Deezer

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

Annotations already declare readOnly, idempotent, and not destructive. The description adds valuable behavioral details: Workers AI is free, Anthropic requires a BYO key, and the tool returns per-model visibility scores. It does not contradict any 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 at 4-5 sentences, front-loads the primary purpose, and efficiently covers parameters and output. Every sentence contributes necessary information 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 having no output schema, the description explicitly states the return format (per-model score, confidence, signals, raw_response + combined view). It covers all four parameters, mentions the default model, and provides use-case context. The tool's complexity is well-addressed.

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

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

With 100% schema description coverage, the schema already describes each parameter. The description adds meaning by explaining the default model for 'models', the need for '_apiKey' only with 'anthropic', and how 'context' helps disambiguate. It provides usage 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: probing LLMs for brand visibility and scoring 0-100 per model. It specifies the verb 'probe', the resource 'LLMs', and the output format, which distinguishes it from sibling tools like 'scan_competitor_ai_presence' or '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?

The description includes explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the optional API key. It could be stronger by mentioning when not to use this tool or by naming specific alternatives, but it provides clear context for appropriate 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?

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that it retrieves data by 'Deezer album id' and returns 'metadata+tracklist', which is meaningful behavioral context beyond annotations. 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?

One short sentence that front-loads the core purpose. No wasted words; every part 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 a single parameter, rich annotations, and an output schema, the description is sufficiently complete for a simple retrieval tool. It could optionally mention that the output follows a standard format, but the output schema covers that.

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 0% with no schema description. The description compensates by specifying that the 'id' parameter is a 'Deezer album id', adding semantic meaning beyond the raw number type. However, it doesn't describe format or constraints.

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

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

The description clearly states it returns 'Album metadata + tracklist' using a 'Deezer album id'. While it lacks an explicit verb like 'get' or 'fetch', the resource and action are unambiguous. It distinguishes from sibling tools like 'artist' and 'track' by specifying the scope (album+tracklist).

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. There is no mention of prerequisites, context, or when not to use it. The description purely states functionality, leaving the agent to infer 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description adds little beyond specifying the ID source. 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?

One short sentence with no fluff, but could benefit from a bit more detail. Efficient and clear.

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

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

With an output schema and rich annotations, the description is adequate for this simple lookup tool. No major 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 has 0% description coverage, but the description clarifies the 'id' parameter represents a Deezer artist ID, adding meaning beyond the schema.

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

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

The description clearly states the tool retrieves artist metadata using a Deezer artist ID, with a specific verb and resource, distinguishing it from sibling tool 'artist_top'.

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 prerequisites or usage context provided. The description is minimal.

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 no behavioral context beyond the name, so it does not improve transparency.

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

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

The description is a single, short sentence with no wasted words. However, it is under-specified and could benefit from more details without losing conciseness.

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

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

Given the tool has only 2 parameters, an output schema, and comprehensive annotations, the description is adequate but minimal. It does not explain what 'top tracks' means (e.g., based on popularity or time range), leaving some ambiguity.

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 50% (only 'limit' has a description). The description does not explain the 'id' parameter (artist ID) or add meaning to 'limit' beyond the schema default. It fails to compensate for the missing parameter documentation.

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

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

The description 'Top tracks for an artist' clearly states the action (retrieve top tracks), the resource (artist's top tracks), and is distinct from sibling tools like 'artist' (artist info) or 'album' (album info).

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 does not specify when to use this tool over alternatives such as 'search' or 'chart'. The purpose is implied but no exclusions or context are provided.

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 context about returning structured answers with pipeworx:// citation URIs, 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?

The description is well-structured with clear sections, starting with a strong directive. While slightly lengthy, every sentence adds value, and the information is front-loaded.

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

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

Given the complexity (4,552 tools, many sources), the description is complete. It covers purpose, alternatives, examples, and output format (structured answers with citations). No output schema needed as description suffices.

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 aliases described. The description adds examples and natural language usage but does not provide additional parameter semantics beyond the schema. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: routing questions to authoritative structured data sources with citations. It uses specific verbs like 'routes' and 'returns', and distinguishes itself from siblings like web search, ask_pipeworx_grounded, and 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?

Explicitly recommends use over web search, provides example queries, and outlines when to use alternatives ('step up only when needed'). Clearly states it's the default entry point.

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 indicate readOnly, openWorld, idempotent, and non-destructive hints, the description adds critical behavioral details: the extraction process, return format (answer, evidence, confidence, source, refusal_reason), and specific refusal reasons. 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 verbose but well-structured with key information front-loaded. Every sentence adds value, though some consolidation of related details could improve conciseness without losing 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 (routing among 4774 tools, refusal reasons, cost tradeoff), the description covers purpose, usage, behavior, output format, and cost. No output schema exists, but the description explicitly documents the return structure.

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 being aliases for 'question'. The description reinforces that it accepts natural language and lists all aliases, adding clarity beyond the schema's property 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 is a 'hallucination-resistant answer mode for high-stakes reads' and explains the routing, extraction, and refusal mechanics. It distinguishes from the sibling 'ask_pipeworx' by noting extra cost and appropriate use cases.

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

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

Explicit guidance: use when 'answer will be quoted, cited, or acted on' and the agent must not invent facts. Also advises 'prefer ask_pipeworx for casual lookups.' Examples of high-stakes domains are provided.

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

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

The description goes far beyond the annotations (readOnlyHint, idempotentHint) by detailing market resolution, fan-out logic, response shapes, resolver contract, parent event extraction, news fallback fields, safety handling, wide-spread indicators, and resolution-rule risk. This level of transparency is exemplary and adds significant value for an agent.

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 quite lengthy. It uses clear section headers and line breaks to organize information, which aids readability. However, it could be more concise; some details (e.g., exhaustive fan-out examples) might be trimmed without losing clarity, making it better suited for an agent.

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 (multiple data sources, fan-out, safety checks, resolution rules) and the absence of an output schema, the description is exceptionally complete. It explains edge cases (low-confidence matches, closed markets, wide spreads, cancellation rules) and ensures an agent can handle all scenarios correctly.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description enhances these by providing examples for market (slug, URL, question), clarifying depth options (quick vs thorough with default), and explaining include_raw behavior (summarized vs full payloads). This adds meaningful 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 researches Polymarket bets via Pipeworx, specifying input formats (slug, URL, question) and the output structure (evidence packet, comparison). It is specific and includes usage examples. However, it does not explicitly contrast with sibling tools such as polymarket_arbitrage or polymarket_edges, which could help an agent differentiate.

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 ('should I bet on X', 'what does the data say', 'is there edge in Z') and provides detailed context on when the tool will short-circuit (low confidence, closed markets, wide spreads). However, it does not explicitly mention when not to use the tool or point to alternatives, missing some exclusion guidance.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description only adds that the chart is 'top tracks/albums/artists', which is useful but not critical. 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?

One concise sentence that efficiently conveys the tool's output. However, it could include additional structure like listing the chart types.

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 rich annotations, the description is sufficient for basic use but could explain what 'current chart' means (e.g., daily, weekly).

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 merely emphasizes the default (worldwide) beyond the schema's 'all genres'. Adds 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?

Clearly states it returns current chart of top tracks/albums/artists. While it doesn't explicitly distinguish from sibling tools like 'artist_top', the purpose is specific and 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?

Provides a usage tip (pass genre_id 0 for worldwide) but lacks guidance on when to use this tool versus alternatives like 'artist_top' or 'track'.

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 rich behavioral details: it pulls specific financial data (revenue, net income, cash, debt) from SEC EDGAR/XBRL for companies, handles off-calendar fiscal years (e.g., AAPL Sep, NVDA Jan), and for drugs pulls FAERS adverse-event counts, FDA approvals, and active trials. It also mentions sorting by primary metric and returning 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 but packs essential information efficiently. It front-loads user intent phrases and provides a clear recommendation to prefer this tool over sequential lookups. While it is detailed, every sentence adds value, and it avoids redundancy. Could be slightly more structured (e.g., bullet points for entity types), but overall concise for the complexity.

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

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

Given no output schema, the description appropriately summarizes return values: 'Returns paired data + pipeworx:// citation URIs per entity' and mentions sorting by primary metric. It covers the tool's scope (2-5 entities), data sources, and special handling (off-calendar fiscal years). The only gap is no explicit mention of output structure or error cases, but for a comparison tool with annotations, this is reasonably 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 descriptions for both parameters, so baseline is 3. The description adds significant value beyond the schema: for the 'type' parameter it explains what data each type returns (company: financials; drug: FAERS/trials) and for 'values' it clarifies that companies use tickers/CIKs and drugs use names, with examples. This extra 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: side-by-side comparison of 2-5 companies or drugs. It provides concrete example phrases users might say (e.g., 'Compare X and Y', 'rank these companies') and explicitly differentiates from sequential single-pack lookups, making its purpose unambiguous and distinct from siblings like entity_profile.

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

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

The description gives explicit when-to-use guidance: ALWAYS PREFER over sequential lookups when comparing entities. It specifies the two entity types (company, drug) and provides examples of input formats (ticker/CIK for companies, names for drugs). This helps the agent know exactly when to invoke this tool over alternatives.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: account requirement, free vs paid plan, parallel execution across 4,774 tools, return format (evidence, confidence, source, fetched_at, pipeworx:// citation, gaps array), expected duration (15-60s), and when it returns empty gaps (unstructured topics). This goes well 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 relatively long but well-structured: it front-loads the critical account requirement, then explains the tool's mechanism, usage guidelines, and limitations. Every sentence provides necessary information. While it could be slightly tighter, the complexity of the tool warrants this length. It is effectively organized with clear separation of concerns.

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

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

Given no output schema, the description fully explains return values (findings packet with specific fields). It covers prerequisites (account), constraints (paid plan for thorough), timing, failure modes (gaps for current news), and the decomposition approach. For a research tool of this complexity, the description is remarkably complete and leaves few unanswered questions for an agent.

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

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

Schema coverage is 100% with both parameters described in the schema. The description adds value by explaining that 'question' accepts broad/multi-part queries ('decomposition is the point') and that 'depth' maps to specific facet counts (quick=3, standard=5, thorough=8). This provides context beyond the enum values and type description, justifying a score 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?

The description clearly states the tool performs 'grounded multi-source research' across Pipeworx's structured data sources, and distinguishes itself from siblings like `ask_pipeworx` (single lookup) and `ask_pipeworx_grounded`. It specifies the resource (1242 structured data sources) and the action (decomposes questions into facets, routes to tools in parallel, returns findings packet). This is a specific verb+resource combination that differentiates from the listed 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?

The description provides explicit guidance on when to use this tool vs alternatives: 'For a single lookup use ask_pipeworx', 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx', and 'If you are not signed in, use ask_pipeworx instead'. It also mentions that depth:'thorough' needs a paid plan, setting clear expectations. This is an excellent example of usage 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 that the tool returns top-N relevant tools with names, descriptions, and full input schemas with examples, and that results are ready to call directly. This adds behavioral context beyond the readOnlyHint and destructiveHint annotations, which are consistent.

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

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

The description is well-structured, starting with purpose, listing examples, explaining output, and giving usage guidance. It is informative but not overly verbose, earning 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?

The description covers the main parameter, output format, and usage context. It does not have an output schema but sufficiently describes the return content. Minor gaps (e.g., limit default) are present in the 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%, and the description explains the query parameter as a natural language description and lists its aliases. This adds clarity beyond the schema's 'string' type and example.

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

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

The description clearly states the tool finds tools by describing data or task, listing many domains. It explicitly distinguishes from siblings like 'search' and 'deep_research' by positioning itself as the tool to discover available tools first.

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

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

The description explicitly says 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear when-to-use guidance, though it does not explicitly exclude 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?

Beyond the annotations (readOnlyHint, etc.), the description details the tool's behavior: it fans out across multiple sources, includes fallbacks (news via GDELT→GNews), mentions USPTO API sunset and soft-fail, and specifies that names are not supported. 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 example queries and covers all necessary details, but it is somewhat verbose with a dense paragraph of return fields. Most sentences earn their place, but minor redundancy exists (e.g., name restriction repeated in schema). Still generally 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?

The description provides a fairly complete overview of what the tool returns (cik, filings, fundamentals, patents with notes, news, LEI) and notes limitations. However, it does not specify if all fields are always present or error handling, and there is no output schema to supplement. Adequate given the complexity and annotations.

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

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

The input schema already provides detailed descriptions for both parameters (type and value), including examples and clarifications. The main description reinforces these but does not add significant new semantic information beyond what the schema already conveys, 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 it provides a full cross-source profile of a US public company, using phrases like 'Tell me about X' and 'brief me on Tesla'. It distinguishes itself from siblings by explicitly preferencing over chaining individual lookups and directing to resolve_entity for names.

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

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

The description explicitly states when to use this tool ('ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view') and when not to use it (for names, use resolve_entity first). It provides clear alternatives.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true. Description adds 'delete' which aligns, but does not provide additional behavioral context beyond what annotations cover. The sensitive data hint adds some 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?

Two sentences with no wasted words. Front-loaded with purpose, then usage guidance. 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?

For a simple delete operation with one required parameter and no output schema, the description is sufficient. Could mention return value or error handling, but not essential for a straightforward tool.

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

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

Schema coverage is 100% with description 'Memory key to delete'. Description adds no extra parameter information 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 'Delete a previously stored memory by key' which is a specific verb-resource pair. It distinguishes itself from siblings 'remember' and 'recall' by implication as the deletion counterpart.

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: 'when context is stale, the task is done, or you want to clear sensitive data.' No explicit when-not, but the context is clear. References siblings for pairing.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, confirming safe, idempotent behavior. The description adds detail on the process (fetch, extract, emit) and output format, but could further clarify data handling or caching. Rich context is provided beyond annotations.

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

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

The description is concise with two sentences plus a bulleted list of use cases. It is front-loaded with the primary action, and every sentence contributes meaningful information 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?

Despite no output schema, the description fully explains the output (single text blob for site-root/llms.txt) and the process. Combined with rich annotations and complete schema, the tool is adequately contextualized for its simplicity.

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

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

Schema coverage is 100% with clear parameter descriptions. The description adds minimal extra meaning beyond the schema, referencing the URL implicitly and confirming max_links default/max. Baseline 3 is appropriate as the description does not significantly surpass schema detail.

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

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

The description specifies the verb 'Generate' and the resource 'a production-ready llms.txt file for any URL', clearly distinguishing it from sibling tools by its specific output format and use case for AI crawlers.

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 (client indexing, own project drafting, competitor auditing) and implies appropriate contexts, though it does not explicitly mention when not to use or name 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, destructiveHint, and idempotentHint. The description adds context about returning specific fields, active vs inactive subscriptions, and caller scoping, which adds value beyond the annotations.

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

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

Two sentences: first defines purpose and output, second gives usage guidance. No wasted words or redundancy.

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

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

Given the tool's simplicity (one optional parameter, no output schema needed), the description sufficiently covers purpose, usage, and return fields. It could mention read-only behavior but annotations cover that.

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 one parameter fully described. The description implies the default behavior (active only) and the parameter's effect (include inactive), which adds some value but not substantially 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 'List the caller's active subscriptions' with specific verb and resource, lists return fields, and distinguishes from sibling tools like subscribe and unsubscribe.

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

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

The description provides explicit context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells when to use the tool, though it lacks explicit when-not 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 indicate no destructive/idempotent/read-only behavior. Description adds valuable context: rate-limited to 5 per identifier per day, free, doesn't count against tool-call quota, and that signals affect roadmap. 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?

Five sentences, front-loaded with purpose, followed by usage tips and meta info. No wasted words; every sentence provides essential guidance.

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 feedback tool with three parameters and no output schema, the description fully explains all aspects: types, context usage, message format, rate limits, and quota. 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 covers all three parameters (type, context, message) with descriptions. The description adds semantic value by specifying message content guidelines (be specific, don't paste user prompt) and explaining the enum values in context, going 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?

Description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It enumerates specific categories (bug, feature, data_gap, praise) and is distinct from all sibling tools, which serve different functions.

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 usage guidance: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' Also warns against pasting end-user prompts and notes rate limits and no quota cost.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true. The description adds valuable behavioral details: caching (5min-1h), data source (CF analytics-engine, no PII), and self-aggregating signal. This goes beyond the annotations.

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

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

The description is well-structured with bullet points for use cases and logical flow. It is slightly lengthy but each sentence adds value. Could be more concise, but no wasted words.

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

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

Without an output schema, the description explains the return structure (top tools, top packs, call volume) and how caching varies by window. This is sufficient for an agent to understand what to expect. Could mention count limits, but not necessary.

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 single parameter. The description adds semantic guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This provides meaningful context beyond the schema's enum list.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over recent windows. The verb 'returns' and resource 'trending calls on Pipeworx' are specific, and it distinguishes itself from sibling tools like search or discover_tools by focusing on trending data.

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

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

Three specific use cases are listed: discovering hot data sources, confirming canonical tool, and aligning use case. This provides clear context. However, it does not explicitly state when not to use or mention alternatives, though the use cases implicitly guide 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 readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds rich behavioral context: monotonicity checks, partition-sum, Jaccard similarity filter, partition filter, fill check with realizable edge. No contradiction.

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

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

Description is well-structured with bolded sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) and bullet points. Every sentence adds value; no redundancy. Appropriate length for 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?

No output schema exists, so description fully explains response structure (opportunities[], partition_check, fill_check). Also references sibling tool polymarket_fill_risk for custom sizing. Complete for an AI agent to understand usage and output.

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

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

Schema coverage is 100% and description adds substantial meaning: explains each mode's logic, provides examples ('fed-decision-may-2026'), and details how parameters are used internally. Far exceeds baseline.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket, and distinguishes between three modes: trending scan (no args), event-specific, and cross-event. This differentiates it from sibling tools like polymarket_edges and 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 tells when to use each mode: no args for trending scan, event for specific market, topic for cross-event scanning. Provides concrete examples. Lacks explicit contrast with sibling tools, but internal guidance is strong.

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

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

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false) are consistent. The description adds extensive behavioral context: caching policy ('Cached 1h at the KV level'), model families, edge calculation details, and response structure including diagnostics. 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 long but well-structured: opens with purpose, then details segments, parameters, response structure. Every sentence adds value for the tool's complexity. Could be more concise for a simpler tool, but appropriate here.

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 documents the response structure (by_segment segments, fed_candidates, _diagnostics). All input parameters explained with defaults and usage notes. Complete for a complex analytical 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% (all 9 parameters described in schema). The description adds substantial meaning beyond schema: explains tradeable-edge knobs, default values, and usage context (e.g., 'Bump for very thin partitions; drop to 0 if you have a smarter fill model').

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 verb ('Scan top Polymarket markets'), resource ('opportunities where Pipeworx data disagrees with market price'), and purpose ('what should I bet on today'). It distinguishes itself from siblings like polymarket_arbitrage and polymarket_kalshi_spread by focusing on Pipeworx data disagreement and edge opportunities.

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

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

The description explicitly frames the tool's use case ('what should I bet on today') and details filtering knobs (min_liquidity, max_spread_pp). However, it does not explicitly contrast with alternative tools or state when not to use it.

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

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

Annotations indicate read-only, idempotent, non-destructive behavior. The description adds detail: output fields (tracked, expired, snapshot_dates), historical depth bounded by 60-day TTL, and decay computed from daily closes, not intraday. No contradiction.

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

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

The description is moderately concise, front-loaded with purpose, then output details, then limits. Every sentence adds value, though some elaboration on output fields could be more compact.

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

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

Given no output schema, the description thoroughly explains the return structure (tracked, expired, snapshot_dates) and their fields. It also covers limits (TTL, snapshot start). Missing details like pagination or max results are minor given the tool's complexity.

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

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

Schema coverage is 100% with descriptions for both parameters. The description repeats defaults and clamps but adds context that window refers to 'snapshot family', which is helpful. Overall, the schema already does the heavy lifting.

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: edge persistence and decay telemetry from daily snapshots. It answers the specific question 'how long has this edge existed and is it shrinking?' and contrasts fresh vs old edges, distinguishing it from sibling tools like polymarket_edges (snapshots) and polymarket_arbitrage.

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

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

The description provides when to use: to determine edge age and decay. It implicitly contrasts with using a single snapshot (polymarket_edges) but does not explicitly name alternatives for other edge-related tasks. It also specifies default parameters and limits.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: requires one of market or event, side defaults for basket mode, size interpretation differences, and return fields including verdict and forced directional risk. No contradiction with annotations.

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

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

The description is well-structured with clear separation of modes via capitals and newlines. However, it is relatively long and dense, with some lengthy sentences. It earns its space by covering both modes thoroughly, 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?

No output schema exists, so the description fully carries the burden of explaining return values. It lists all major outputs for both modes (top_of_book, vwap_fill_price, slippage_pp, verdict, theoretical_sum, capture_ratio, thin_legs, forced_directional_risk, etc.), making the tool entirely understandable without additional documentation.

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

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

Schema coverage is 100%, baseline 3. The description adds substantial meaning beyond the schema: explains each parameter's behavior in each mode (e.g., side values for single vs basket, size_usd as spend vs settlement notional), defaults (auto side for basket), and constraints (clamp 10–1,000,000). This justifies a 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 specifies the tool as a 'realizable-vs-theoretical edge check' against live order-book depth, clearly differentiating between single-market and basket modes. It explicitly states the verb ('check') and resource ('fill risk'), and contrasts with sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

The description explicitly states when to use the tool: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains why (theoretical overround not capturable, partial fills create directional risk), providing clear context and exclusion conditions.

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

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

Annotations already indicate read-only and idempotent behavior. The description adds significant behavioral context: response structure includes leg-by-leg prices, matched spreads, and safety fields (compatibility_warning, temporal_alignment, skipped counters). It explains conditions under which spreads are meaningful (bet shape equivalence, temporal alignment) and common pitfalls (most pre-mapped topics are not currently tradeable). This far exceeds minimal 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 lengthy but well-structured: begins with core purpose, then modes, response details, and safety fields. Each sentence adds value, explaining complex concepts (bet shape equivalence, compatibility warnings). However, it could be slightly 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?

No output schema, but the description explicitly lists response fields: leg-by-leg prices, matched spread top spreads, compatibility_warning, temporal_alignment, skipped counters. It covers edge cases (shape mismatches, temporal gaps) and explains counters. This is remarkably complete for a complex cross-venue comparison tool.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds context: explains the two modes (topic vs explicit), provides examples for topic, clarifies override behavior (explicit tickers override topic-mapped sides). While the schema already covers basics, the description enriches understanding of parameter interplay.

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: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It specifies the action (computes spread) and resource (two prediction markets), distinguishing it from siblings like 'polymarket_arbitrage' which likely deals with single-venue 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 provides explicit usage guidance: two modes (topic shortcuts or explicit tickers), and warns that most pre-mapped topics return compatibility warnings and should not be assumed tradeable. It explains when to use (when comparing same question across venues) and when not (when compatibility warning fires due to shape mismatch or temporal misalignment). However, it does not explicitly name alternative tools like 'polymarket_arbitrage' for single-venue comparisons.

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 state readOnlyHint=true and idempotentHint=true, so the description adds beyond that by noting scoping to an identifier and pairing with other tools. No contradictions; lacks mentioning of potential empty state or performance, but still adds 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?

Three concise sentences with no wasted words. Front-loaded with core action, followed by usage and scope. Every sentence serves a 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-only retrieval tool with one optional parameter and no output schema, the description covers purpose, usage, scope, and relationships to siblings. No gaps are apparent.

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 single parameter. The description adds clarity by explaining the effect of omitting the key ('list all saved keys'), which goes beyond the schema's description. This provides actionable guidance for both use cases.

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 ('retrieve', 'list') and identifies the resource ('value previously saved via remember', 'saved keys'). It clearly distinguishes from siblings like 'remember' and 'forget' by stating its role in the retrieval/storage cycle.

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 on when to use ('to look up context the agent stored earlier') and implies alternatives (use 'forget' to delete, 'remember' to save). Mentions scoping by identifier, providing clear context for appropriate 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?

Adds behavioral context beyond annotations: describes return fields (source, citation_uri, raw payload), mark_read effect on subsequent calls, and polling compatibility. 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?

Front-loaded with main purpose, followed by details. Reasonably concise, though could trim slightly. 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?

Explains what is returned (fields in alerts), filtering options, mark_read feature, and alternative access via HTTP. Adequate for a read-only tool with simple parameters. Missing pagination nuance but acceptable.

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?

Adds meaning to parameters with examples (type: 'sec_8k', since: ISO timestamp) and explains mark_read behavior. Schema coverage is 100%, but description enhances understanding.

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

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

The description clearly states the tool pulls fired events from a subscription feed and returns recent alerts, specifying the resource and verb. However, it does not explicitly differentiate from sibling tools like 'list_subscriptions' or '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?

Provides guidance on filtering by type and since, using mark_read, and polling. Mentions an alternative HTTP endpoint. But no explicit when-to-use vs alternatives or exclusion criteria.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds value by explaining the tool fans out to multiple APIs in parallel, handles fallbacks (GDELT to GNews), and soft-fails for USPTO. It also mentions the return structure (changes[] grouped by source) and citation URIs, providing 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 somewhat lengthy but every sentence adds value. It front-loads with example queries and then explains mechanics. It could be slightly more concise, but it is well-structured and 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?

Given the complexity (multiple sources, fallbacks, no output schema), the description is complete. It covers behavior, return structure (changes[] grouped by source, total_changes count, citation URIs), and mentions the alternative tool for static profiles. There are no gaps given the tool's complexity.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds practical context: explains 'since' accepts ISO date or relative shorthand with examples, 'value' can be ticker or CIK, and 'type' is only 'company'. This enhances understanding 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 provides a change feed for a company in a recent time window, fanning out to multiple sources (SEC EDGAR, GDELT→GNews, USPTO). It distinguishes from the sibling 'entity_profile' which provides static profiles, making the purpose unambiguous.

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

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

The description provides explicit query examples ('What's new with X', 'latest on Y') and advises to use 'entity_profile' for static profiles. It explains when to use each source (GDELT preferred, GNews fallback) and gives guidance on the 'since' parameter with examples ('7d', '30d', '3m', '1y'). This covers both when and 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 idempotentHint=true and destructiveHint=false. The description adds critical behavioral context: 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours'. This goes beyond annotations and helps the agent understand retention behavior. 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 very concise and well-structured: opens with the core purpose, then usage context, then storage details, then sibling references. Every sentence adds value with no redundancy or 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 key-value store with 2 required string parameters and no output schema, the description is comprehensive. It covers purpose, usage, persistence differences, and relationship to sibling tools. No gaps.

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

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

Schema description coverage is 100%, so parameters are already well-documented. The description adds value by providing example key formats ('subject_property', 'target_ticker') and clarifying that the value is 'any text', but this is incremental. Baseline of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later'. It provides concrete examples like 'resolved ticker, target address, user preference', which makes the purpose specific and distinct from sibling tools such as '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?

The description explicitly states when to use the tool ('when you discover something worth carrying forward') and when not to use alternatives. It directly mentions sibling tools 'recall' and 'forget', providing a clear 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?

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds behavioral context: 'Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.' This reveals multi-step internal processing without contradicting annotations. No annotation contradictions present.

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 clear structure. Each sentence adds value. Slightly verbose with repeated examples of user queries, but overall efficient for the amount of detail. Could be tightened by removing some redundant example phrasing.

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

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

Despite no output schema, the description fully explains return values for each type, including citation URIs. Given the complexity of multiple entity types and the need to guide agents on what data they will receive, the description is complete. Covers both types and their output fields, enabling correct invocation without external documentation.

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

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

Schema coverage is 100% (both parameters fully described). Description adds significant meaning: explains what each type returns (for company: ticker, CIK, company_name, citation URI; for drug: RxCUI, ingredient, brand, citation), notes auto-disambiguation for company, and provides examples of acceptable values. Goes well beyond the schema's basic descriptions.

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

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

Description starts with concrete examples of user queries, clearly states verb+resource: 'resolve a user-spoken NAME to the canonical/official identifier'. It distinguishes from siblings with 'Use FIRST whenever you have a name but need an ID.' Detailed listing of supported entity types (company, drug) and their outputs further clarifies 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?

Provides explicit usage guidance: 'Use FIRST whenever you have a name but need an ID.' Clearly indicates when to use. Does not explicitly state when not to use or mention alternative tools among siblings, but the instruction implies that if an ID is already known, this tool is unnecessary. Lacks exclusions for ambiguous names or cross-type lookups.

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

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

Describes internal mechanism ('Probes each entity with ai_visibility_check, ranks by score, surfaces which is most/least recognized') and expected output ('Returns ranked list with score, confidence, signal density'). This adds value beyond annotations which are already non-destructive and read-only.

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 concise sentences, front-loaded with core purpose, followed by method and use case. 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 lacking an output schema, the description fully specifies return fields (score, confidence, signal density). Covers what the tool does, how it works, when to use, and what to expect. Complete for a comparison tool with good annotations.

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

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

Schema coverage is 100%, but the description adds critical detail: 'First entry treated as the 'subject' for narrative; rest are competitors.' This clarifies the role of the first entity, which is not in 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 the action ('Compare AI visibility across multiple entities side-by-side') and the resource ('entities'). It distinguishes itself from sibling tool 'ai_visibility_check' by focusing on comparison and ranking, making its purpose unambiguous.

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

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

Provides explicit use case ('competitive AI-marketing audits') and a concrete question ('does Claude know about us as well as our competitors?'). While it implies when to use over single-entity checks, it does not explicitly contrast with siblings.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds behavioral details: partial failures, bundlephobia timeout (5-30s), sources_failed field. No contradiction.

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

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

Description is relatively long but well-structured and front-loaded with purpose and use case. Every sentence adds value, though could be slightly more concise. No wasted words.

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

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

No output schema, but description thoroughly explains return fields (summary block, per-advisory detail, links, alternative versions). Covers edge cases like partial failures and other ecosystems.

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 documented. Description adds extra meaning: scoped packages accepted, version defaults to latest. Goes 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 it's a composite check for adding npm packages, covering license, advisories, bundle size, etc. It distinguishes from siblings by specifying npm ecosystem and mentioning fallback for other ecosystems via deps.dev.

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 agent asks 'is X safe/popular/small' or 'what does adding lodash cost me'. Specifies npm only in v1 and mentions deps.dev for other ecosystems. Could mention when not to use, but provides clear context.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds no behavioral context beyond what annotations provide, so a baseline score of 3 is appropriate.

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

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

Description is a single sentence, very concise. It could be slightly more structured but is clean and front-loaded with the essential 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?

Given that the output schema is present and annotations cover safety, the description is just barely adequate. It lacks details on result format, pagination, or sorting, but is sufficient for a basic search tool.

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

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

Input schema has 100% coverage with descriptions for all parameters. The tool description does not add further parameter meaning beyond what the schema provides, so 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 clearly states it searches Deezer for tracks, albums, artists, and playlists. However, it does not differentiate from sibling tools like 'album' or 'artist' which are more specific, leaving the agent to infer the tool's broader scope.

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

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

No guidance on when to use this tool versus alternatives. Sibling tools include more specific entity lookup tools, but the description does not indicate that 'search' is for broad queries while 'album', 'artist', etc. are for direct lookups.

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

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

The description adds significant behavioral detail beyond annotations: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation and flagging, and return of character offsets. Annotations (readOnlyHint, idempotentHint) are consistent and do not contradict.

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?

Six well-structured sentences with the purpose first, then usage guidance, then technical details and pairing. No fluff; every sentence earns its place. The description is informative yet 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 no output schema, the description describes return values (passages with offsets and scores), truncation behavior, embedding model, and windowing. It also mentions a pairing tool. This is fully sufficient for an agent to invoke correctly.

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

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

Schema has 100% coverage with descriptions for all 3 parameters. The description reinforces the cap (text) and gives concrete query examples, adding value. However, it doesn't explain the return format per parameter (e.g., how 'limit' maps to passages). Still a strong addition.

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 ('semantic search'), the resource ('inside a fetched record'), and distinguishes from siblings by specifying use case (too big to cram into prompt) and pairing with ask_pipeworx_grounded. It does not overlap with other tools like '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?

Explicitly says 'Use when the record is too big to cram into the prompt' and describes how it saves context. It also recommends pairing with ask_pipeworx_grounded for grounded queries, providing clear when-to-use 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?

The description discloses behavioral traits beyond annotations: requires Pipeworx OAuth account, delivery channels (feed always on, optional email/sms/webhook), SMS cap of 10/day, webhook auto-disable after 10 failures, and HMAC signing details. No contradiction with annotations (readOnlyHint false, openWorldHint true, idempotentHint true, destructiveHint false).

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

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

The description is comprehensive but somewhat lengthy. However, it is well-structured with clear sections for types and delivery channels, and all sentences add necessary detail. Could be slightly more concise, but no substantial waste.

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 subscription types, delivery options, authentication requirements, and no output schema), the description is highly complete. It covers the return value (new subscription ID), constraints (phone verification, delivery limits), and error conditions (anonymous not allowed, webhook auto-disable).

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

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

Schema coverage is 100%, but the description adds significant meaning: explains each subscription type's parameters with concrete examples (e.g., sec_8k items codes, polymarket_edge topic and min_spread_bps), delivery channel details (SMS phone verification, webhook secret return, email pattern validation). This goes beyond the schema's property 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 creates a proactive monitoring subscription to a live-data event stream and returns the new subscription ID. It distinguishes itself from sibling tools like 'unsubscribe' and 'list_subscriptions' by focusing on creation.

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

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

The description explicitly states when to use this tool (for persistent subscriptions requiring an OAuth account) and when not (anonymous + BYO cannot persist subscriptions). It also provides detailed supported types with example parameters and delivery channel options.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds that it returns example questions drawn from a live catalog, is the onboarding entry point, and supports topic filtering, providing rich 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 thorough but slightly lengthy, front-loading common user questions and then explaining functionality. Every sentence adds value, though a more concise summary could be achieved without losing key details.

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

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

For a tool with one optional parameter and no output schema, the description covers purpose, usage, and return type (example questions with tool shapes). However, it lacks an explicit example of the output format, which would make it more complete for agents.

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 one optional 'topic' parameter described. The description adds practical usage details: examples like 'finance', 'pharma', 'betting', and the option to omit for full spread, enhancing the schema's meaning.

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

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

The description clearly states the tool's purpose: it returns category-bucketed example questions with exact tool and argument shapes, serving as an onboarding entry point. It distinguishes itself from siblings like ask_pipeworx by being the first tool to use for exploration.

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 use this FIRST when the agent doesn't know what Pipeworx can do, and provides guidance on calling with no arguments for full spread or passing a topic to focus. This clearly differentiates from alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as true/false, which provide safety and behavior cues. The description adds no extra behavioral context beyond stating the operation, so it meets the baseline but does not exceed it.

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

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

The description is a single sentence that directly and efficiently communicates the tool's purpose with no unnecessary words. It is front-loaded and ideal in terms of conciseness.

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

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

Given the tool's simplicity (single parameter, annotations, and an output schema), the description is minimally adequate. It does not explain what metadata is returned, but the presence of an output schema reduces the burden. Still, it could be more complete.

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

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

The schema description coverage is 0%, so the description carries the burden of explaining parameters. It adds meaning by clarifying that the 'id' parameter is a Deezer track id, which is helpful. However, it does not elaborate further on format or constraints, so it partially compensates.

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 tracks metadata by Deezer track id, specifying the verb and resource. However, it does not differentiate itself from sibling tools like 'album' or 'artist' that serve similar purposes for other entities.

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

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

No guidance is provided on when to use this tool versus alternatives, nor are there any exclusions or prerequisites. The description only states the input requirement (Deezer track id) without context on usage 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?

Beyond the annotations (destructiveHint=false, idempotentHint=true), the description adds critical context: ownership enforcement, soft-deletion (deactivate, not delete), and that historical events remain accessible via recent_alerts.

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, front-loaded with the main action, and every sentence adds essential value without any 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?

The description covers the action, behavioral constraints, and side effects for a simple one-parameter tool with no output schema. It is almost complete, but does not specify the return value or confirmation of success.

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

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

The schema coverage is 100% with a clear description for the 'id' parameter. The tool description does not add any additional semantic meaning beyond what the schema already provides, so the score is at 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?

The description clearly states the action 'Cancel a subscription by id' with a specific verb and resource, and distinguishes itself from sibling tools like subscribe (create) and list_subscriptions (list).

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 ownership enforcement ('you can only cancel your own subscriptions') and explains the soft-deletion behavior with reference to recent_alerts, giving context for when to use. However, it lacks explicit alternatives or 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 already declare read-only, open-world, idempotent, non-destructive. Description adds return format (verdict types, structured form, citation, delta) and data source (SEC EDGAR + XBRL), providing useful behavioral context beyond annotations.

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

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

Description is concise yet comprehensive, front-loaded with trigger phrases, and every sentence adds value—covering purpose, usage, input, output, and rationale for efficiency.

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 adequately covers input format, domain, return types, and data sources. Missing edge cases or error handling but sufficient for typical 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?

Single parameter 'claim' has 100% schema coverage. Description adds natural-language examples and clarifies acceptable formats (e.g., 'Apple's FY2024 revenue was $400 billion'), adding meaning beyond schema.

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

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

Description clearly states the tool performs natural-language claim verification against authoritative sources, specifically for company-financial claims. It lists trigger phrases like 'Is it true that…' and distinguishes itself as a composite tool replacing multiple sequential 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?

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and specifies the domain (company-financial claims). Lacks direct exclusion criteria but context implies non-financial claims require different tools.

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