Seatgeek

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, indicating safe, non-destructive behavior. The description adds value by detailing the return structure (per-model {score, confidence, signals, raw_response} + combined view) and noting that Anthropic calls are billed directly to the user. 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 that effectively front-loads the core purpose and key details. It is concise without redundancy, though splitting into a bullet list for return structure could improve scannability. Still, every sentence earns its place.

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

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

Given the parameter count (4) and lack of output schema, the description adequately explains the return values and use cases. It covers the main behavioral aspects, but does not discuss error handling, rate limits, or pagination. However, annotations cover safety, so completeness is good.

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

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

All 4 parameters are described in the schema with coverage 100%. The description adds meaning beyond the schema: it explains that _apiKey is only needed for Anthropic, the default model, and that context helps disambiguate entities. This provides clear semantic guidance for parameter usage.

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

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

The description clearly states the tool probes LLMs for brand knowledge and scores visibility (0-100). It specifies the default model and the optional Anthropic probing. This differentiates it from sibling tools like ask_pipeworx (which queries a single LLM) and scan_competitor_ai_presence (which might be similar but less specific). The use cases (AI-marketing audits, pre-launch brand checks) further clarify purpose.

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

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

The description explains when to use the tool (brand visibility checks) and how to configure it (default model, optional Anthropic with BYO key). However, it does not explicitly state when not to use it or directly compare it to sibling tools like scan_competitor_ai_presence, leaving some ambiguity for the agent.

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

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

The description adds context beyond annotations by explaining that the tool routes questions to 2,793 tools and returns structured answers with citation URIs. Annotations already indicate readOnly, openWorld, idempotent, non-destructive. No contradictions found.

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

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

The description is comprehensive but somewhat lengthy. It is well-structured with a clear opening instruction ('PREFER OVER WEB SEARCH'), a list of sources, and examples. The key information is front-loaded, making it efficient for an agent to parse.

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, no output schema, and comprehensive annotations, the description fully covers what the tool does, when to use it, and how it behaves. It includes examples and sources, making it complete for an agent to select and invoke correctly.

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

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

The input schema has 100% description coverage for the single parameter 'question', which is described as 'Your question or request in natural language'. The description does not add significant meaning 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 tool's purpose: answering factual questions about real-world entities and structured data. It distinguishes itself from web search by saying 'PREFER OVER WEB SEARCH' and lists specific domains like SEC filings, FDA drug data, etc., making the purpose highly specific.

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

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

The description provides detailed guidance on when to use this tool, including explicit examples and phrases like 'what is', 'look up', etc. It also suggests preferring it over web search. However, it does not explicitly state when not to use it, such as for subjective or creative queries.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. The description adds value by explaining the fan-out behavior to different packs based on classification (crypto price, Fed rate, etc.) and the include_raw parameter behavior (summarized vs full payloads). No contradictions. Could detail error handling or rate limits.

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?

Every sentence adds value. The description front-loads the core purpose, then details input formats, behavior, and use cases. It is well-structured and appropriately sized for the tool's complexity.

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

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

Despite no output schema, the description adequately explains the output: evidence packet plus market-vs-model comparison. It covers classification, fan-out logic, and parameter details. Essential for an agent to understand usage and expectations.

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

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

Schema coverage is 100%, and the description adds meaningful context: for market, it explains three input formats and gives examples; for depth, it defines quick vs thorough; for include_raw, it explains size implications and use cases. This adds significant guidance beyond the schema.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input formats (slug, URL, question text) and output (evidence packet plus market-vs-model comparison). It distinguishes itself from siblings by being the core demo product that automatically fans out to appropriate packs.

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

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

The description provides explicit use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?' It also implies when to use (for Polymarket bets) and notes that agents using this tool convert better. It does not explicitly state when not to use or list alternatives, but the context is clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds value by detailing data sources (SEC EDGAR/XBRL, FAERS) and output format ('paired data + pipeworx:// citation URIs'). Could mention potential for missing data or error handling, but overall good coverage.

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 clear sequence: purpose, usage triggers, per-type behavior, output expectations, efficiency claim. No redundant or vague sentences; every sentence adds 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?

Tool has moderate complexity with two types and diverse data sources. Description explains what data is returned and the presence of citation URIs, but lacks details on result structure (e.g., ordering, table format). No output schema exists, so description could be slightly more explicit about how paired data is presented.

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

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

Schema has 100% coverage; description expands by explaining enum values 'company' and 'drug' map to specific data pulls (financial metrics vs. adverse events/approvals/trials). For 'values', provides format expectations (tickers/CIKs for company, drug names for drug) and concrete examples. Adds clarity beyond the bare schema.

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

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

Description clearly states verb 'compare' with resource '2–5 companies or drugs' and specifies entity types. Distinguishes from sibling tools like entity_profile by emphasizing multi-entity side-by-side comparison, and mentions it replaces 8–15 sequential calls, making its unique value 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?

Explicitly lists trigger phrases ('compare X and Y', 'X vs Y', 'how do X, Y, Z stack up', 'which is bigger') and gives specific use cases (tables/rankings of financials or adverse events). Explains data sources per type (SEC EDGAR/XBRL for companies, FAERS for drugs) and provides examples of valid input values (AAPL, MSFT, ozempic, mounjaro).

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, no destruction. Description adds that it returns top-N tools with names and descriptions, but this is predictable. Adequate but no extra value beyond annotations.

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

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

Purpose is front-loaded, usage context follows, and examples are included. Could be slightly more concise by moving examples to schema, 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?

No output schema, so description explains it returns top-N tools with names and descriptions. Low complexity, all necessary information is present.

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%; both parameters are described in the schema. The description does not add any new semantic meaning beyond what the schema provides.

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

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

Clearly states the verb 'find' and resource 'tools', and distinguishes itself from siblings by being the discovery tool to call first when exploring available options.

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

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

Explicitly instructs to call this tool first when many tools are available, and provides examples of use cases and domains. Could mention when not to use but strong overall.

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 safe, idempotent read operation. Description adds value by detailing returned data (SEC filings, revenue, patents, news, LEI with citation URIs), but does not mention error handling or edge cases.

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 covering purpose, usage examples, and return details. Front-loaded with main action, no redundant words. Efficient and well-structured.

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

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

Given the tool's complexity (aggregates from multiple sources), the description is comprehensive: lists returned data types, mentions citation format, and specifies parameter constraints. No missing critical information.

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

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

Schema has 100% coverage, but description adds significant meaning: explains that 'value' can be ticker or CIK, notes that names are not supported, and directs to resolve_entity. This supplements the schema descriptions effectively.

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 'Get everything about a company in one call' with a specific verb and resource. It distinguishes itself from sibling tools by listing the consolidated data sources (SEC EDGAR, SEC XBRL, USPTO, news, GLEIF).

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 example queries ('tell me about X', 'give me a profile of Acme') and states when to use (when you'd need to call many tools). Also advises to use resolve_entity first if only a name is available, giving an alternative.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the description does not need to repeat these. However, it adds no additional behavioral context (e.g., error handling, data freshness), which would be valuable.

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?

At two words, the description is extremely concise, but at the cost of informativeness. It is appropriately sized for a trivial tool? Given the context signals, it is under-specified.

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

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

With an output schema present, return values are documented, but the description lacks context about the event domain (e.g., sports, prediction markets) and when to prefer this over siblings. For a tool with one parameter and zero schema coverage, the description is insufficient.

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

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

Schema description coverage is 0% and the description provides no explanation for the 'id' parameter beyond the schema type and example. The description should compensate but fails to do so.

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 'Single event.' indicates the tool returns one event, but lacks a specific verb (e.g., 'get' or 'fetch') and does not clarify what kind of event compared to sibling 'events' tool. It is better than a tautology but still vague.

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

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

No guidance is provided on when to use this tool versus alternatives (e.g., 'events' for multiple events, or 'venue' for location data). The agent receives no contextual hints for selection.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive traits. The description adds no behavioral context beyond what the annotations provide, such as result format or query flexibility.

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

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

The description is extremely concise at two words, which is not necessarily a virtue when substance is missing. It lacks any structure, but it is front-loaded as a single statement.

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 open schema, multiple siblings, and existence of an output schema, the description is severely incomplete. It fails to explain search capabilities, parameter expectations, return values, or use cases, making it insufficient for effective tool selection.

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 no defined properties, making schema description coverage 100% by default. However, the examples imply parameters like q, city, per_page, geoip, and the description does not clarify their meaning or usage, leaving the agent to infer from examples.

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

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

The description 'Event search.' conveys a general search action on events, but it does not differentiate from the sibling tool 'event' (singular). It is not a tautology but lacks specificity about the scope or nature of the search.

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

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

No guidance is provided on when to use this tool vs alternatives like 'event', nor any context on appropriate query parameters or limitations. The description offers no usage direction.

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' which is consistent but does not disclose additional behavioral traits such as what happens if the key does not exist or whether the deletion is permanent beyond what annotations imply.

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

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

The description is two sentences long: first sentence states the purpose, second gives usage context and tool relationships. Every sentence is meaningful, no filler.

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

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

For a simple tool with one parameter and no output schema, the description covers purpose, usage, and related tools. It does not explain behavior for missing keys or error handling, which is a minor gap.

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 'key' parameter. The description does not add extra meaning beyond 'by key', so it provides no additional value over 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 'Delete a previously stored memory by key', using a specific verb (delete) and resource (memory). It distinguishes itself from sibling tools like 'remember' (store) and 'recall' (retrieve) by being explicitly a deletion operation.

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

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

The description gives explicit when-to-use scenarios: 'when context is stale, the task is done, or you want to clear sensitive data'. It also mentions pairing with 'remember' and 'recall', indicating related tools. However, it lacks explicit when-not-to-use guidance.

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

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

Annotations (readOnlyHint, idempotentHint) align with description; description adds context on output format ('single text blob ready to drop at site-root/llms.txt') and extraction process, enhancing transparency beyond annotations.

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

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

Description is concise (3 sentences) and well-structured, front-loading the main action and output, then listing use cases.

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

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

Given no output schema, description adequately specifies return value format; tool is simple and complexity is low, so completeness is sufficient.

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

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

Schema coverage is 100% and fully describes both parameters; description restates but does not add significant new meaning beyond schema fields.

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

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

Description states specific action 'Generate a production-ready llms.txt file' for any URL, clearly distinguishing from sibling tools like ai_visibility_check or scan_competitor_ai_presence.

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

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

Provides clear use cases: getting a client site indexed, drafting for own project, auditing competitor AI visibility. Lacks explicit when-not-to-use or alternatives, but context is sufficient.

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

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds no extra context about what the tool returns, potential errors, or performance implications.

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?

Although very short ('Single performer.'), the description is under-specified. Conciseness should not sacrifice clarity; this lacks essential information about the tool's function and parameters.

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

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

Given the tool has one required parameter and an output schema, the description fails to convey the input-output relationship or any meaningful context. It leaves the agent guessing about how to invoke the tool correctly.

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

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

The description does not explain the required 'id' parameter at all. With 0% schema description coverage, the agent has no clue what value to provide or how to format it.

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 'Single performer.' is vague and lacks a verb (e.g., get, fetch, retrieve). It does not specify that the tool retrieves a performer by ID or how it differs from sibling tools like 'performers' or 'performer_by_slug'.

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

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

No guidance is provided on when to use this tool versus alternatives such as 'performers' (for listing) or 'performer_by_slug'. There is no mention of prerequisites or typical use cases.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. However, the description fails to add any behavioral context such as that it retrieves performer details or any limitations.

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 short, the description is a tautology and provides no useful information. Every word doesn't earn its place.

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

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

Given an output schema exists, the description should indicate what is returned (e.g., performer details). It misses the opportunity to provide complete context.

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

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

Schema description coverage is 0%. The description provides no explanation of the 'slug' parameter beyond its name, and no format guidance.

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

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

The description 'Performer by slug.' is a tautology, restating the tool name without adding specific verb or resource. It does not distinguish from sibling tools like 'performer' or 'performers'.

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

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

No guidance on when to use this tool versus alternatives. No context about prerequisites or appropriate scenarios.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds no behavioral context beyond what annotations provide, such as pagination, authentication, or rate limits.

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

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

The description is extremely concise at two words, which sacrifices clarity. It does not provide enough context to be useful, though it is not verbose.

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

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

Given the complexity of a search tool and the presence of sibling tools for performers, the description is incomplete. It lacks details on search behavior, filtering, and differentiation from alternatives.

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% because no parameters are formally defined, only an example with 'q' and 'per_page'. The description does not explain any parameters, but the baseline is 3 due to 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?

The description 'Performers search.' identifies a search action on a 'performers' resource. However, it is vague because it does not specify the search mechanism or scope, and it does not distinguish from sibling tools like 'performer' or 'performer_by_slug'.

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

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

No guidance is provided on when to use this tool versus alternatives such as 'performer' for singular lookup or 'performer_by_slug'. The description does not mention context or prerequisites.

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=false, destructiveHint=false), the description adds that the tool is rate-limited to 5 per identifier per day, is free, and does not count against tool-call quota. 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 concise (a few sentences) with a clear structure: purpose first, then usage guidance, then tips on content, and finally constraints. Every sentence adds value without redundancy.

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

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

Given no output schema, the description need not explain return values. It covers all necessary aspects: purpose, when to use, how to write feedback, rate limits, and impact. The mention of roadmap influence adds valuable context.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal extra meaning beyond clarifying enum values for 'type' and the optional 'context' object, which are already well-described 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 states the tool's purpose: to provide feedback about bugs, feature requests, data gaps, or praise. It distinguishes itself from sibling tools like ask_pipeworx (questions) and discover_tools (exploration) by specifying its feedback role.

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

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

The description provides explicit when-to-use scenarios (bug, feature/data_gap, praise) and what to avoid (pasting user prompts). It also mentions rate limits and that the tool is free and doesn't count against quota, giving clear usage boundaries.

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, open-world. Description adds caching behavior (5min-1h), data source (CF analytics-engine), and no PII. 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?

Front-loaded with core output, then use cases, then technical details. Slightly verbose but well-structured. Could be more concise.

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

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

Given simple one-param tool, description covers purpose, usage, data source, caching, privacy. No output schema needed. 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 covers parameter with enum and description. Description adds context on window behavior (short vs long windows). Baseline 3 enriched to 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?

Clearly states it returns top tools, top packs, and total call volume over a window, with three explicit use cases. Distinguishes from siblings by focusing on aggregated trending data.

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

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

Provides three clear scenarios when to use, but does not explicitly mention when not to use or name alternative tools. Sibling tool list implies alternatives.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds valuable context about searching Polymarket, checking monotonicity, and returning ranked opportunities with trade direction, without contradicting annotations.

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

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

Extremely concise yet comprehensive. Two clearly separated modes with no wasted words. Uses efficient structure with parentheses for mode details. Every sentence adds value.

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

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

Given no output schema, description adequately states return format: 'Returns ranked opportunities with suggested trade direction + reasoning.' Two optional parameters are fully explained. Annotations provide safety guarantees. Complete for a search/analysis tool.

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

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

Schema coverage is 100%, so baseline is 3. Description adds significant meaning beyond schema: explains how each mode works, especially for 'topic' parameter (cross-event search and grouping). Provides concrete examples for both parameters.

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

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

Description states 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations' and clearly distinguishes two modes (event and topic), differentiating from siblings by explaining cross-event mode catches missed cases.

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

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

Explicitly explains when to use event mode vs topic mode with examples (e.g., 'pass a single Polymarket event slug' vs 'pass a topic / seed question'). Provides clear context but doesn't mention when not to use the tool.

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

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

Annotations already indicate read-only and idempotent behavior. The description adds valuable procedural details: fetching price history once, computing model probability, ranking by edge, and netting slippage. 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 concise and front-loaded with the main purpose, followed by key details. Every sentence adds value, with no redundancy.

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

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

Despite no output schema, the description explains return values (top N with edge magnitude and suggested direction) and covers the algorithm, model, and parameter interactions sufficiently for an agent to use the tool correctly.

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

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

Schema coverage is 100% with good parameter descriptions. The description enhances understanding by explaining operational context, e.g., edge is net of slippage and Kelly fractions are half-Kelly. 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 clearly states it scans high-volume Polymarket markets to find those where Pipeworx data disagrees most with market price, specifying the model and process. It distinguishes itself from siblings like 'polymarket_arbitrage' by focusing on edge detection for betting opportunities.

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

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

The description implies usage for the 'what should I bet on today' question, but does not explicitly state when not to use or provide alternatives. The guidance is implied rather than explicit.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by explaining the auto-fetch behavior, the two modes, and the return structure (leg-by-leg prices, spread in percentage points). 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 information-dense but well-structured: first sentence states core purpose, then explains two modes and return values. Slightly long but all sentences earn their place.

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

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

Given no output schema and 3 optional parameters, the description covers modes, parameter usage, and return format. Some edge cases (e.g., topic not found) are not addressed, but overall it provides sufficient context for an AI agent.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the 'topic' parameter as pre-mapped macro shortcuts and describing the override behavior for explicit parameters. This enhances understanding beyond the schema.

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

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

The description clearly states it computes cross-venue spreads for the same resolving question, with two distinct modes (topic and explicit). It differentiates from sibling tools like polymarket_arbitrage by focusing on cross-venue comparison.

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

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

The description explains when to use each mode (topic for pre-mapped shortcuts, explicit for custom pairings) and provides examples. However, it does not explicitly state when not to use this tool or mention alternatives like single-venue 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?

Adds context beyond annotations: explains scoping and relationships with sibling tools. 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?

Three sentences, front-loaded with action, no unnecessary words.

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

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

For a simple retrieval tool with full schema and annotations, description covers all needed context.

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

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

Schema covers the parameter fully; description adds the behavior of omitting key (list all), which is not in schema.

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

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

Description clearly states it retrieves saved values or lists all keys, distinguishing it from sibling tools remember and forget.

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

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

Explicitly states when to use (look up stored context), mentions pairing with remember/forget, and notes scoping per identifier.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavior details like parallel fan-out to multiple sources, input format specifics, and output structure including citation URIs.

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 front-loaded with the core purpose. It is reasonably concise given the information conveyed, though it could be slightly more structured for readability.

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

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

The description covers input, sources, and output structure but lacks information on limits (e.g., max number of changes returned) or pagination. Given the complexity and absence of an output schema, additional context would improve completeness.

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

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

Schema coverage is 100%. The description adds meaningful details: explains 'since' with examples (ISO date or relative shorthand), clarifies 'value' as ticker or CIK, and suggests default monitoring windows. This goes 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 'What's new with a company in the last N days/months?' and provides specific use cases and examples. It mentions data sources (SEC EDGAR, GDELT, USPTO) but does not explicitly differentiate from sibling tools like entity_profile or events.

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 example queries and context for when to use the tool. However, it does not mention when not to use it or provide alternative tools.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety and idempotency. The description adds no new behavioral context (e.g., whether recommendations are personalized or real-time). It fails to leverage annotations to deepen understanding.

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

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

The description is extremely short ('Recommended events.'), which is concise but lacks a structured sentence. It front-loads the core concept but omits important context, making it barely adequate.

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

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

Given the presence of an output schema and annotations, the description should provide context about the recommendation logic, scope, or personalization. It does not, leaving the tool's behavior underspecified 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?

The input schema has no defined properties but allows additional properties. Schema description coverage is 100% (no properties to document). The description provides no parameter info, but baseline is acceptable since schema is minimal. Consideration of additionalProperties behavior is missing but not required for score.

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

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

Description states 'Recommended events,' indicating the tool deals with event recommendations. However, it lacks a verb specifying the action (e.g., list, get) and is vague about the nature of recommendations (personalized, popular, etc.). While it adds resource context beyond the name, it does not fully clarify purpose.

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

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

No guidance is provided on when to use this tool versus alternatives. Sibling tools like 'event' and 'events' suggest related functionality, but the description offers no differentiation or usage 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?

While annotations already declare idempotentHint=true and describe mutation semantics, the description adds valuable context: scoping by identifier, persistence differences between authenticated and anonymous users (24 hours vs persistent). 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 a single, well-structured paragraph of five sentences. Every sentence adds essential information: purpose, usage, storage mechanism, persistence, and companion tools. No unnecessary words.

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

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

For a write-only tool with no output schema, the description covers all key behavioral aspects: what it stores, scope, persistence, and integration with recall/forget. Could mention error handling or size limits, but not essential given simplicity.

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

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

Schema describes key and value with straightforward descriptions. The description adds example key patterns (e.g., 'subject_property', 'target_ticker') which provide useful context beyond the schema. With 100% schema coverage, baseline is 3, and the examples add marginal value.

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

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

The description clearly states 'Save data the agent will need to reuse later' and specifies it's for memoization across conversations and sessions. It explicitly names sibling tools (recall, forget) for retrieval and deletion, providing clear 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?

The description explains when to use the tool ('when you discover something worth carrying forward') and directs to pair with recall and forget. It lacks explicit 'when not to use' guidance, 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=true, idempotentHint=true, and destructiveHint=false. The description adds value beyond annotations by specifying that the tool returns IDs plus pipeworx:// citation URIs. 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 a single paragraph of moderate length. It is well-structured with a clear opening, examples, and usage guidance. Every sentence adds value, though it 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 simplicity (2 params, no output schema), the description is complete. It covers purpose, when to use, parameter semantics, and return value (IDs + citation URIs). No significant 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%, so baseline is 3. The description adds meaning by explaining acceptable values for each parameter (e.g., for company: ticker, CIK, or name; for drug: brand or generic name) and provides examples. This goes beyond the schema's enum and type 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: 'Look up the canonical/official identifier for a company or drug.' It specifies verbs (look up), resource (identifiers), and provides concrete examples (Apple, Ozempic) and ID types (CIK, ticker, RxCUI, LEI). It distinguishes from sibling tools by noting it replaces 2–3 lookup calls.

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

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

The description gives explicit guidance: 'Use this BEFORE calling other tools that need official identifiers.' It also provides use cases (when a user mentions a name needing various IDs). While it does not explicitly state when NOT to use it, 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 (readOnlyHint, idempotentHint) already indicate safety. Description adds that it probes each entity with ai_visibility_check, returns ranked list with score/confidence/signal density, and treats first entity as subject. 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 sentences plus a short phrase. Purpose is front-loaded: 'Compare AI visibility across multiple entities side-by-side.' Every sentence adds unique value with no redundancy.

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

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

No output schema, but description specifies return fields: rank, score, confidence, signal density per entity. With 4 parameters and simple structure, this is sufficient. Could mention that it calls a sibling tool but not required.

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

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

Schema coverage is 100%, but description adds value: 'First entry treated as the subject for narrative' and clarifies that context is shared across probes. This goes beyond the schema's parameter descriptions.

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

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

Description clearly states 'Compare AI visibility across multiple entities side-by-side' with specific verb and resource. Distinguishes from sibling ai_visibility_check by indicating multi-entity comparison and ranking.

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 use case: 'competitive AI-marketing audits: does Claude know about us as well as our competitors?' and describes the process (probes each entity, ranks by score). No explicit when-not-to-use, but context is clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds no behavioral information beyond 'list'. While annotations cover safety, the description does not contribute additional transparency like pagination or output structure.

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 words, front-loaded with the action, and contains no unnecessary information. Every word earns its place.

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

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

Given the tool has no parameters and an output schema exists, the description is minimally adequate. However, it could be expanded to mention what taxonomies are being listed (e.g., 'all available taxonomies') to be fully 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?

There are zero parameters, so the description does not need to add parameter semantics. Baseline 4 applies as no detail is expected.

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 'List taxonomies' clearly states the action and resource. It is a specific verb+resource pair, though it could be slightly more descriptive to differentiate from any similar tools. No siblings are directly similar, so lack of differentiation is acceptable.

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

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

No guidance is provided on when to use this tool versus alternatives. The description merely states the action without any context about prerequisites, scope, or exclusions.

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

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

Beyond annotations (readOnly, idempotent, openWorld), description details output: verdict types (confirmed, refuted, etc.), extracted structure, actual value with citation, percent delta. Also explains replacement of sequential calls, adding behavioral efficiency context.

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

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

Compact two-paragraph structure: purpose first, then usage, limitations, output, benefits. Every sentence adds unique information, no redundancy.

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

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

Given one-parameter simplicity and no output schema, description fully explains return values (verdict, etc.) and data source (SEC EDGAR+XBRL). Clearly states scope limitations (v1, US public companies). Also covers efficiency benefit.

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 description of 'claim' parameter. Description adds domain context (company-financial) and example formats, going beyond schema's generic description. Baseline 3, plus extra value for domain specification.

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?

Describes tool's verb (fact-check/verify) and resource (natural-language factual claim against authoritative sources), specifies supported domain (company-financial claims) and data source (SEC EDGAR+XBRL). Clearly distinguishes from siblings by stating it replaces 4-6 sequential calls, implying unique functionality.

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

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

Explicitly says when to use: when checking truth of a claim, with examples. Mentions it replaces sequential calls. Lacks explicit when-not-to-use or alternatives for non-financial claims, but domain scope 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 indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds nothing beyond these structured signals, failing to disclose any additional behavioral traits.

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 extremely short, the description is not concise in a helpful way; it omits critical information and does not earn its place. It is under-specified rather than efficiently informative.

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 a simple tool with one parameter and existing annotations, the description fails to complete the picture. It does not explain what a venue is, how results are structured, or how to use the tool effectively.

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

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

Schema description coverage is 0%, yet the description does not mention the single parameter 'id' or its semantics. The description adds no meaning beyond the input schema, which is insufficient.

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 'Single venue.' is a noun phrase, not a verb+resource statement. It vaguely indicates the tool deals with a single venue but doesn't specify the action (e.g., retrieve, create). It barely distinguishes from sibling 'venues' (plural).

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

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

No guidance is provided on when to use this tool versus alternatives like 'venues'. The description lacks context on prerequisites, when not to use it, or which sibling tool to choose instead.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false, indicating a safe read operation. The description adds no extra behavioral context, such as pagination behavior, result limits, or that it accepts arbitrary parameters (inferred from additionalProperties). The burden is fully on annotations, so description adds minimal value.

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?

At two words, it is extremely concise. However, it lacks meaningful structure and omits critical details like scope, parameters, or result format. Conciseness alone does not warrant a higher score when information is insufficient.

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

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

Given the presence of an output schema and comprehensive annotations, the description is minimally complete. However, for a search tool, additional context about searchable fields, filtering, or result structure would improve completeness. The description fulfills the basic function but leaves gaps.

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

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

The input schema has no defined properties but allows additional properties (additionalProperties: true) and includes an example with 'city' and 'per_page'. Schema description coverage is 100% due to empty properties, so baseline is 3. The description does not explain the example or hint at accepted parameters, missing an opportunity to add semantic value.

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

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

The description 'Venue search.' clearly identifies the tool's purpose: to search for venues. However, it does not differentiate from the sibling tool 'venue' (singular), which likely provides specific venue details. The verb 'search' is appropriate, but without scope details, it leaves ambiguity.

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

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

No usage guidance is provided. The description does not indicate when to use this tool vs alternatives like 'venue' or other search tools. There are no clues about typical use cases or prerequisites.

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