Discogs

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

The description adds behavioral context beyond the rich annotations (readOnlyHint, etc.): it explains default model, API key requirement for Anthropic, return structure (per-model {score, confidence, signals, raw_response} + combined view), and free/default model. 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?

Two sentences that efficiently convey purpose, operation, and value. Front-loaded with key information (what, how, returns, use cases). No wasted words.

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

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

Given 4 parameters, no output schema, and strong annotations, the description covers return values, use cases, and configuration options. It is complete for the tool's complexity.

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

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

Schema description coverage is 100%, and the description adds meaning: default model, free vs. BYO key for Anthropic, and purpose of context parameter for disambiguation. Enhances understanding of how parameters work together.

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

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

The description clearly states the tool probes LLMs for knowledge about a business/brand/product/topic and scores visibility 0-100. It distinguishes from siblings by focusing on AI visibility checks, not general search or entity resolution.

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) but does not explicitly state when not to use it or name alternative tools for other purposes.

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

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

Annotations already declare read-only/idempotent behavior, so the description adds context by explaining routing across thousands of tools, auto-filling arguments, and returning pipeworx:// citation URIs. This goes beyond the safety annotations and gives meaningful behavioral insight, though it doesn't discuss potential failure modes or response format details.

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

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

The description is long but front-loaded with the core directive and packed with actionable examples and alternatives. Some repetition exists (e.g., listing many examples), but every section contributes to decision-making, so it earns a high score without being fully concise.

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

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

Given the tool's broad scope as a router, the description fully covers use cases, when to use alternatives, and output characteristics (structured answer with citations). No output schema exists, but the description adequately sets expectations for a complex meta-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?

The schema provides 100% coverage with six aliases for the single 'question' parameter, each with clear descriptions. The description adds no additional parameter semantics beyond noting natural-language input, so baseline 3 is appropriate.

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

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

The description clearly states the tool's primary function: routing natural-language questions to one of 5,358 tools across verified sources and returning structured answers with citation URIs. It differentiates itself from siblings like ask_pipeworx_grounded and deep_research, and establishes itself as the default starting 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?

Extensive guidance is provided on when to use this tool: prefer it over web search for factual questions, trigger phrases like 'what is' and 'find', and explicit examples. It also names alternatives (ask_pipeworx_grounded for hallucination-resistant answers, deep_research for multi-part questions), satisfying the when/alternatives requirement.

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

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

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context about the experimental nature, the possibility of candidate routing improvements, and current equivalence to ask_pipeworx. 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 in three sentences: what it is, when to use it, and what it falls back to. Every sentence adds essential information without redundancy.

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

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

For a router tool with six alias parameters and no output schema, the description conveys the tool's purpose, current state, and relation to ask_pipeworx. It could mention the response shape explicitly, but referencing ask_pipeworx's response shape is sufficient 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 coverage is 100%, with all parameters documented as aliases for 'question'. The description does not add additional parameter semantics, so the baseline of 3 is appropriate.

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

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

The description clearly states it is a beta version of ask_pipeworx, an identical universal router with the same tools and response shape, but with experimental routing improvements. It also explains that it currently matches ask_pipeworx exactly when no candidate is active, differentiating it from the stable sibling.

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

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

Explicitly instructs to use it exactly like ask_pipeworx when wanting the newest routing, and clarifies that its results are compared against the stable router. It also states it is a full working router, not a fallback, which prevents misuse.

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

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

Discloses refusal behavior with exact refusal reason enum, describes the full return shape including evidence and fetched_at, and notes the extra LLM call cost. Even with readOnlyHint and idempotentHint annotations, this adds substantial behavioral context 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?

Three dense sentences with no filler. Front-loaded with the core purpose, followed by return format, then usage guidance. Every sentence provides valuable 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 having no output schema, the description fully documents the return structure including refusal reasons and success fields. It also covers cost, use cases, and differentiation from the sibling – a complete package for agent decision-making.

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

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

Schema description coverage is 100% with all parameters being aliases of 'question' and a clear description. The tool description adds no additional parameter-specific information, but the schema fully covers semantics, 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 opens with 'Hallucination-resistant answer mode for high-stakes reads' – a specific verb+resource+scope statement. It clearly distinguishes from sibling ask_pipeworx by explaining the same routing but extraction restricted to tool results.

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: 'whenever an answer will be quoted, cited, or acted on' and when not to: 'prefer ask_pipeworx for casual lookups.' Also mentions the cost tradeoff (one extra LLM call), giving concrete decision criteria.

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

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

The description adds substantial behavioral details beyond annotations: low-confidence resolutions short-circuit, closed markets return status, wide-spread markets flag illiquidity, resolver contract with confidence/alternatives, parent_event extraction, news fallback handling, and resolution-rule risk. Annotations already declare readOnlyHint, etc., but the description enriches context significantly. No contradiction.

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

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

The description is long and packed with details, with front-loaded purpose. Every sentence adds value, but it could be more structured (e.g., bullet points for classifiers, fan-out examples). The density may be overwhelming for an AI agent, though it is informative.

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

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

Given no output schema, the description thoroughly covers return shapes (market, analysis, evidence), edge cases (low confidence, closed markets, wide spreads), resolver contract, parent events, news fallbacks, and resolution-rule risk. It is complete for an AI agent to understand tool behavior without additional documentation.

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

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

Schema coverage is 100% (all 3 parameters documented). The description adds context for 'market' (accepts slug, URL, or text) which aligns with schema, and explains 'depth' and 'include_raw' consequences (quick vs thorough, summarized vs raw). This goes beyond baseline 3 by providing behavioral semantics, though schema already covers basics.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data in one call. It specifies input formats (slug, URL, question text) and output (evidence packet + comparison). This is a specific verb with resource, and while it doesn't explicitly differentiate from siblings, its unique function is unambiguous.

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

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

The description explicitly says 'Use for should I bet on X, what does the data say about Y, or is there edge in Z.' This provides clear context for when to use. However, it does not mention when not to use or list alternatives, though sibling names imply other betting-related tools.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive. Description adds details: for company pulls 10-K data with off-calendar handling, for drug pulls FAERS/FDA/trial counts, sorted by primary metric, returns paired data with citation URIs. No contradictions.

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

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

Description is somewhat lengthy but efficient, front-loading core purpose and usage. It packs significant detail (data sources, sorting, output format) without redundancy, though could be slightly more concise.

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

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

Given no output schema, description adequately explains return format (paired data, citation URIs). Covers both entity types, parameters, and edge cases (off-calendar fiscal years). Complete for the tool's complexity.

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

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

Schema coverage is 100% with basic descriptions. Description enriches by specifying entity types (company/drug) with concrete examples (tickers/CIKs, drug names) and constraints (min 2, max 5). This adds meaningful 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?

The description explicitly states the tool performs side-by-side comparison of 2-5 companies or drugs, with clear examples of natural language queries. It distinguishes from sibling tools like entity_profile by emphasizing multi-entity comparison.

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

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

Explicitly advises to prefer this tool over sequential single-pack lookups when comparing entities, providing clear usage guidance and alternatives.

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

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

Even with strong annotations (readOnly, openWorld, idempotent, non-destructive), the description goes far beyond them: auth and paid-tier requirements, the full return packet schema (verbatim evidence, confidence, source, fetched_at, pipeworx:// citation), explicit 'never invented' gaps[], hop and contradiction fields, semantic excerpting behavior, and latency expectations. This is exactly the behavioral context an agent needs and matches the annotations.

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

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

The description is long, but every segment carries operational value—account requirement, scope, alternatives, output structure, depth tiers, and timing. It is front-loaded with the most actionable info (account and fallback) and uses clear topic sentences. There is slight redundancy (e.g., 'IN ONE call' plus 'in one call' later), but the density is justified for a tool of this 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?

This is a highly complex tool (parallel orchestration across thousands of tools, multi-hop research, no output schema), yet the description covers all critical dimensions: purpose, use cases, exclusions, authentication, the complete return packet including gaps[] and contradictions[], depth behaviors, and latency. The examples further anchor the expected input style. There are no significant missing aspects that would impair tool selection or 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 schema already documents both parameters thoroughly (100% coverage), so the baseline is 3. The description adds meaningful nuance: depth:'thorough' requires a paid plan, 'broad/multi-part' is explicitly fine for the question parameter, and real examples ('compare X and Y's regulatory + financial exposure') clarify the intended form. This pushes it above baseline.

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

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

The description opens with a specific verb ('research'), a concrete resource (Pipeworx's 1395 structured data sources), and a clear mechanism (decomposes into facets, routes to 5,358 tools in parallel). It explicitly distinguishes itself from open-web search and from ask_pipeworx, making its purpose unmistakable.

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 guidance ('Best for broad/multi-part questions over structured data') and equally explicit exclusions ('For a single lookup use ask_pipeworx', 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'). It even handles the authentication edge case by directing unsigned-in users to ask_pipeworx, and explains when each depth level is appropriate.

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

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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds value by explaining the return format (top-N tools with schemas and examples) and that results are ready to call directly, which annotations do not cover.

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

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

The description is a single paragraph that efficiently communicates purpose, usage, and return value. It is front-loaded with the core action and includes necessary details 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 parameter count (6, all documented), no output schema, and the tool's discovery nature, the description fully explains what the tool returns and how to use it, leaving no critical gaps.

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

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

Schema description coverage is 100%. The description further clarifies that multiple alias names (task, q, search, description) map to the query parameter and provides default limit values, adding meaning beyond the schema.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists many domains and explains that it returns top-N relevant tools with full input schemas, distinguishing it from sibling tools like search.

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

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

Explicitly advises 'Call this FIRST when you have many tools available and want to see the option set,' providing clear when-to-use and implicitly when-not-to (if the tool is already known).

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 context: fans across multiple sources, mentions patents soft-fail after May 2025, and notes it's a single parallel call. 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?

Relatively long but well-structured and front-loaded with example queries. Every sentence is informative, though could be slightly more concise.

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

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

Despite no output schema, the description details return fields (CIK, filings with URIs, fundamentals, patents, news, LEI). Covers edge cases (patents soft-fail) and explains parallel execution. Fully informs an agent.

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

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

Schema covers both parameters (100% coverage). Description adds meaning: explains 'value' can be ticker or zero-padded CIK, and states names not supported. Also clarifies 'type' is only 'company'.

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

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

The description clearly states the tool provides a full cross-source profile of US public companies, listing specific data points (CIK, filings, fundamentals, patents, news, LEI). It distinguishes from siblings like 'resolve_entity' and 'compare_entities'.

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

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

Explicitly advises to prefer this tool over chaining multiple single-pack lookups for holistic views. Also tells when not to use (if only a name, use resolve_entity first) and provides example queries.

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

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

Annotations already indicate destructive behavior and idempotency. Description adds that it clears saved data but provides no further behavioral context beyond what annotations convey.

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

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

Two sentences: first states action, second gives use cases. No fluff, front-loaded.

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

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

Given one simple parameter and no output schema, description is sufficient. Slight lack of detail on return value, but overall 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 a description for the only parameter. Description adds no extra semantics beyond 'by key'.

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

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

Description clearly states the verb 'Delete' and the resource 'memory by key', distinguishing it from siblings 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?

Explicitly describes when to use (stale context, task done, clear sensitive data) and mentions pairing with remember and recall.

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

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

Annotations already indicate safe, read-only, idempotent, non-destructive behavior. Description adds value by detailing the process (fetches page, extracts title/description/links, emits markdown format) and output format (single text blob for site-root/llms.txt), which goes beyond annotations.

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

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

Description is concise (3-4 sentences), front-loads the main purpose, then explains process and use cases without 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?

Given no output schema, description adequately covers return value (single text blob). All parameters and use cases are addressed, providing sufficient context for correct tool selection and invocation.

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

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

Schema covers 100% of parameters with descriptions. Description adds the default value (25) and maximum (50) for max_links, plus clarifying that url should be a full URL, providing extra meaning beyond schema.

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

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

Description clearly states the tool generates a production-ready llms.txt file for any URL, with specific use cases. It distinguishes from sibling tools like ai_visibility_check and scan_competitor_ai_presence by focusing on the llms.txt file generation.

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

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

Description lists explicit use cases (client indexing, self-drafting, competitor auditing), providing clear context. However, it lacks explicit exclusions or comparisons to alternative tools among siblings.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. Description adds specific return fields and parameter context, enhancing transparency beyond annotations.

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

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

Two tight sentences with no waste. Front-loaded action verb and resource.

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 get-by-ID tool with output schema, description covers input and output adequately. No missing 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 coverage is 100%, with description of artist_id matching schema's 'Discogs artist ID'. Adds minimal extra 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?

Clearly states 'Fetch a Discogs artist profile by artist_id' with specific verb and resource, and lists return fields. Distinguishes from sibling tools like get_label and get_release.

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?

Implies use when needing an artist profile by ID, but lacks explicit when-to-use guidance or alternatives (e.g., when to prefer search or entity_profile).

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. Description adds detail on return fields and confirms read-only nature. No contradictions.

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

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

One concise sentence that front-loads the purpose and lists return values. No superfluous content.

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 parameter and existence of output schema, the description adequately covers the tool's behavior and return value composition.

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 description for label_id. The tool description does not add additional meaning beyond the schema, meeting the baseline.

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

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

Clearly states it fetches a Discogs record label profile by label_id and lists returned fields (name, profile, contact info, URLs, etc.). Distinguishes from siblings like get_artist, get_master, get_release.

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?

Does not explicitly state when to use this tool over alternatives or provide exclusions. Usage is implied from the resource type, but no direct guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds context about the master's role as a canonical work record, which is useful 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?

Two efficient sentences, front-loaded with the purpose. Every word adds value; no redundancy.

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

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

Given low complexity (1 parameter, output schema exists), the description adequately lists key return fields. No additional explanation of return values needed due to 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% for the only parameter master_id, with a brief description. The tool description adds no further detail about the parameter 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 verb 'Fetch', the resource 'master release detail', and specifies the canonical nature of a master across pressings/editions. It lists return fields, distinguishing it from sibling tools like get_release.

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

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

No guidance on when to use this tool versus alternatives (e.g., get_release). Does not mention when not to use or prerequisites.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety and idempotency. The description adds that it returns a full set of details but does not disclose further behavioral traits (e.g., authentication, rate limits). Since annotations carry the burden, a score of 3 is appropriate.

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

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

The description is a single sentence that efficiently conveys the tool's output scope. It is front-loaded with 'Full release detail' and uses a colon to list specific fields. 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 retrieval tool with one required parameter and an output schema, the description is complete. It lists the key fields returned, and since an output schema exists, further detail on return values is unnecessary. All essential information is provided.

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

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

The input schema has one parameter (release_id) with a description 'Discogs release ID' and 100% schema coverage. The tool description does not enhance this parameter meaning; it simply lists the output fields. Baseline 3 is justified as the schema already documents the parameter adequately.

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 starts with 'Full release detail:' immediately conveying the tool's purpose. It lists specific fields (title, artists, labels, etc.), making it clear what information is retrieved. This distinguishes it from sibling tools like get_artist or get_label.

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 implicitly states when to use this tool: when you need comprehensive release details. It does not explicitly specify when not to use it or name alternatives, but the context from sibling tool names (e.g., get_artist, get_master) provides adequate guidance.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint=false. Description adds return field details and the include_inactive parameter, but no behavioral contradictions.

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

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

Two concise sentences: first states purpose and return fields, second gives usage guidance. 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?

Despite no output schema, the description lists return fields. Parameter is optional and described. Annotations cover safety. Complete for a simple list tool.

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

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

Schema coverage is 100% with a clear description of the only parameter. Description adds no extra meaning beyond the schema, so baseline 3 is appropriate.

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

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

Title and description clearly state the tool lists active subscriptions. It lists specific return fields and distinguishes from siblings like subscribe and unsubscribe.

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

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

Explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' Clearly tells when to use and hints at alternatives.

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

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

Discloses rate limit ('Rate-limited to 5 per identifier per day'), costing ('Free; doesn't count against your tool-call quota'), and how feedback is processed ('The team reads digests daily and signal directly affects roadmap'). Annotations are silent on these, so description adds significant value 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, well-organized paragraph with no redundancy. It covers purpose, usage, constraints, and behavior in a compact yet comprehensive manner. Every sentence contributes 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?

Despite having no output schema and moderate complexity (3 params, nested object), the description covers all relevant aspects: when to use, what to include, constraints (rate limit, quota), and describes how feedback impacts roadmap. It is fully 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 description coverage is 100%, but the description reinforces parameter meanings: e.g., 'bug = something broke or returned wrong data' and provides formatting guidance ('1-2 sentences typical, 2000 chars max'). It also explains the purpose of nested context object, adding clarity beyond schema.

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

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

The description starts with a specific verb-resource combination: 'Tell the Pipeworx team' and clearly states what the tool does (send feedback for bugs, features, data gaps, praise). It distinguishes itself from sibling tools like ask_pipeworx or discover_tools by focusing on direct feedback to the team.

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: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' Also advises against pasting end-user prompts, providing clear guidance on 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=true, openWorldHint=true, idempotentHint=true. Description adds valuable behavioral details: self-aggregating from CF analytics-engine, no PII, caching policy (5min-1h). 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?

Very concise: one opening sentence defining the tool, followed by bulleted use cases and a note on data source and caching. Every sentence adds value, no fluff.

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

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

Given the tool's simplicity (1 optional param, no output schema), the description fully covers purpose, return values, data source, privacy, caching, and ideal use cases. No gaps.

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

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

Schema covers 100% with enum and description. Description adds the semantic distinction: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This provides usage 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?

The description clearly states the tool returns top tools, top packs, and total call volume over selectable windows, derived from CF analytics-engine with no PII. This is a specific verb+resource that distinguishes from sibling tools like search or ask_pipeworx.

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

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

Explicitly lists three use cases (discovering hot data sources, confirming canonical tools, aligning use case with demand). Does not specify when not to use, but context is clear enough given sibling list and description. Missing explicit alternatives, but not needed for such focused tool.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. Description adds detailed behavioral context: semantic anchor (Jaccard similarity), partition filter (placeholder slugs), fill check (realizable vs theoretical edge), 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?

Description is thorough but verbose (multiple paragraphs). However, it is well-structured with clear sections for each mode and additional details. Every sentence adds value; minor redundancy could be trimmed but overall 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?

Despite no output schema, description explains the response structure (opportunities array, partition_check, fill check) and all edge cases (placeholder filtering, similarity threshold, fill risk). Covers all modes thoroughly. Very complete for a complex 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%, but description greatly expands on each parameter: 'event' expects a slug or URL, 'topic' expects a seed question for cross-event scanning. Explains how they work (walks child markets, checks monotonicity, partition check) and their output differences.

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

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

The description explicitly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks, and distinguishes three modes (trending scan, event, topic). This is specific, uses strong verbs, and differentiates from siblings which are unrelated or focus on edges/fill risk.

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

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

Clear guidance on when to use each mode: no args for trending scan, 'event' for a specific market (recommended), 'topic' for cross-event scanning. Explains trade-offs like cross-event catching patterns single-event misses, and includes fill check to avoid non-tradeable signals.

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 the description adds beyond that by detailing internal models, caching behavior ('cached 1h at the KV level'), and response diagnostics explaining why segments may be empty. 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 the main purpose and well-structured into segments (model families, response, knobs). It is dense but contains valuable detail, though it could be more concise for quick consumption.

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 explains the response structure (by_segment, fed_candidates, diagnostics) and all parameter use cases. It covers edge cases like why segments might be empty and caching behavior, making the tool self-explanatory for an agent.

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

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

Schema coverage is 100%, and the description adds significant meaning beyond the schema, e.g., explaining slippage_pp's rationale ('Polymarket has zero trading fees... but bid/ask + thin depth typically eats 20-50bp'), and min_partition_leg_kelly's specific behavior for partition arbs.

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 with a specific verb-resource pair: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edge_tracker by focusing on Pipeworx disagreement 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 provides clear usage context ('Built for "what should I bet on today"') and explains the response structure and knobs. However, it does not explicitly mention when not to use it or contrast with siblings, leaving some inference to the agent.

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

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

Annotations declare readOnlyHint and idempotentHint true. The description goes beyond by detailing data sources (daily snapshots), limits (60-day TTL, snapshot enablement start), decay calculation from daily closes not intraday, and that gaps indicate no scan. No contradiction.

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

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

The description is front-loaded with a clear summary and question. It then details args and response structure. While lengthy, every sentence adds value, especially in the RESPONSE section given no output schema. Could be slightly tighter but efficient.

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

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

The description is comprehensive: it explains inputs, output structure (tracked, expired, snapshot_dates), limits, and computation details. With no output schema, the RESPONSE section fully compensates. Annotations provide safety profile.

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

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

Schema description coverage is 100%, so baseline is 3. The description repeats the same information for 'days' and 'window' parameters without adding new semantic context beyond the schema.

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

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

The description clearly states the tool provides edge persistence and decay telemetry from daily snapshots. It distinguishes itself from sibling tools like polymarket_edges by focusing on historical longevity and shrinkage, answering 'how long has this edge existed and is it shrinking?'

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

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

The description implies usage for historical edge analysis but does not explicitly state when to use this tool versus alternatives. No mention of when not to use it or which sibling tool might be more appropriate for current edges.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false, so tool is safe and side-effect-free. The description adds behavioral details: 'walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, etc.' and warns about forced directional risk. No contradictions.

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

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

The description is long but well-structured with labeled sections (SINGLE-MARKET, BASKET) and front-loaded purpose. Every sentence provides value, though minor verbosity exists (e.g., explaining defaults). Could be slightly tighter 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 complexity (dual mode, no output schema), the description comprehensively covers return fields, use cases, and risks. It explains what each mode returns, how to interpret results, and warns about dominant loss mode (unhedged directional position). Complete for agent decision-making.

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

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

Schema coverage is 100% with descriptions. The tool description adds meaning beyond schema: explains single-market vs basket modes, default values, how size_usd is interpreted differently per mode, and auto-detection for side in basket. This adds significant context.

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

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

The description specifies 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and differentiates single-market and basket modes. It explicitly states 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500', distinguishing it from sibling tools.

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

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

The description gives explicit usage context: 'REQUIRES one of market or event', explains when to use (before arb signals or edges above $500), and provides rationale (theoretical overround not capturable, partial fills cause unhedged positions). It lacks explicit 'when not to use', but the guidance is strong.

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

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

Annotations already mark the tool as readOnly, idempotent, and non-destructive. The description adds rich behavioral details beyond these: the two modes, response structure (leg-by-leg prices, top_spreads_pp), safety fields (compatibility_warning, temporal_alignment, skipped counters), and a caveat that real spreads are rare. 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 lengthy but well-structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS). Every sentence earns its place by explaining behavior, response fields, or usage caveats. Could be shortened slightly without losing meaning, but the structure aids readability.

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

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

Given no output schema, the description thoroughly explains the return format (leg prices, matched spread, safety fields) and their meanings. It covers edge cases (skipped pairs, temporal misalignment) and the rarity of tradeable spreads. For a complex tool with three optional parameters, this provides sufficient context for correct invocation.

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

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

Schema coverage is 100% with each parameter described. The description adds value by listing all 10 topic shortcuts and explaining that kalshi_event_ticker and polymarket_event_slug override the topic-mapped side. This context helps agents choose between topic and explicit parameters effectively.

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

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

The description begins with a clear, specific statement: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It explicitly details two modes (topic shortcuts and explicit tickers) and the tool's primary output (matched spreads). This purpose is distinct from sibling tools like polymarket_arbitrage (single venue) or polymarket_edges (other metrics), making selection unambiguous.

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

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

The description explains when to use the tool (to compare Kalshi and Polymarket for the same outcome) and when not to expect tradeable spreads (compatibility_warning for mismatched bet shapes or unrelated events). It contrasts the topic mode with explicit mode but does not explicitly enumerate all alternatives among siblings. The warning that 'most pre-mapped topics return compatibility_warning today' guides appropriate expectations.

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=true, idempotentHint=true, destructiveHint=false), the description adds scoping details, the behavior of listing keys when key is omitted, and mention of pairing with siblings. 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?

Three sentences, each serving a distinct purpose: core action, usage guidance, and scoping/companion tools. No unnecessary words.

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

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

Given the simple parameter set, full annotations, and clear sibling relationships, the description provides all necessary context for an agent to use the tool correctly.

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

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

The input schema has 100% coverage for the single parameter 'key' with a description. The tool description adds context about the key's purpose and the behavior when omitted, which is valuable beyond the schema.

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

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

The description uses the specific verb 'Retrieve' and clearly identifies the resource as 'a value previously saved via remember'. It also explains the dual behavior of retrieving a specific key or listing all keys, distinguishing it from siblings like 'remember' and 'forget'.

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

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

Explicitly states when to use: 'to look up context the agent stored earlier... without re-deriving it from scratch'. It implies when not to use by describing pairing with 'remember' and 'forget', and notes scoping by identifier.

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

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds valuable context: mark_read flag behavior, that setting it true makes next call return newer events only, and what each alert contains. No contradictions.

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

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

Four sentences, front-loaded with main action, no fluff. Every sentence adds information: purpose, return fields, filtering, mark_read behavior, and alternative access.

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 fields, parameter usage, polling suitability, and alternative endpoint. Complete for an alert retrieval tool with good annotation support.

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. Description adds meaning by showing usage examples (e.g., 'sec_8k' for type), explaining mark_read effect, and stating default limit. Adds 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 clearly states it pulls fired events from subscription feed, lists specific fields returned (source, citation_uri, raw payload), and mentions filtering options. Distinguishes from siblings by focusing on recent alerts and providing an alternative REST endpoint.

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

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

Mentions polling works fine and provides an alternative access URL, but does not explicitly compare to sibling tools like recent_changes or search, nor does it state when to avoid using this tool.

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

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

Annotations already indicate read-only, idempotent, non-destructive. The description adds details on sources (SEC, GDELT/GNews, USPTO), the parallel call nature, date format handling, and return structure. No contradictions.

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

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

The description is well-structured with clear sections, but slightly verbose. However, every sentence contributes meaningful information, so it earns a high score.

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 sources, fallback, relative dates, no output schema), the description covers sources, behavior, return structure, and advises on typical parameters. It is comprehensive.

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

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

Schema has 100% coverage with descriptions. The description adds value by explaining the `since` parameter's format and providing a typical recommendation ('30d' or '1m'), and clarifying the `value` parameter accepts ticker or CIK.

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

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

The description clearly states the tool provides a change feed for a company in a time window, listing example queries and distinguishing it from entity_profile. It uses specific verbs and resources.

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 (e.g., 'what's new with X') and when not to (use entity_profile for static profile). It also explains the fallback behavior between GDELT and GNews.

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

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

Annotations already indicate idempotentHint=true. Description adds scoping by identifier, persistence duration (24hrs for anonymous, permanent for authenticated), which is valuable 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?

Four sentences, front-loaded with core purpose, no redundancy. 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?

Covers all needed aspects: purpose, usage cues, persistence behavior, and relation to sibling tools, despite no output schema.

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

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

Schema has 100% coverage, but description provides concrete examples for key (e.g., 'subject_property') and value ('findings, addresses'), adding meaning beyond the schema's generic 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 states the tool saves data for later reuse, with clear examples like ticker, address, preference. It distinguishes from siblings 'recall' and 'forget'.

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

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

Explicitly says when to use: 'when you discover something worth carrying forward'. Mentions pairing with recall and forget, but does not provide when-not-to-use exclusions.

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

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds that each call cascades through several internal endpoints, replacing 2-3 manual lookups, and describes return formats. No contradiction with annotations.

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

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

The description is front-loaded with the core purpose and examples. Every sentence adds value, though it is somewhat lengthy. It could be slightly more concise without losing information.

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

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

Despite lacking an output schema, the description comprehensively explains return fields for both entity types (ticker+CIK for company, RxCUI+ingredient+brand for drug). It also mentions citation URIs. This covers the essential information an agent 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%, but the description adds meaningful context: for 'type' it lists supported values, for 'value' it gives examples and explains auto-disambiguation for company. This goes beyond the schema's minimal definitions.

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

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

The description clearly states the tool resolves names to canonical IDs, with specific verbs like 'resolve' and examples such as 'ticker', 'CIK', 'RxCUI'. It distinguishes from siblings by emphasizing its role as the first step when a name but no ID exists.

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

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

The description explicitly advises to 'Use FIRST whenever you have a name but need an ID.' It does not explicitly state when not to use, but the context is clear and the examples cover common scenarios.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds behavioral details: it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with specific fields (score, confidence, signal density). 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 extremely concise (2 sentences plus a short example sentence) and front-loaded with the core purpose. Every sentence adds value with no fluff.

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

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

Despite no output schema, the description comprehensively explains the return format (ranked list with score, confidence, signal density). All parameters are covered, and the use case is clearly articulated. The description is complete for this tool's complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining that the first entity is treated as the 'subject' for narrative and that models and context parameters are optional. It also clarifies the role of the _apiKey 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 compares AI visibility across multiple entities side-by-side, using ai_visibility_check and ranking by score. It distinguishes itself from sibling tools like ai_visibility_check (single probe) by emphasizing the comparative, multi-entity aspect.

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 a clear use case ('competitive AI-marketing audits') and an example question. It implicitly guides against using for single entities (use ai_visibility_check instead), but lacks explicit when-not-to-use or alternative references.

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, not destructive. Description adds critical context on partial failure (graceful degradation, 5-30s timeout for bundlephobia first measurement, sources_failed field) and return structure.

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

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

Dense single paragraph front-loads purpose. Every sentence adds value, though slightly long. Could be broken into two sentences for readability.

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

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

Despite no output schema, the description comprehensively details return fields (summary, advisories, links, alternative versions) and handles edge cases (partial failures, timing). Complete for agent decision-making.

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

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

Schema already covers both parameters fully (100% coverage). Description adds useful context: scoped package acceptance and default version behavior, enhancing schema meaning.

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

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

The description clearly states the composite one-call check for npm package evaluation, specifying the exact resources (deps.dev, bundlephobia) and outputs. It distinguishes itself from siblings by being the only npm dependency scanner.

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

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

Explicitly states when to use ('is X safe / popular / small') and when not ('NPM ecosystem only in v1; PyPI/Maven/Cargo/Go fall under deps.dev:version directly').

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, and non-destructive. Description adds that it searches multiple Discogs entity types and supports many filters, providing 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?

Two sentences: first establishes purpose and scope, second enumerates filters. Every word earns its place; no redundancy.

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

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

Given the presence of an output schema and comprehensive annotations, the description adequately covers what the tool does and its primary filters, leaving no critical gaps for an agent to misunderstand.

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

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

Schema coverage is 100% with descriptions for all 12 parameters. Description lists many filters but adds no new meaning beyond what the schema already provides; examples are helpful but not essential.

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 'Full-text search across Discogs (releases, masters, artists, labels)' with specific verb and resource, distinguishing it from sibling search tools like 'search_within' which target different scopes.

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?

Lists many filters indicating typical usage, but does not explicitly state when to use this tool versus alternatives like 'search_within' or 'deep_research'.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable technical details: embedding model (BGE-base-en), similarity metric (cosine), windowing (500-char overlapping), and character limit (200K chars with truncation and flagging). 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 a single, well-structured paragraph that front-loads the purpose. All sentences add value, including examples and technical details. It could be slightly shorter but remains efficient.

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

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

Despite no output schema, the description explains the return format (passages with offsets and similarity scores), the truncation behavior, and the pairing with a sibling tool. For a tool with three parameters and moderate complexity, this is thorough and complete.

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

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

Schema coverage is 100%, so parameters are already well-documented. The description adds minor context (e.g., natural-language query examples, default limit of 5) but does not significantly enhance understanding beyond the schema.

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

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

The description clearly states it performs semantic search inside a fetched record, with specific examples (SEC 10-K, article) and output details (top-N passages with offsets and scores). It distinguishes itself from sibling tools like 'ask_pipeworx_grounded' by explaining the pairing relationship.

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

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

Explicitly states when to use: 'when the record is too big to cram into the prompt — search_within saves context'. It also provides integration guidance with 'ask_pipeworx_grounded'. However, it does not explicitly state when not to use the tool.

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

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

Description adds behavioral details beyond annotations: returns subscription id, requires OAuth, SMS rate limit (10/day), webhook auto-disable after 10 failures. 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?

Concise yet comprehensive; first sentence summarizes, then types and delivery options are clearly separated. 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?

Covers return value, prerequisites (OAuth), constraints (phone verification, rate limits), and all parameter details. 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?

Input schema has 100% coverage, but description adds rich examples and constraints for each type and delivery channel, significantly enhancing understanding.

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

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

The description clearly states 'Create a proactive monitoring subscription to a live-data event stream' and specifies return value. It distinguishes from siblings like list_subscriptions 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?

Description provides context on when to use: requires OAuth account, lists subscription types with examples. Lacks explicit when-not-to-use but alternatives are implied.

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

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

Annotations already indicate safe, idempotent, and non-destructive behavior. The description adds context about the return format (category-bucketed examples) and usage (no arguments for full spread, topic for focus). 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 somewhat lengthy but well-structured. It is front-loaded with examples, then explains the return format and usage. Every sentence adds value, though it could be slightly more concise 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 simplicity (one optional parameter, no output schema), the description fully covers its purpose, usage, and expected output. It also mentions the underlying catalog, making it complete for an AI agent.

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

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

Schema coverage is 100%, and the description adds significant meaning: it explains the topic parameter is optional, lists possible values (finance, pharma, etc.), and describes the effect of omitting it (cross-category spread). This goes beyond the schema's minimal description.

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

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

The description clearly states the tool returns category-bucketed example questions to help users know what to ask. It uses specific verbs like 'returns' and 'call with no arguments', and distinguishes itself from siblings by positioning it as an onboarding entry point, mentioning other meta-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 explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. It provides clear context for when to use it, though it does not explicitly state when not to use it. The mention of alternatives is implied but not exhaustive.

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

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

Annotations already provide readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds valuable context: ownership enforcement, row deactivation (not deletion), and availability of historical events. No contradictions.

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

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

Two sentences: first states the core purpose, second adds critical behavioral details. No wasted words, and key information is front-loaded.

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

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

Given the tool's simplicity (one parameter, no output schema), the description is complete. It covers purpose, ownership constraint, side effects (deactivation), and spin-off (historical events in recent_alerts).

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%, with the 'id' parameter already well-documented as a UUID from subscribe. The description adds no further semantic detail beyond referencing 'by id', 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 verb 'cancel' and resource 'subscription by id', with additional context on ownership and deactivation. It effectively distinguishes from sibling tools like 'subscribe' and 'list_subscriptions'.

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 that ownership is enforced, implying only self-owned subscriptions can be canceled. It hints at usage context by noting historical events remain available via 'recent_alerts', but does not explicitly compare with sibling tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, establishing safety and idempotence. The description adds behavioral details: the two processing paths (SEC EDGAR vs. grounded pipeline), the output format (verdict, value with citation, reasoning), and efficiency gains replacing multiple calls. No contradiction.

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

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

The description is around 150 words, well-structured with examples first, then purpose, then dual-path explanation, then output summary. Every sentence contributes meaning; it could be slightly tighter but remains efficient.

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

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

Given the tool's complexity (two claim types, two processing paths, and a detailed output), the description fully covers usage, behavior, and output without needing an output schema. It explains the verdict types, the citation format, and the fallback mechanism 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?

Both parameters have schema descriptions (100% coverage). The description adds value by explaining how tolerance_pct overrides the default (implied by claim wording, capped at 5) and its use in hallucination detection, which goes beyond the schema.

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

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

The description uses specific verbs like 'check', 'verify', 'confirm or refute' and clearly identifies the resource: 'natural-language claim verification against authoritative sources'. It distinguishes from siblings by stating it replaces multiple sequential calls and is tailored for factual verification, not general Q&A.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and outlines two paths for different claim types. It provides clear context but does not explicitly state when not to use or name alternatives, though the distinction from siblings is implied.

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