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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds transparency about the default model being free and the Anthropic API key requiring direct payment, which are not covered by annotations. No contradictions.

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

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

The description is three sentences long with no filler. The first sentence states the core action and output; the second clarifies default and optional models; the third lists use cases. Every sentence adds value.

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

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

Given the full schema descriptions, no output schema, and moderate complexity, the description is complete. It covers the purpose, parameters, model behavior, cost implications, and use cases. The return structure is summarized, which is sufficient.

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

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

Schema coverage is 100% with clear descriptions for all four parameters. The description adds practical context: the default model is Workers AI Llama-3.3-70b (free) and the _apiKey enables Anthropic probing. This slightly exceeds the baseline for high schema coverage.

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

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

The description clearly specifies the tool's function: probing LLMs for knowledge about a business/brand/product/topic and returning visibility scores per model. It distinguishes itself from sibling tools by focusing on multi-model visibility scoring rather than single-model Q&A or entity profiles.

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

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

The description provides explicit use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains model selection and API key requirements. However, it does not explicitly state when not to use this tool or list alternatives for other scenarios, though the sibling list suggests alternatives like entity_profile or ask_pipeworx.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description's safety profile is clear. The description adds useful context: it routes to 3,748 tools across 884 sources and returns structured answers with stable citation URIs. This goes beyond annotations without contradicting them.

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 reasonably concise given the breadth of functionality, with key information front-loaded ('PREFER OVER WEB SEARCH'). It includes a list of examples and trigger phrases, but could be slightly trimmed without losing clarity.

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

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

Given the tool's complexity (routing to many sources) and the presence of annotations and sibling tools, the description sufficiently covers purpose, usage, and behavioral context. It mentions the return format (structured answer with citation URIs), which is adequate despite the lack of an output schema.

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

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

Schema description coverage is 100% (all 6 parameters are documented as aliases for the question). The description does not add any additional meaning or details about parameter usage beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description clearly states the tool routes questions to many authoritative sources for structured data, listing specific domains (SEC filings, FDA, etc.) and providing examples. It distinguishes itself from web search by explicitly saying 'prefer over web search' and specifying the types of queries it handles.

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

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

The description gives explicit guidance on when to use this tool over alternatives: 'PREFER OVER WEB SEARCH' for a wide range of factual queries. It lists trigger phrases like 'what is', 'look up', 'find', and provides concrete examples of appropriate questions.

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

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

Discloses extra LLM call, extraction method using only tool result, and return structure including refusal reasons. Consistent with annotations (readOnlyHint, destructiveHint, idempotentHint).

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?

Concise but informative; front-loaded with purpose. Slightly long but every sentence adds value. Could be trimmed slightly but effective.

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

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

Covers behavior, refusal modes, use cases, trade-offs, and return structure. Without output schema, description adequately explains output fields.

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

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

Schema coverage is 100%, so baseline is 3. Description adds minimal extra beyond noting aliases and natural language; no contradiction or enrichment.

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, distinguishing it from ask_pipeworx by noting extra cost and use cases where answers will be quoted or acted upon.

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 (quoted/cited/acted on answers, high-stakes) and when to prefer ask_pipeworx (casual lookups), with examples of domains.

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

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

Annotations already declare readOnlyHint and idempotentHint. The description adds extensive behavioral context: fan-out to category-specific data packs, resolver contract with confidence levels, parent event extraction, news fallback handling, safety short-circuits for low-confidence matches and closed markets, illiquidity warnings, and cancellation rule parsing. 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 long (over 550 words) but well-structured with labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.). While each section earns its place for a complex tool, it could be shorter without losing clarity. Front-loading the core purpose helps.

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 thoroughly documents return shapes (result.market, result.analysis, result.evidence), resolver contract (confidence, score, alternatives), parent event extractor, news fields with fallback, safety mechanisms, and cancellation rules. This fully compensates for the missing output schema.

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

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

Schema coverage is 100%, and the description adds meaning for all three parameters: it provides examples for market (slug, URL, question text), explains depth enum (quick vs thorough with default), and describes include_raw's effect on response size. This goes beyond the schema's basic descriptions.

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

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

The description begins with a clear verb-resource pair: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It then specifies input types (slug, URL, question text) and output structure. The purpose is distinct from siblings like polymarket_edges and polymarket_arbitrage because it focuses on a single bet research call rather than edge detection or arbitrage.

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

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

The description explicitly states when to use the tool: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also provides warnings about inspecting market_match_confidence and avoiding phantom matches. However, it does not explicitly contrast with sibling 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 already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds rich behavioral context: data sources (SEC EDGAR for companies, FAERS for drugs), handling of off-calendar fiscal years, return of citation URIs, and efficiency (replaces 8-15 lookups). 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 appropriately sized given the tool's complexity. It front-loads the core purpose with multiple common query examples, then details behavior. Every sentence adds value, though a minor redundancy exists (e.g., 'side-by-side comparison' and 'compared' are repeated). Still well-structured.

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

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

Despite no output schema, the description fully explains return data: paired data with pipeworx:// citation URIs, sorted by primary metric. It covers both entity types, data sources, fiscal year handling, and edge cases. For a tool of this complexity, the description is remarkably 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. The description adds meaning beyond schema: explains that 'type' enum values pull different data sources and that 'values' for companies are tickers/CIKs while for drugs are names. This adds semantic context that helps the agent choose correct values.

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

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

The description clearly states the tool performs side-by-side comparisons of 2-5 companies or drugs in a single parallel call, with specific verb phrases like 'compare', 'compare X and Y', and 'rank these companies'. It distinguishes from sibling tools like entity_profile (single entity) and sequential lookups.

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

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

Explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use guidance. Also explains how sorting works for queries like 'largest' or 'most', so the agent knows the top result is the answer.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds significant behavioral context: it decomposes questions, routes in parallel, returns verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, and explicit gaps[] for unanswered facets. It also states the tool 'never invented' data, adding honesty 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 long (4 sentences) but each sentence adds valuable information. It is front-loaded with the core purpose and well-structured with usage guidelines, prerequisites, and output description. Could be slightly trimmer but not overly verbose.

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

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

Given the complexity of the tool (multi-step, parallel invocation of many tools), the description fully covers purpose, usage, behavioral traits, parameters, prerequisites, and the return format (structured findings packet with citations and gaps). No output schema exists, but the description compensates by explaining what the agent receives.

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

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

Schema coverage is 100%. The description adds extra nuance: for 'depth', it explains that quick=3 facets, standard=5, thorough=8 and that thorough requires a paid plan. For 'question', it clarifies that broad/multi-part questions are fine and decomposition is the point.

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 'Grounded multi-source research in ONE call' and explains how it decomposes questions, routes to tools in parallel, and extracts grounded answers. It distinguishes itself from sibling 'ask_pipeworx' by noting that tool is for single lookups.

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

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

Explicitly says 'Use for broad or multi-part questions' and provides a clear alternative: 'use ask_pipeworx for single lookups'. It also mentions prerequisites (Pipeworx account, paid plan for 'thorough' depth) and a time expectation of 15-60s.

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 mark the tool as read-only and idempotent. The description adds that it returns 'the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples)' and that results are ready to call directly. This provides valuable 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 well-structured, starting with the core purpose and then adding details. It is informative but slightly lengthy; however, every sentence serves a purpose and maintains clarity.

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

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

Given the tool's complexity (6 parameters with aliases, no output schema), the description covers all essential aspects: purpose, usage timing, return format, and examples. It is complete for an agent to decide when and how to use the tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by providing concrete examples of what to pass as query (e.g., 'analyze housing market trends') and notes that the tool accepts multiple aliases for query. This aids parameter understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists many example domains and distinguishes from siblings by positioning itself as a discovery tool to call first when exploring the available toolset.

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

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

The description explicitly advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear usage context. It does not list explicit exclusions or alternative tools but the guidance is strong enough for selection.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds crucial behavioral context: it fans out across multiple sources in one parallel call, specifies that patents rely on a sunsetting API (USPTO PatentsView) and will soft-fail, and explains the fallback chains (GDELT to GNews for news). There is 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 dense with information, but every sentence serves a purpose. It is well-structured: starting with example queries, then the core function, then details on what is returned and limitations. It is slightly long, but front-loaded with the most critical information (prefer over chaining, one parallel call). Could be slightly more concise, but still highly effective.

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

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

Given the complexity of the tool (multiple sources, no output schema), the description is remarkably complete. It details all return components: CIK, company_name, recent_filings with pipeworx URIs, fundamentals (with specific fields and sorting), patents with API sunset warning, news via GDELT→GNews fallback, and LEI via GLEIF. It also mentions limitations (names not supported, patent soft-fail). The lack of output schema is compensated well.

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 required parameters: type (enum: company) and value (ticker or CIK). The description adds significant semantic value beyond the schema: it explains that only tickers or zero-padded CIKs are accepted, provides examples ('AAPL', '0000320193'), and clarifies that names are not supported (directing to resolve_entity). It also hints at future support for person/place types.

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: providing a 'full cross-source profile of a US public company in ONE parallel call'. It lists specific outputs (CIK, filings, fundamentals, patents, news, LEI) and distinguishes from sibling tools by explicitly stating it should be preferred over chaining single-source lookups for holistic views. The example queries ('Tell me about X', 'research Acme', etc.) make the purpose immediately understandable.

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

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

The description provides explicit when-to-use guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clearly states when not to use: names are not supported, so the agent should use resolve_entity first if only a name is available. Additionally, it warns about the patent source soft-failing until May 2025.

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

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

Annotations declare readOnlyHint, idempotentHint, openWorldHint, destructiveHint. Description adds return columns, ordering by Data descending, and survey scope (150 economists), 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?

Two sentences, front-loaded with purpose and key details, no fluff. Efficiently conveys 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?

For a read-only tool with 3 params, good annotations, and no output schema, description covers return fields, filtering, ordering, and data source, fully meeting needs.

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 params have descriptions). Description adds value by illustrating indicator examples and clarifying top default/max and ordering, enhancing schema.

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

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

Description clearly states it returns median/mean forecasts from Focus market expectations survey, with specific examples of indicators and return fields. Distinguishes from siblings like list_indicators.

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

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

States to filter by indicator and optionally reference year. While explicit when-to-use is provided, no exclusion or alternatives mentioned, but context is clear.

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

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

The description aligns with annotations (destructiveHint=true, idempotentHint=true) and adds context on why deletion is appropriate. While annotations already indicate destructiveness, the description provides situational context.

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

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

Two sentences with no wasted words, front-loading the action and purpose efficiently.

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 tool is simple (one required parameter), and the description covers purpose, usage guidelines, and context. No output schema needed, so completeness is high.

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

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

Schema coverage is 100% with a clear description of the parameter. The description does not add additional semantics beyond what's in the schema, so baseline 3 is appropriate.

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

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

The description clearly states the action (delete), the resource (previously stored memory by key), and distinguishes from sibling tools like 'remember' and 'recall'.

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

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

Provides explicit when-to-use scenarios (stale context, task done, clear sensitive data) and suggests pairing with 'remember' and 'recall', making the usage context very clear.

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

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

The description discloses that it fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. Annotations already indicate readOnly, openWorld, and idempotent, which the description complements. No contradictions; the behavioral details add value beyond annotations.

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

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

Three sentences: purpose, process, and use cases. No wasted words, front-loaded with the key action, and every sentence adds value.

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

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

Given no output schema, the description fully explains the output format (single text blob) and covers input, process, output, and typical use cases. Complete for a simple tool.

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

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

Schema coverage is 100%, so both parameters have descriptions. The tool description adds context about output format and usage, but does not significantly enhance parameter understanding beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb (generate), resource (llms.txt file), and scope (for any URL). It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on the standard llms.txt format.

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

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

The description provides explicit use cases: getting a client's site indexed, drafting llms.txt for own project, or auditing competitor AI visibility. It does not explicitly mention when not to use or alternative tools, but the use cases guide 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety. The description adds return format details, but no additional behavioral traits like error handling or limits beyond schema.

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

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

Two sentences front-load purpose and return format with zero extraneous content. Highly efficient.

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

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

Given no output schema, it describes return format briefly. It also lists valid names, compensating for lack of enum in schema. Sufficient for a simple lookup tool, though error cases are omitted.

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

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

Schema coverage is 100%, so the description's mention of friendly names and return format adds minor reinforcement but not substantial new meaning beyond the schema.

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

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

The description clearly states it's a convenience lookup for Brazilian macro indicators using friendly names, listing specific names and return format. This distinguishes it from sibling tools like sgs_series that likely require SGS codes.

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

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

It implies use when you have a friendly name and want quick access, contrasting with SGS code-based tools. However, it doesn't explicitly state when not to use it or compare directly 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, and destructiveHint false. Description adds value by specifying the output content (names, codes, units), but does not elaborate on other behaviors like pagination or error states. Bar lowered due to strong annotations, so score is adequate.

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

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

Single sentence, efficiently conveys purpose and output. 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?

For a simple list tool with no parameters and strong annotations, the description fully covers what the tool returns. No output schema, so description adequately explains the output (friendly names, SGS codes, units).

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?

No parameters (0 params), schema coverage 100%. According to guidelines, baseline is 4 for zero parameters. Description does not add parameter info but none needed.

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

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

Description clearly states verb 'List', resource 'friendly indicator names available to the indicator tool', and includes what is returned (SGS codes and units). Distinguishes from sibling 'indicator' tool by indicating it provides the list of names that the indicator tool uses.

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

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

Explicitly states this tool provides names available to the 'indicator' tool, implying it should be used before indicator. Does not provide explicit when-not-to-use or alternatives, but context is clear.

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

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds context: returns specific fields, scoped to caller, defaults to active only, plus include_inactive parameter behavior.

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

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

Two sentences, front-loaded, no fluff. Efficiently conveys purpose, return fields, and usage advice.

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 list tool with one optional parameter and good annotations, description covers return fields, default behavior, and use case. No missing information.

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

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

Schema coverage is 100% with description for the sole parameter. Description does not add additional parameter semantics beyond what schema provides.

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

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

Description clearly states 'List the caller's active subscriptions' with specific verb and resource. Returns specific fields. Distinct from sibling tools subscribe and unsubscribe.

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

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

Explicitly states when to use: 'review what you're monitoring before adding more or to find an id to cancel.' Implicitly contrasts with subscribe/unsubscribe 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?

Beyond annotations, the description discloses rate limits (5 per identifier per day), free usage, no quota impact, and that signal affects roadmap. Annotations do not contradict; readOnlyHint=false is consistent with a write operation.

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

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

The description is well-structured and concise, covering purpose, usage, and behavior in a few sentences. Minor redundancy ('Free; doesn't count against your tool-call quota' is fine) but overall efficient.

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

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

Given the tool's simplicity (3 params, nested object, no output schema), the description provides complete context: purpose, when to use, parameter guidance, behavioral constraints, and roadmap impact. No gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the enum values in more detail and suggesting message length (1-2 sentences, 2000 chars max). This helps the agent craft appropriate feedback.

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

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

The description clearly states the tool's purpose: to send feedback (bug, feature, praise) to the Pipeworx team. It distinguishes from siblings like ask_pipeworx by focusing on reporting issues rather than asking questions.

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

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

The description explicitly tells when to use each feedback type (bug, feature, data_gap, praise) and provides a clear exclusion: 'don't paste the end-user's prompt.' This leaves no ambiguity about appropriate use cases.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral details: data derived from CF analytics-engine, no PII, caching behavior (5min-1h by window), and self-aggregating nature, exceeding annotation coverage.

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

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

The description is concise and well-structured, with a clear opening statement followed by bullet-pointed use cases and a closing note on caching and data derivation. No unnecessary words.

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

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

Given the simple schema (one optional parameter with enum) and comprehensive annotations, the description fully covers behavior and return values (top tools, top packs, total call volume). No output schema exists, but the description adequately explains what the tool returns.

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

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

Schema coverage is 100% for the single parameter 'window', so baseline is 3. The description adds meaningful semantics: '24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.' This clarifies the impact of the parameter choice.

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 'returns' and the resource 'top tools, top packs, and total call volume'. It specifies the data source and that it's self-aggregating, distinguishing it from sibling tools by focusing on trending signals.

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

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

The description lists three explicit use cases, providing clear guidance on when to use the tool. It does not explicitly mention when not to use it or alternatives, but the use cases are sufficiently specific.

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, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds extensive details beyond annotations: algorithms, Jaccard similarity for cross-event, partition filter for placeholders, fill check against live CLOB depth, and response structure. No contradictions.

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

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

The description is front-loaded with the requirement and well-structured with sections for each mode, response, and fill check. However, it is quite verbose; some redundancy could be trimmed without losing value.

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

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

With no output schema, the description thoroughly explains the response structure including opportunities, partition_check, and fill_check details. All relevant aspects of the tool's behavior and limitations are covered, making it 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 description coverage is 100%, and the description adds significant meaning: explains modes, gives examples of slugs and seed questions, accepts full URLs for event, and details behavioral context for each parameter.

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 via monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) with specific parameter uses, and the name and title align with the purpose.

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

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

The description explicitly states the requirement for one of 'event' or 'topic', warns that calling with no args fails, recommends event mode for specific markets, and suggests polymarket_fill_risk for custom sizing. This provides clear guidance on when and how 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?

The description goes far beyond annotations (readOnlyHint, idempotentHint, etc.) by detailing caching behavior, diagnostic output, model families, and tradeable-edge filters. It fully discloses behavioral traits without any 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 overly verbose with excessive technical detail (formulas, parameters, examples). It lacks conciseness and may overwhelm the agent with irrelevant specifics, hindering quick understanding.

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 (9 parameters, detailed models, no output schema), the description is extremely complete, detailing response structure, model types, edge calculations, and diagnostics. It compensates fully for missing output schema.

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

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

Schema coverage is 100%, so the schema already documents parameters well. The description adds minor context (e.g., 'tradeable-edge filter') but does not significantly enhance 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's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It distinguishes itself from sibling tools by focusing on edge opportunities and is built for discovering betting opportunities.

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

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

The description implies usage for bet discovery but lacks explicit guidance on when to choose this tool over siblings like polymarket_arbitrage or bet_research. No when-not-to-use or alternative guidance is 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 details behavioral aspects beyond annotations, such as history depth bounded by a 60-day TTL, dependence on snapshotting enablement, and decay computed from daily closes. Annotations indicate read-only, open-world, idempotent, and non-destructive behavior, which the description does 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?

The description is front-loaded with purpose and well-structured: purpose, args, response fields, limits. It is somewhat lengthy but every sentence adds value. It could be slightly more concise, but overall efficient 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?

Despite no output schema, the description thoroughly explains the response structure (tracked, expired, snapshot_dates) and limits. It covers all necessary details for an agent to understand what the tool returns and its constraints.

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

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

Input schema covers both parameters with descriptions, and the description reiterates them with defaults and limits. While it adds some context (e.g., snapshot family for window), it does not significantly expand beyond the schema. Baseline of 3 applies given 100% coverage.

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

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

The description clearly states the tool's purpose: providing edge persistence and decay telemetry based on daily polymarket_edges snapshots. It answers a specific question about edge longevity and distinguishes itself from sibling tools like polymarket_edges, which likely provides current edges.

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

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

The description includes a practical context for when to use this tool (e.g., a fresh vs. three-week-old wide edge), implying its value in assessing edge persistence. It does not explicitly state when not to use it or name alternative tools, but the context is clear.

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

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

Description details internal behavior (walks the ladder, returns top_of_book, vwap_fill_price, slippage_pp, etc.) and warns of forced_directional_risk. Annotations already indicate readOnlyHint, idempotentHint, openWorldHint; description adds significant context 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 lengthy (three paragraphs) with detailed explanations, which is justified by the complexity of two modes, but it could be more concise. Structure is logical with front-loaded 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 no output schema, the description thoroughly lists all return fields for both modes, including edge cases like thin_legs and forced_directional_risk, making it complete for a complex tool with 4 parameters.

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

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

Schema coverage is 100% but the description adds meaning beyond the schema, such as explaining how size_usd is interpreted differently in single-market vs basket mode and the default behavior of side.

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 realizable-vs-theoretical edge check against live CLOB order-book depth, with two explicit modes (single-market and basket). It distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edges by describing its specific function.

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

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

Explicitly advises to use before polymarket_arbitrage signals or polymarket_edges trades above $500. It also explains when not to use: theoretical overround on thin books is not capturable and partial basket fills convert arb into unhedged directional risk.

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, openWorldHint, idempotentHint) by detailing compatibility warnings, temporal alignment, skipped cross-type/subtype counters, and the fact that real spreads are rare. 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: purpose, modes, response format, safety fields. It is appropriately sized for the complexity, with no redundant sentences. Front-loaded with essential information.

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

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

Given no output schema, the description explains all major response fields (leg prices, matched spreads, compatibility_warning, temporal_alignment, skipped counters). It covers edge cases (no matches, shape mismatches) comprehensively.

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 property descriptions. The description adds significant value by explaining the interaction between parameters (e.g., topic vs explicit, override behavior) and providing context for parameter values (e.g., list of topic shortcuts). This justifies 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's purpose: computing cross-venue spread between Kalshi and Polymarket for the same question. It distinguishes itself from siblings like polymarket_arbitrage by focusing on cross-venue comparison. The two modes (topic and explicit) are explicitly described.

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 each mode: topic for pre-mapped macros, explicit for custom pairings. It also warns that pre-mapped topics often return compatibility warnings and are not tradeable, helping the agent decide when 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 readOnly, idempotent, and non-destructive behavior. The description adds that quotes are not published on weekends/holidays and lists return fields, but does not elaborate on other behavioral traits like rate limits or data freshness. 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?

Two sentences efficiently convey purpose, return fields, and a key constraint. Every sentence adds value without redundancy.

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

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

For a simple tool with one parameter and no output schema, the description adequately explains what it does and what it returns. It covers the key constraint (no weekend quotes). Could optionally mention timezone or source, but not necessary for completeness.

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

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

Schema coverage is 100%, so the schema already documents the date parameter with format and business day constraint. The description repeats the format and adds context about weekend/holiday unavailability, which is more about tool behavior than parameter meaning, so baseline 3 is appropriate.

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

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

The description clearly states the tool returns the official PTAX USD/BRL quote (buy and sell rates) for a specific business day, including the timestamp. It differentiates from siblings by specifying a unique financial resource not covered by other tools.

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

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

The description implies usage for obtaining a quote on a business day and notes quotes are unavailable on weekends/holidays. It lacks explicit alternatives or when-not-to-use guidance, but the sibling list contains no direct competitor, so context is sufficient.

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

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

Annotations (readOnlyHint=true, destructiveHint=false) align with description. Adds behavioral context: scoping by identifier, behavior when key omitted (list all), and pairing with 'remember' and 'forget'.

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

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

Three efficient sentences front-load the main action. No redundancy; every sentence adds critical 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?

Despite no output schema, the description fully explains behavior for both cases (key provided vs omitted), scope, and tool relationships. Adequate for a simple tool with rich 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%. Description adds value by explaining the effect of omitting the key (list all) and provides a real example (user_research_topic).

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

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

The description clearly states the tool's dual behavior: retrieve a specific value or list all keys, with explicit verb-resource pairing. It distinguishes from siblings like 'remember' and 'forget' by mentioning them.

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

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

Provides concrete examples of when to use (look up stored context like ticker, address, notes) and mentions scope and pairing. Lacks explicit when-not-to-use instructions, but context is sufficient.

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

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

Annotations already declare readOnlyHint=true, idempotent=true, destructiveHint=false. The description adds that mark_read:true flags events read, and mentions poll-friendliness and the alternative HTTP endpoint. This provides useful behavioral context 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 a single paragraph of four sentences, each serving a purpose: main action, output fields, parameters, and usage notes. It's concise, well-structured, and front-loaded with the key purpose.

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

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

Despite no output schema, the description specifies the output fields (source, citation_uri, raw payload) and even mentions an alternative access method (GET endpoint). For a tool with 5 parameters and no complex nested objects, the description is thorough.

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%, providing baseline 3. The description adds context for parameters like 'since' (ISO timestamp), 'mark_read' (flag returned events read), and 'unread_only' (return only unread), going 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 'Pull fired events from your subscription feed' and specifies the output includes source, citation_uri, and raw payload. It's specific and informative, but does not explicitly differentiate from sibling tools like 'list_subscriptions' or 'recent_changes'.

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

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

The description mentions 'polls work fine' and gives examples of filtering by type and since, and using mark_read. However, it does not explicitly state when to use this tool versus alternatives or 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?

Beyond annotations (readOnlyHint, idempotentHint, etc.), description details multi-source fan-out (SEC EDGAR, GDELT→GNews fallback, USPTO) and explains fallback conditions and soft-failure for USPTO. 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?

Description is somewhat long but well-structured with user-intent examples, parameter details, and sibling differentiation. Every sentence adds value; minimal redundancy.

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

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

Given no output schema, description sufficiently explains return shape (changes[] grouped by source, total_changes count, citation URIs) and covers edge cases (fallback, sunset). Complete for a complex multi-source 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 100% but description adds: explains since accepts ISO date or relative shorthand (with examples), value accepts ticker or CIK, and suggests '30d' for typical monitoring. This adds significant value beyond schema.

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

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

Description starts with concrete query examples, specifies it returns a change feed for a company in a time window, and contrasts with sibling tool entity_profile. The verb 'returns' and resource 'change feed for a company' are 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?

Explicitly states when to use this tool ('What's new with X' etc.) and when to use entity_profile instead ('when you want the static profile'), providing clear differentiation.

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

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

Disclosures scoping by identifier, persistent vs 24-hour retention based on authentication, and pairing with other tools. Adds significant value beyond annotations (idempotentHint, etc.) 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?

Four sentences front-load the core action, then provide usage guidance, scope details, and pairing hints. No wasted words; every sentence earns its place.

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

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

Given simple parameters (string key-value), no output schema, and clear annotations, the description covers purpose, usage, and behavioral details adequately. Sibling tool list further clarifies context.

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

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

Schema already provides 100% coverage with descriptive examples. Description reinforces by explaining usage patterns for key (e.g., subject_property) and value types, adding context 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?

Verb 'save data' plus resource 'memory key-value pairs' clearly states function. Distinguishes from siblings recall and forget by explaining persistence and session 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?

Explicitly says 'Use when you discover something worth carrying forward' and mentions pairing with recall/forget. Lacks explicit when-not-to-use, but the context of persistent storage implies appropriate use cases.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context: it cascades through multiple lookup endpoints internally and replaces 2-3 manual lookups. It also specifies the exact return data per type (ticker, CIK, citation URI for company; RxCUI, ingredient, brand for drug), enhancing transparency without contradicting annotations.

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

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

The description is two paragraphs with the key point ('resolve user-spoken NAME to the canonical/official identifier') stated early. The list of example queries is helpful but slightly redundant. Overall it is fairly concise and front-loaded, though it could be tightened slightly.

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?

There is no output schema, so the description carries full responsibility for explaining return values. It does so thoroughly: for company returns ticker, 10-digit CIK, company_name, and citation URI; for drug returns RxCUI, ingredient, brand, and citation. It also covers accepted input formats (ticker, CIK, name for company; brand or generic for drug). This makes the tool complete and self-contained.

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 described). The description adds meaning beyond the schema: for the 'type' parameter it lists the enum values (company, drug) and explains what each returns; for 'value' it provides input examples (AAPL, 0000320193, ozempic) and formats. This enriches the bare 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 uses specific verbs like 'resolve' and lists example queries ('ticker for', 'CIK for', 'RxCUI for'), clearly stating it converts spoken names to canonical IDs. It distinguishes from siblings by positioning itself as the first step before other tools that require IDs, contrasting with tools like compare_entities or 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?

Explicitly states 'Use FIRST whenever you have a name but need an ID', providing clear when-to-use guidance. However, it does not explicitly mention when not to use it or name specific alternatives among siblings, though the context is clear.

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

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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that the tool probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. It also reveals that the anthropic model requires an _apiKey, which is a behavioral constraint beyond annotations. 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 two sentences with a brief example and a note on return value. It front-loads the primary action and is free of redundant or extraneous 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?

Despite no output schema, the description specifies the return format ('ranked list with score, confidence, signal density per entity'). Annotations cover safety and idempotency. The tool is simple enough that this level of detail is sufficient for correct usage.

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

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

Schema coverage is 100%, but the description adds meaning: it explains the models parameter (supported models, default, key requirement), clarifies that context disambiguates common names, and states that the first entity in entities is the subject and the rest are competitors. This goes beyond the schema descriptions.

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

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

The description clearly states the action ('Compare AI visibility across multiple entities side-by-side') and the resource ('AI visibility'). It explicitly distinguishes from siblings by mentioning it probes each entity with ai_visibility_check and ranks them, which is a multi-entity comparison vs. the single-entity sibling ai_visibility_check.

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

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

The description provides an explicit use case ('competitive AI-marketing audits') and an example question. It also notes the first entity is treated as the 'subject'. However, it does not explicitly exclude other use cases or contrast with siblings beyond the implicit differentiation.

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

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

Annotations already mark the tool as read-only and idempotent. The description adds crucial behavioral details: bundlephobia's first measurement can take 5-30 seconds, partial failures degrade gracefully with sources_failed listed, and the return format. 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 purpose and usage, then details return format and limitations. While packed with information, it is slightly verbose with multiple clauses and lists. Still, every sentence earns its place, making it 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?

Despite no output schema, the description explicitly enumerates all return fields: summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and alternative versions. It also covers failure modes and ecosystem limitations, making the tool fully understandable.

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 well-documented. The description adds context beyond the schema: package accepts scoped names like '@types/node', and version defaults to latest. This provides useful clarification, though not extensive.

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: a composite check for whether to add an npm package, covering license, advisories, version history from deps.dev and bundle size from bundlephobia. It specifically distinguishes from siblings (e.g., scan_competitor_ai_presence) and lists return fields, leaving no ambiguity.

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

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

Explicitly says when to use: when an agent asks about safety, popularity, or size of an npm package. Also provides limitations: NPM ecosystem only in v1, and alternatives for other ecosystems (deps.dev:version directly). Gives context on partial failures and timing (5-30s for first bundlephobia measurement).

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

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

Adds significant behavioral context beyond annotations: truncation at 200K chars with flag, embedding model (BGE-base-en), cosine similarity, 500-char overlapping windows, character offsets for verification. 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?

Single paragraph well-structured: purpose first, then use cases, pairing, technical details. Front-loaded. Slightly dense but no wasted sentences.

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 covers return format (passages with offsets and scores), limitations (200K char cap, truncation), and search mechanism. Adequate for a tool with three parameters.

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

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

Schema covers all parameters with descriptions; baseline 3. Description adds usage examples for query but doesn't substantially enhance parameter meaning beyond schema. Schema coverage 100%.

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 semantic search inside a fetched record with specific verb ('search within') and resource ('a source'). Distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded and contrasts with fetching whole documents.

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 provides direct alternative guidance: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document.'

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. Description adds return format and alternate usage patterns but does not disclose additional behavioral traits like rate limits or error handling.

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

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

Two sentences efficiently convey purpose, common codes, and usage patterns. No unnecessary words or repetition.

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 data retrieval tool with rich annotations and clear schema, the description covers return format, required code, and optional parameters. Could include pagination or error behavior but not essential.

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

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

Schema coverage is 100%, so each parameter is described. The description adds usage context (e.g., using 'last' vs dates) but does not add new meaning beyond what the schema already provides.

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

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

Description clearly states it fetches a specific type of time series (Banco Central do Brasil SGS) by numeric code, with examples of common codes. This uniquely identifies the tool among diverse siblings.

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

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

Provides explicit guidance on using 'last' for recent observations versus date range for full series. Does not explicitly mention when not to use, but context is sufficient for correct invocation.

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 such as OAuth requirement, SMS cap (10/day), webhook auto-disable after 10 failures, and that the subscription is persistent. These go beyond annotations which only indicate non-readonly, open world, idempotent, non-destructive.

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 and clear sections, but slightly verbose. Every sentence adds value, though could be trimmed for brevity.

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, OAuth requirement), the description is complete: covers requirements, type-specific parameters, delivery details, limitations, and return value (subscription ID). No output schema needed.

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

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

With 100% schema coverage, the description adds substantial meaning: it explains each type's params with examples, delivery channel details (email, SMS, webhook with HMAC signing and auto-disable), and nested object structures.

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 proactive monitoring subscriptions and returns a subscription ID. It distinguishes from siblings like 'unsubscribe' and 'list_subscriptions' by specifying creation and providing detailed type examples.

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

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

The description specifies requirements (Pipeworx OAuth account) and gives examples for each subscription type, but does not explicitly contrast with alternatives like 'recent_alerts' for pulling alerts.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds context that results are drawn from a live catalog and returns example questions, which is consistent and non-contradictory.

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 compact and front-loaded with trigger phrases and key information. It efficiently lists categories and usage patterns without unnecessary words, though it could be slightly shorter.

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

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

Despite no output schema, the description adequately explains the return format (category-bucketed example questions with tool+arguments). For an onboarding tool, this is sufficient context for an AI agent to understand expectations.

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

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

The single parameter 'topic' is fully described in the input schema (focus area list). The description merely reiterates this and adds 'Omit for cross-category spread', providing minimal extra value 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: returning category-bucketed example questions with tool+argument shapes, triggered by specific phrases like 'What can I ask Pipeworx?'. It distinguishes itself from sibling tools (e.g., 'discover_tools') by being the onboarding entry point.

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

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

The description gives explicit guidance on when to use: 'Use this FIRST when you do not yet know what Pipeworx can do for you.' It also explains how to call it (no args vs topic). It doesn't explicitly mention when not to use it or alternatives, but the use case is clear.

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

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

The description explains that the row is deactivated (not deleted) and that historical events persist, which goes beyond the annotations (e.g., destructiveHint false). It clarifies the mutation behavior and the side effects, providing transparency without contradicting annotations.

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

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

The description is two sentences with very high information density. The first sentence states the core purpose, and the second adds two important clarifications. There is no unnecessary text.

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

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

For a simple tool with one parameter and no output schema, the description covers the purpose, ownership constraints, behavioral effects, and how to access related data (recent_alerts). It is fully sufficient for agent understanding.

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

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

The input schema already fully describes the only parameter (id) with 100% coverage, stating that it is a subscription id returned by subscribe. The description adds no additional semantic value beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states the action ('Cancel a subscription by id'), specifies the resource, and distinguishes from sibling tools like 'subscribe' and 'list_subscriptions'. It also adds context about ownership and deactivation.

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

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

The description mentions ownership enforcement ('you can only cancel your own subscriptions'), which helps agents decide when to use this tool. It also hints at alternatives by noting that historical events remain available via 'recent_alerts'. However, there is no explicit when-not-to-use or direct comparison to 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 indicate read-only, idempotent, non-destructive. The description adds that the tool returns a verdict with types (confirmed, refuted, etc.), extracted structured form, actual value with citation, and percent delta. It also discloses the domain limitation and that it replaces 4-6 sequential calls.

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

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

The description is front-loaded with trigger phrases and a clear purpose. It is somewhat verbose due to listing trigger phrases but remains well-structured and informative without redundancy.

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

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

Given the single parameter, no output schema, and annotations present, the description thoroughly covers the tool's domain, return types, scope limitations, and efficiency benefits. It provides a complete understanding for correct invocation.

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

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

The single parameter 'claim' has 100% schema coverage. The description provides concrete examples and clarifies the types of claims supported (revenue, net income, cash position), adding significant value 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 explicitly identifies the tool as a claim verification tool with verbs like 'check', 'verify', 'confirm or refute'. It distinguishes from siblings by noting it replaces multiple sequential calls and focuses on company-financial claims.

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 states 'Use whenever the agent needs to check whether something a user said is factually correct' and delimits scope to company-financial claims for public US companies. While it doesn't provide explicit when-not-to-use guidance, the context is clear and sufficient.

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