depth, which are not captured by 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?

While the description is comprehensive and front-loaded with the key requirement, it is somewhat verbose. Every sentence adds value, but the length could be reduced by integrating some technical details (e.g., Jaccard, partition filter) more succinctly. Still acceptable for a complex tool.

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

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

Despite lacking an output schema, the description thoroughly explains the response structure (opportunities[], partition_check, fill_check with fields). It covers all essential aspects: modes, inputs, constraints, expected results, and links to related tools, making it complete for agent decision-making.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by explaining what each mode does, providing examples of valid slugs and seed questions, and clarifying the behavior difference (single-event vs cross-event). This goes beyond the schema field descriptions.

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

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

The description clearly specifies it finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes two distinct modes (event/topic) with specific use cases, making it easy to differentiate from sibling tools like polymarket_edges or polymarket_fill_risk.

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

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

Explicitly states the requirement for one of `event` or `topic`, and that calling with no arguments fails. Provides guidance on when to use each mode (single-event vs cross-event) and references the sibling `polymarket_fill_risk` for custom sizing, preventing misuse.

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

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

The description discloses extensive behavioral traits: segmentation into three model families, caching behavior (1h at KV level), edge calculation details (edge_pp_net after slippage, kelly_fraction capped at 0.25), diagnostics for why segments might be empty, and the effect of knobs. This adds significant context beyond the annotations, which already indicate readOnly, openWorld, and idempotent hints. No contradictions with annotations.

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

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

The description is very long and dense with technical details about model families. While it is structured with bullet points and clear sections, it is not concise. Some details may be excessive for an AI agent, and the length could hinder quick parsing. It earns a 3 for being well-structured but verbose.

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

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

Given there is no output schema, the description thoroughly explains the return value structure (by_segment with three segments, fed_candidates/fed_note, and _diagnostics with funnel counters). It also describes every field in opportunities (edge_pp_net, kelly_fraction, liquidity, spread, volume, 24h-move warning). This fully compensates for the lack of an output schema and makes the tool easy to use.

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

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

The input schema has 100% description coverage, so the baseline is 3. The description adds extra meaning beyond the schema by explaining the reasoning behind parameters (e.g., slippage_pp explanation about Polymarket fees) and how parameters interact (e.g., min_partition_leg_kelly applies to per-leg Kelly inside top_legs). This additional context justifies a score of 4.

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

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

The description clearly states the tool's purpose: 'Scan top Polymark markets and return opportunities where Pipeworx data disagrees with market price.' It uses specific verbs and resources, and distinguishes itself from sibling tools by focusing on this disagreement detection and segmentation into three model families.

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

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

The description provides explicit guidance on when to use the tool: for 'what should I bet on today' and for agents to discover opportunities without paging hundreds of markets. It also details tradeable-edge knobs (min_liquidity, max_spread_pp, etc.) and explains that Fed bets are excluded from ranking. It does not directly compare to sibling tools but offers sufficient context for usage.

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

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

Description adds significant behavioral context beyond annotations: explains response structure (tracked, expired, snapshot_dates), defines trend and decay fields, and notes snapshots are written on cache-miss. 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 and front-loaded with purpose. Every sentence adds value, though slightly lengthy. Could be slightly more concise but overall efficient.

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

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

Despite no output schema, the description fully covers return values and limitations. Explains tracked arrays, expired with lifespan_days, and snapshot_dates with gaps. Also addresses TTL and data source (daily closes). 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?

Both parameters are fully described in schema (100% coverage) and description adds context: default values, clamp range for days, and window options. Provides value beyond schema.

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

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

The description clearly states the tool's purpose: tracking edge persistence and decay over time. It distinguishes from sibling tools like polymarket_edges by focusing on historical trends and answering 'how long has this edge existed and is it shrinking?'

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: to differentiate between fresh and old edges, and notes that a 3-week-old wide edge is different. Also mentions limitations like 60-day TTL. However, does not explicitly state when not to use or list alternative tools.

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

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

Annotations indicate readOnlyHint, idempotentHint, and no destructive actions. The description details what the tool computes (top_of_book, VWAP fill price, slippage, etc.) and warns about forced directional risk and thin legs. Adds value beyond annotations by explaining behavior like walking the ladder and basket partition checks. Does not contradict annotations.

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

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

The description is relatively long but front-loaded with the core purpose. It uses clear sections (REQUIRES, SINGLE-MARKET, BASKET) to structure the content. While verbose, each sentence adds necessary detail for a complex tool. No wasted words, but could benefit from more formatting 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?

Given the complexity of dual modes and no output schema, the description provides comprehensive coverage: lists return fields for both modes, explains risk indicators (thin_legs, forced_directional_risk), and gives usage bounds. Lacks exact data types or structured output description but is sufficient for an AI agent to understand behavior.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the differing meanings of size_usd in single-market vs basket mode, side auto-detection logic, and the distinction between market and event parameters. This extra context goes beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes and explicitly positions itself as a prerequisite before using related tools like polymarket_arbitrage or polymarket_edges, differentiating from siblings.

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 usage guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' Explains why (theoretical overround not capturable on thin books, partial fills leading to unhedged directional positions), giving clear when-to-use and when-not-to-use 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, openWorldHint, idempotentHint), the description adds extensive behavior details: compatibility_warning conditions, temporal alignment, skipped types, and what the response looks like. No contradictions with annotations.

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

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

The description is well-structured with a clear first sentence and logical flow (purpose, modes, response, safety fields). It is somewhat lengthy but every sentence adds necessary detail for a complex tool.

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

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

Given no output schema, the description fully covers inputs, modes, output structure (leg-by-leg prices, top spreads), and safety fields (compatibility_warning, temporal_alignment, counters). It also warns about limitations, making it complete for agent decision-making.

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

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

Schema coverage is 100%, so baseline 3. The description adds value by explaining the two modes, providing examples, and clarifying when to use each parameter, which goes beyond the schema descriptions.

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

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

The description clearly states the tool does cross-venue spread analysis between Kalshi and Polymarket, specifies the verb 'spread', and distinguishes from sibling tools like polymarket_arbitrage by focusing on two different venues.

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 and explicit), gives examples, and warns that most pre-mapped topics are not tradeable, providing context on when to use the tool. However, it could explicitly compare to similar tools like polymarket_arbitrage for stronger 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=true, destructiveHint=false, idempotentHint=true. The description adds scoping details (anonymous IP, key hash, account ID), which is useful context beyond the annotations.

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

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

Three focused sentences: core function, usage examples, scoping/pairing. No wasted words, front-loaded with 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?

For a simple read tool with good annotations, the description covers purpose, usage, parameter, and scoping. No gaps considering no output schema is needed.

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

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

Schema coverage is 100% and the description explains the key parameter's meaning and behavior (omit to list all). This adds value beyond the schema's 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 retrieves values saved via remember or lists all keys. It distinguishes from siblings (remember, forget) and specifies the 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 when to use (look up stored context) and provides examples (ticker, address, notes). Mentions pairing with remember and forget, and scoping to identifier, giving clear context.

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

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

The description discloses the mark_read behavior, which modifies read status, contradicting the readOnlyHint annotation. This is a serious inconsistency, making transparency poor despite otherwise clear disclosure.

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

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

The description is concise with 3-4 sentences, front-loaded with the core purpose, and each sentence adds distinct value (purpose, return fields, filtering, mark_read, alternative endpoint). No fluff.

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

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

Given 5 parameters and no output schema, the description covers return fields, filtering, mark_read behavior, and provides an HTTP alternative. It lacks explanation of pagination and ordering beyond 'most recent', but overall sufficiently complete for a read tool with decent 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% with good descriptions, but the description adds behavioral context for type, since, and mark_read beyond the schema, including return fields. It does not mention limit or unread_only in the description, but the schema covers them. Moderate added value justifies 4.

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 pulls fired events from the subscription feed and returns recent alerts. It distinguishes itself from sibling tools like subscribe and list_subscriptions by focusing on reading alerts, and even provides an alternative HTTP endpoint for scripts.

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 indicates when to use the tool (to get recent alerts) and mentions polling and an HTTP alternative. However, it does not explicitly exclude other uses or compare with sibling tools, missing some explicit when-to-use/not guidelines.

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 details fan-out to multiple sources (SEC EDGAR, GDELT→GNews fallback, USPTO), includes fallback logic and soft-fail for USPTO due to API sunset, going beyond the annotations which only indicate read-only and idempotent hints.

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 example queries and is mostly concise, but contains some verbose details (e.g., USPTO sunset explanation) that could be trimmed without losing essential information.

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

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

Given the tool's complexity (multiple data sources, fallback, relative dates), the description fully covers behavior and return structure (changes[] grouped by source, total_changes count, citation URIs) without requiring an 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 is 3. The description adds usage examples for `since` (ISO date and relative shorthand) and explains `type` is limited to 'company', but does not add significant 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 the tool is a change feed for a company over a time window, with example queries like 'What's new with X' and 'updates on Acme'. It distinguishes from sibling tool `entity_profile`, which is 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?

The description explicitly advises using `entity_profile` for static profiles instead of recent changes, providing clear alternative guidance. However, it does not explicitly state when not to use the tool beyond this.

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

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

Annotations provide idempotentHint=true, destructiveHint=false. Description adds persistence details: authenticated persistent, anonymous 24-hour TTL. No contradictions, but lacks size limits or overwrite behavior.

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

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

Four sentences, each purposeful. Front-loaded with action and clear structure. 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?

For a 2-param tool with no output schema, description sufficiently explains behavior and persistence. Covers key operational aspects.

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 100% with clear key/value descriptions. Description adds scope context ('scoped by your identifier') but adds minimal 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 verb 'Save' and resource 'data...to reuse later'. Explicitly distinguishes from sibling tools 'recall' and 'forget'.

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

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

Provides explicit when-to-use: 'when you discover something worth carrying forward'. Mentions companions 'recall' and 'forget', implying 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 (readOnlyHint, idempotentHint, destructiveHint) are consistent. Description adds value by noting internal cascading calls and auto-disambiguation, which is beyond what annotations provide.

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

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

Description is well-structured with examples and supported types. Slightly long but every sentence is informative. Front-loaded with purpose in quotes.

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 2-parameter tool with full schema coverage and annotations, the description provides sufficient context about input and output per type. Missing explicit return structure but no output schema exists to require it.

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

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

Schema coverage is 100%, but description enriches parameters by providing concrete examples (e.g., 'AAPL', 'ozempic') and clarifying accepted formats per type, adding meaning beyond enum and 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?

Description clearly states the tool resolves names to canonical identifiers with specific examples (ticker, CIK, RxCUI). It distinguishes from siblings by focusing on entity resolution, which is unique among listed sibling tools.

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

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

Explicit guidance: 'Use FIRST whenever you have a name but need an ID.' Provides query examples. No mention of when not to use or alternatives, but the context is clear enough.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral details: it calls ai_visibility_check internally, ranks results by score, and returns a ranked list with score, confidence, signal density. Does not contradict annotations.

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

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

Three sentences, front-loaded with primary action. Every sentence adds distinct value: purpose, method, usefulness. No repeated or irrelevant information.

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

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

Given 4 parameters and no output schema, the description explains input semantics and output format (ranked list with score, confidence, signal density). It also covers constraints like entity count (2-8) via schema description. Missing explicit error handling, but overall adequate for an agent to use correctly.

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

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

Schema coverage is 100% with descriptions, but the description adds crucial semantics: first entity is treated as 'subject' for narrative, models default to free 'workers-ai' and require API key for 'anthropic', context disambiguates common names. This adds significant meaning beyond the schema.

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

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

The description clearly states the verb 'Compare', 'Probes', 'ranks', and 'surfaces' with a specific resource 'AI visibility across multiple entities'. It distinguishes from sibling tool 'ai_visibility_check' by describing a multi-entity comparison that internally uses that single-entity check.

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 a clear use case example: competitive AI-marketing audits. It implicitly advises use when comparing brands' AI recognition, but does not explicitly state when to avoid or mention alternatives among siblings like 'compare_entities'.

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, destructiveHint. Description adds valuable behavioral details: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed lists timeouts. No contradictions. Could mention rate limits or other constraints, but still strong.

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

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

Single paragraph with upfront purpose, then details. Every sentence carries information, but it is slightly dense. Could be broken into multiple sentences for readability, but overall efficient.

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

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

Despite no output schema, the description fully lists what is returned (summary block fields, per-advisory detail, links, recent versions). It also covers ecosystem limitations and failure modes. Given low complexity (2 params, no nested objects), the description is very complete.

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

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

Schema coverage is 100% with clear descriptions. Description adds that scoped packages are accepted for 'package' and that 'version' defaults to latest when omitted. This provides useful nuance beyond the schema, pushing above baseline 3.

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

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

Description uses specific verb 'scan' and resource 'npm package', defines it as a composite check across deps.dev and bundlephobia, and clearly distinguishes it from siblings by focusing on the npm ecosystem and the 'is it safe/popular/small' question.

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 ('is X safe / popular / small', 'what does adding lodash cost me') and when not to (other ecosystems like PyPI/Maven/Cargo/Go should use deps.dev:version directly). Provides clear context and 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 significant detail beyond annotations: specifies embedding model (BGE-base-en), window size (500-char overlapping), char cap (200K) with truncation flag, and output structure (offsets, similarity scores). 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?

Three focused sentences: first defines core function and output, second gives usage context, third details technical spec. Front-loaded with key info, zero 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?

Compensates for missing output schema by describing return format. Covers input limits, algorithm, and sibling pairing. Lacks error/empty result handling but overall adequate for 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. Description adds value with examples for 'query', clarifies 'text' is document body, and implies 'limit' controls top-N passages—slightly enriches 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 'Semantic search INSIDE a fetched record' with specific verb and resource, and distinguishes from sibling tools like ask_pipeworx_grounded by explaining pairing use-case.

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

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

Explicitly advises using when record is too large for prompt, suggests pairing with ask_pipeworx_grounded, and explains benefit of offset verification—clear 'when and 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 indicate non-readOnly, openWorld, idempotent, non-destructive. The description adds behavioral context: requires Authentication, returns a subscription id, details delivery channels with failure auto-disable for webhooks. However, it does not clarify idempotency behavior (e.g., whether duplicate subscriptions return the same id or create a new one).

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

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

The description is long but information-dense, with essential details front-loaded. It could benefit from bullet points for readability, but the structure is logical and each sentence adds value.

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

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

Given the tool's complexity (3 parameters with nested objects, multiple types, delivery options), the description covers all aspects: types, params, delivery channels, limitations, and return value. No output schema exists, but the return of an id is noted. No gaps remain.

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

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

Although schema coverage is 100%, the description enriches each parameter with practical examples, formatting, and usage context (e.g., type-specific params, delivery constraints, webhook signing secret). This adds significant value 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 verb (create), resource (proactive monitoring subscription), and that it returns the new subscription id. It lists supported types, distinguishing this from sibling tools like list_subscriptions, recent_alerts, and unsubscribe.

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

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

It specifies the requirement for a Pipeworx OAuth account and mentions that anonymous/BYO cannot persist. It provides examples for each subscription type and delivery channels with constraints (SMS cap, phone verification). While it doesn't explicitly state alternatives for one-time queries, the context is sufficient for deciding when to use this tool.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description does not need to repeat these. It adds value by describing the output format (category-bucketed example questions with tool and argument shapes) and the behavior of calling with no arguments versus a topic. No contradictions with annotations.

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

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

The description is a single paragraph but packs a lot of information efficiently. It is front-loaded with the purpose and ends with usage instructions. Slightly more structured formatting could improve, but it is 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?

Given the tool's complexity and lack of an output schema, the description adequately explains what is returned: category-bucketed example questions with tool and argument shapes, and lists the categories. It does not mention limits or pagination, but for an onboarding tool, the information 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?

The input schema has one optional parameter 'topic' with a description. The description adds meaning by listing the valid focus areas (finance, pharma, etc.) and explaining that omitting it gives a cross-category spread. This goes beyond the schema's 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's purpose: an onboarding entry point for an agent that just connected, returning category-bucketed example questions with exact tool and argument shapes. It distinguishes itself from sibling tools like discover_tools and ask_pipeworx by focusing on generating example questions rather than listing tools or answering 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 explicitly advises to use this tool first when you do not know what Pipeworx can do, and to learn how to call meta-tools. It also explains how to pass a topic for focus. However, it does not explicitly state when not to use it or mention alternatives beyond implied contrast with 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 readOnlyHint=false, destructiveHint=false, idempotentHint=true. The description adds crucial context: ownership enforcement and that the row is deactivated (not deleted), aligning with non-destructive behavior. No contradiction.

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

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

Two concise sentences with no fluff. The purpose is front-loaded, and every sentence provides value.

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

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

For a simple tool with one parameter and no output schema, the description covers ownership, deactivation behavior, and historical data availability. It is complete for its 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% with a clear description for the 'id' parameter. The description does not add significantly beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states the action ('Cancel a subscription by id') and the resource ('subscription'). It distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' by specifying cancellation.

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 explains ownership enforcement ('you can only cancel your own subscriptions') and the effect ('deactivated, not deleted'), implicitly guiding when to use this tool. However, it does not explicitly mention when not to use it or alternatives.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. The description adds valuable behavioral details: the return types (verdict, extracted form, actual value with citation, percent delta) and the efficiency claim of replacing 4-6 sequential calls. 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 front-loaded with common query patterns, then explains the tool's domain and output. It is slightly longer than necessary but every sentence serves a purpose, and the structure is logical. Could be slightly more concise, but still 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 the tool has only one parameter and no output schema, the description comprehensively covers what the tool does, what input it expects, what output to expect (verdict types, citation), and its current scope. It leaves no critical gaps for an agent to misunderstand.

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

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

With 100% schema description coverage for the single parameter 'claim', the baseline is 3. The description enriches this by explaining the supported claim types (company-financial), providing example values, and describing the output structure, which adds semantic 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's purpose: natural-language claim verification against authoritative sources, specifically for company-financial claims via SEC EDGAR and XBRL. It includes trigger phrases like 'fact check' and 'verify the claim', making it easy for an agent to recognize. It distinguishes itself by mentioning it replaces multiple sequential calls, setting it apart from siblings like ask_pipeworx_grounded or deep_research.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also notes the scope limitation to company-financial claims in v1. However, it does not provide explicit when-not-to-use guidance or directly name alternatives, though the sibling tools offer related but different capabilities.

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