Fda Drug Competition

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

Discloses default free model, BYO key for Anthropic, cost implications, return structure (score, confidence, signals, raw_response, combined view). Complements annotations well.

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

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

Three concise sentences: purpose, default behavior, API key note, return structure, 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?

No output schema but description covers return structure. Explains default model and optional Anthropic. Sufficient for agent to understand inputs and outputs.

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

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

Schema coverage is 100% but description adds examples and clarifies usage (e.g., entity can be name/product/person/topic, models supported, _apiKey only needed if anthropic selected).

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 probes LLMs for visibility scoring (0-100) per model, with default model and optional Anthropic. No sibling tool overlaps exactly, so it distinguishes well.

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 use cases provided (AI-marketing audits, pre-launch brand checks, competitive monitoring). Lacks explicit non-usage or alternatives but context is clear.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the description's job is to add beyond those. It adds context like returning stable pipeworx:// citation URIs, routing to appropriate tools among 5,564 sources, and working on all tiers. The only missing detail is whether the tool handles errors or returns partial results when sources are unavailable, but this is minor.

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 compelling preference statement and key capability summary, followed by guidance and examples. It could be slightly tighter – the list of example fields (SEC filings, FDA data, etc.) is helpful but includes 15 items, some of which overlap. Still, every sentence adds value for an agent's decision-making, so it's well-structured for its length.

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

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

Given the tool's high complexity (routing among thousands of sources), no output schema, and rich annotations, the description provides complete guidance: when to use, what to expect (structured answer with citations), how parameters work (aliases), and how it differs from siblings. The absence of an output schema is compensated by describing the return format (structured data with stable citation URIs). Everything an agent needs to decide whether to call this tool is present.

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

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

Schema description coverage is 100% – all six parameters (question plus aliases q, text, input, query, prompt) are documented. The description goes further by stating the parameter accepts 'your question or request in natural language' and provides multiple examples. This adds meaningful context by emphasizing natural language flexibility, but the baseline is 3 due to full schema coverage; the extra examples and alias enumeration justify a 4.

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

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

The description clearly states the tool routes questions to 5,564 verified sources across 1,462 domains, returning structured answers with citations. It uses specific verbs ('routes', 'fills arguments', 'returns'), identifies the resource (Pipeworx system), and distinguishes from siblings like ask_pipeworx_grounded and deep_research by scoping use cases and citing the tool's role as the default entry point.

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

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

The description explicitly tells when to use this tool (factual questions about real-world data) and when to step up to alternatives (ask_pipeworx_grounded for high-confidence single answers with verbatim evidence, deep_research for broad multi-part questions, and 'web search' for breaking news). It also lists example query triggers ('what is', 'look up', 'current') and concrete examples, making usage boundaries clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value beyond these by explaining the experimental nature, that it is a full working router (not a fallback), and the current state of no active candidate. It does not contradict annotations. The description could be more transparent about potential behavioral differences when a candidate is active, but overall it is strong.

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

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

The description is moderately long but every sentence adds value: identity, current state, usage guidance, fallback behavior. It is front-loaded with the key beta purpose. Could be slightly tighter, but there is no wasteful 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 the tool's complexity (a universal router wrapping 5,564 tools) and the absence of an output schema, the description adequately explains the beta nature, current equivalence, and purpose. It does not detail how to craft questions or the return shape, but that is expected from the stable sibling. The description is complete enough for an agent to decide when to use this beta version.

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, with all 6 parameters described (including aliases for question). The description adds no additional parameter information beyond what is already in the schema. With high schema coverage, the baseline is 3, and the description does not improve upon it.

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

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

The description clearly identifies the tool as a beta version of ask_pipeworx, a universal router handling 5,564 tools. It distinguishes from siblings by explicitly stating it is the experimental edge with candidate routing improvements, and notes that currently it matches the stable version exactly. This provides specific verb+resource with clear differentiation.

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

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

The description provides explicit guidance: 'Use it exactly like ask_pipeworx when you want the newest routing; results are compared against the stable router to decide what merges.' This tells the agent when to use it (to test new routing) and implies the alternative (stable ask_pipeworx). It could be more direct about when not to use it, but the context is sufficient.

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

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

Beyond the annotations (readOnly, openWorld, idempotent), the description richly discloses behavioral traits: it extracts answers only from tool results, returns evidence and confidence, gives explicit refusal reasons like 'not_in_source' and 'data_truncated', and costs an extra LLM call. No contradiction with annotations.

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

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

The description is dense but well-structured, front-loading purpose and mechanism, then detailing return formats and usage guidance. Every sentence earns its place; the trade-off cost and refusal reasons are critical and concise.

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

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

Given the tool's complexity and absence of an output schema, the description fully covers return values, refusal types, use cases, and cost implications. It also contrasts with the sibling ask_pipeworx, making the contextual picture complete for an agent to invoke correctly.

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

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

Schema description coverage is 100%—all six parameters are documented as aliases for the question. The description adds no parameter-specific semantics beyond what the schema already states, so the baseline of 3 applies.

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 clear, specific purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It distinguishes from siblings by detailing the grounded extraction mechanism and explicit refusal behavior, making it clearly different from ask_pipeworx and ask_pipeworx_beta.

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

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

The description explicitly states when to use this tool: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also provides a strong exclusion guideline: 'prefer ask_pipeworx for casual lookups' due to the extra LLM call cost.

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

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

Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds extensive behavioral details: fan-out to category-specific data packs, market resolution, classification, evidence packet return, error handling (low-confidence matches short-circuit, closed markets skip fan-out), tradeability flags, and resolution-rule risk. No contradiction with annotations.

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

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

The description is long but well-structured with labeled sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, etc.), making it scannable. Every sentence adds value, though some redundancy (e.g., repeated examples) could be trimmed. Forefront loading of purpose and usage is good.

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

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

Given the tool's complexity (3 params, no output schema), the description is exceptionally thorough. It covers response shapes, error cases, edge conditions (closed markets, wide spreads, low-confidence matches), and even resolves arbitration rule risk. An agent has sufficient information to invoke and interpret the tool correctly.

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

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

The description adds meaning beyond the input schema: it explains that 'market' can be slug, URL, or question text; clarifies 'depth' options (quick vs thorough) and default; and explains when to use 'include_raw' (true for recompute/cite/post-process, false for smaller responses). Schema coverage is 100%, but the description significantly enriches 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 it researches a Polymarket bet by pulling Pipeworx data, with specific verb 'research' and resource 'Polymarket bet'. It distinguishes from siblings like ask_pipeworx and polymarket_edges by being the go-to tool for bet-specific research, explicitly listing use cases such as 'should I bet on X'.

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"', giving clear context for when to use. It does not explicitly exclude alternatives or mention when not to use, but the list of siblings and the specialized nature imply boundaries.

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

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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds value by detailing data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, result sorting by primary metric, and the inclusion of citation URIs. No contradictions with annotations.

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

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

The description is front-loaded with common query patterns and is well-structured, but it is somewhat verbose. Every sentence adds value, but it could be slightly more concise for a 5.

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

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

Given no output schema, the description explains the return format (paired data + citation URIs) and sorting. It covers data sources and edge cases like fiscal year handling. Missing error handling or limits, but adequate for a two-parameter tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context about what each type pulls and example values, but does not provide new parameter semantics beyond what the schema already contains.

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

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

The description clearly states the tool performs side-by-side comparisons of 2-5 companies or drugs. It provides concrete example queries and differentiates itself from sequential single-pack lookups, which is a key distinction from sibling tools like entity_profile.

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

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

The description explicitly advises to prefer this tool over sequential lookups when comparing entities, and gives examples of when to use it (e.g., comparing, ranking). However, it does not explicitly state when not to use it, which would make it a 5.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. The description adds extensive detail: explicit gaps[] for unanswered facets, semantic excerpting, hop field and citation_uri, contradictions[] for standard/thorough, and expected response times. 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 long but well-structured, front-loading the critical account requirement and alternative tool guidance. Every sentence adds value, though minor trimming could improve conciseness without losing information.

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

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

For a tool with only two parameters and no output schema, the description covers output format, evidence structure, iteration behavior, performance expectations, and edge cases (empty gaps for non-structured topics). It is thorough and leaves no major gaps.

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

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

Schema coverage is 100% so baseline is 3. The description adds meaningful context: for 'depth' it explains behaviors of each enum value (quick=3 facets, standard adds gap-recovery + contradictions, thorough=6 facets + iterative hop + contradictions), and for 'question' it clarifies natural language and suitability for broad/multi-part questions.

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

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

The description clearly states the tool does grounded multi-source research across structured data sources, distinguishing it from open-web search and siblings like ask_pipeworx. It uses specific verbs: 'researches', 'decomposes', 'routes', 'returns'.

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

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

Explicitly tells when to use this tool vs alternatives: 'For a single lookup use ask_pipeworx', 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx', and 'If you are not signed in, use ask_pipeworx instead'. Also states account requirement and depth tier limitations.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: it returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' 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 moderately long but well-structured. It front-loads the purpose, then provides usage context, and finally details return format. Each sentence is informative and avoids redundancy. Could be slightly tighter but is efficient overall.

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

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

Given the tool's simplicity (1 required param, no output schema, 6 params total with aliases), the description adequately explains input format (natural language query), output format (list of tools with schemas), and behavior (top-N, ready to call). No gaps significant enough to hinder usage.

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

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

Schema description coverage is 100%, so the description adds limited new meaning beyond the schema. However, it provides example queries and clarifies that multiple aliases ('q', 'task', 'search', 'description') map to 'query', which reinforces understanding but does not significantly elevate parameter semantics beyond baseline.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists numerous specific data sources (SEC filings, FDA drugs, etc.) and explicitly differentiates from sibling tools by positioning itself as a discovery tool to call first, contrasting with siblings that are domain-specific data 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 provides explicit usage guidance: 'Use when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This clearly indicates when to use, though it does not explicitly state when not to use or name alternative tools.

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

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

The description details the cross-source behavior, what fields are returned (cik, recent_filings, fundamentals, etc.), and notes the patent API sunset (soft-fail). Annotations are consistent (readOnly, idempotent, non-destructive). Lacks mention of potential limitations like rate limiting or timeout, but otherwise transparent.

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 query examples first, then detailed list of outputs. It is somewhat long but each sentence adds value (e.g., patent sunset, fallback mechanisms). No redundancy or waste.

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

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

Given the tool's complexity (multiple data sources), 2 parameters, no output schema, and annotations present, the description is remarkably complete. It explains the fan-out behavior, returns list, error handling for patents, and LEI fallback. No gaps identified.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds concrete examples ('AAPL', '0000320193') and clarifies that names are not supported, which goes beyond the schema. A slight reduction from 5 because the schema already described both parameters 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 clearly states 'full cross-source profile of a US public company in ONE parallel call,' providing a specific verb ('profile') and resource ('entity'). It distinguishes from siblings like resolve_entity (for names) and compare_entities (for comparisons).

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 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view' and warns 'names not supported (use resolve_entity first if you only have a name).' This provides clear when-to-use and when-not-to-use guidance.

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

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

Annotations already indicate destructiveHint=true, so description adds context about clearing sensitive data, justifying the destructiveness. No contradiction.

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

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

Two efficient sentences front-loaded with action and condition. Every word earns its place.

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

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

For a simple 1-parameter tool with no output schema, the description fully covers purpose, usage, and relationships. 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 already describes the key parameter at 100% coverage. Description adds no new parameter information beyond 'by key', so baseline 3 applies.

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 'delete a previously stored memory by key', specifying the verb and resource. Distinguishes itself from siblings by mentioning pair with 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 lists when to use: when context is stale, task is done, or want to clear sensitive data. Also directs to pair with 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?

Description details behavioral steps beyond annotations: fetches the page, extracts title/description/key links, emits standard markdown. Annotations confirm read-only, idempotent, non-destructive, and description aligns without contradiction.

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

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

Three sentences: first explains purpose and action, second details process and output, third lists use cases. No wasted words, front-loaded with key 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?

With 2 params, full schema coverage, rich annotations, and no output schema, the description fully explains what the tool does, how it works, and what output to expect (text blob). Use cases cover typical scenarios.

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

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

Schema coverage is 100% with clear descriptions. Description adds context by explaining that url is used to fetch the page, and mentions extraction process. For max_links, it adds no new info but reinforces schema. Overall 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?

The description clearly states the verb 'generate', resource 'llms.txt file', and purpose 'so AI crawlers can index the site cleanly'. It distinguishes the tool from all siblings (e.g., ai_visibility_check, ask_pipeworx) which are unrelated.

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 three explicit use cases: getting a client's site indexed, drafting for your own project, auditing competitor. No exclusions or alternatives mentioned, but siblings are so dissimilar that context is sufficient.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds no behavioral details beyond listing return fields, which is sufficient but not extra.

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

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

Two sentences, front-loaded with the main action, and no unnecessary words. Every part serves a purpose.

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

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

Given the simple schema, rich annotations, and no output schema, the description covers all needed context: return fields, usage guidance, and no gaps.

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

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

Schema coverage is 100% with a well-described parameter ('include_inactive'). The description does not add 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?

The description clearly states the verb 'list' and the resource 'subscriptions', specifying it's the caller's active subscriptions. It lists the return fields, distinguishing it from sibling tools like 'subscribe' and 'unsubscribe'.

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

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

The description provides guidelines: use it to review what you're monitoring before adding more or to find an ID to cancel. This implies context and alternatives, though not explicit exclusions.

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

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

Beyond the annotations (readOnlyHint, idempotentHint), the description adds critical context: 'Patent and exclusivity dates are FDA listings, not a legal conclusion about enforceability or actual generic entry.' This warns users about data limitations and legal implications, 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?

Two sentences with no redundancy. The first sentence states the core purpose, and the second provides an essential caveat. Every sentence adds value without waste.

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

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

The description covers what data is retrieved (product, patent, exclusivity rows) and the scope (single application). An output schema exists, so return values are defined. A mention of any limits or error handling could improve completeness, but it is sufficient.

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

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

Schema coverage is 100% with a clear description for the only parameter 'application_number'. The tool description adds no extra parameter information beyond the schema, so a 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 uses a specific verb 'Retrieve' and clearly defines the resource: 'all FDA Orange Book product, patent, and exclusivity rows for one NDA or ANDA application'. This distinguishes it from sibling tools like 'orange_book_product_search' (broader search) and 'orange_book_patent_expirations' (expiration-focused).

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

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

The description states the tool is for 'one NDA or ANDA application', implying it is for single-application detail rather than bulk queries. However, it does not explicitly mention when not to use it or provide alternatives from the sibling list, such as the expiration tools for aggregate views.

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 a valuable behavioral note that 'Approved ANDAs indicate regulatory approval, not launch, supply, substitution, or market share.' This clarifies output interpretation beyond what annotations provide, though it could mention more about data freshness or rate limits.

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

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

The description is two sentences with no wasted words. The first sentence concisely states the purpose, and the second adds a critical caveat. It is front-loaded and efficient.

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

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

Given the tool's complexity (2 parameters, output schema present, annotations rich), the description covers the core purpose and a key behavioral note. It does not address pagination or error cases, but with openWorldHint and a well-defined schema, this is acceptable.

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

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

Schema coverage is 100% with descriptions for both parameters (drug and limit). The description does not add new information about parameters beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Profile' and the resource 'FDA Orange Book competition' for a specific input (ingredient or trade name). It lists the key outputs (matched applications/products, holders, RLD, patents, exclusivities) and includes a clarifying caveat about ANDAs. This distinguishes it from sibling tools like orange_book_product_search and orange_book_application_detail.

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

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

The description implies usage for profiling Orange Book competition but does not provide explicit guidance on when to use this tool versus alternatives. No when-not or alternative recommendations are given, though the context of Orange Book (small molecules) versus Purple Book (biosimilars) may be inferred from sibling names.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds useful behavioral context about filtering ('anything excluded is counted and dated back to you') and a caveat ('exclusivity codes have program-specific meanings; expiration alone does not guarantee generic approval or launch'). This adds value beyond annotations without contradicting them.

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

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

The description is three sentences, front-loaded with the core purpose, and every sentence conveys unique information. There is no fluff, repetition of the tool name, or redundant restating of annotations.

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 annotations, 100% schema coverage, and presence of an output schema, the description is nearly complete. It covers the main use case, filter behavior, and interpretational caveats. It lacks only an explicit pointer to sibling alternatives for related but different questions (e.g., patent expirations), but that is not necessary for basic invocation.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds conditional semantics by explaining when to include from_date/to_date ('only when you want a window') and clarifies behavior for excluded items. This enriches the parameter meaning beyond the bare schema descriptions.

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

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

The description opens with a specific question ('When does FDA marketing exclusivity expire for a drug') and identifies the resource ('listed Orange Book exclusivity expiration dates for a brand or ingredient'). It also adds scope detail ('earliest and latest across the whole schedule'), which clearly distinguishes it from sibling tools like orange_book_patent_expirations.

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: 'Ask by drug alone to get everything on file; add from_date/to_date only when you want a window.' It clearly states when to use date filters, though it does not name alternative tools or explicitly state when not to use this tool.

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

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

Annotations already declare the tool as read-only, open-world, idempotent, and non-destructive. The description adds value by specifying the return format (dated events with ingredient, trade name, applicant, application, patent/use-code, and exclusivity-code context), which goes beyond the baseline. It does not mention pagination or rate limits, but the annotation coverage is strong.

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

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

Two sentences, front-loaded with purpose, no unnecessary words. Each sentence adds information: what the tool does and what output to expect.

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?

While the output schema and annotations cover a lot, the description leaves gaps in date-range semantics and event_type filtering. The tool has 5 parameters and the description only addresses the overall purpose and output. For an agent to select and invoke correctly, more detail on required parameters would improve completeness. However, the presence of an output schema and example partially mitigates this, so a 3 is fair.

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 only 40% (only drug and limit have descriptions). The description does not compensate by explaining parameter semantics for from_date, to_date, or event_type. It hints at patent and exclusivity as event types, but lacks details on required date range meaning or the format of dates. The example in the schema provides some context, but the description itself adds little parameter-level value.

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

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

The description states a specific verb ('Create') and resource ('combined calendar of FDA Orange Book patent and regulatory-exclusivity expirations'), clearly differentiating it from sibling tools that list only patent expirations or only exclusivity expirations. The purpose is immediately clear.

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

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

The phrase 'for cliff monitoring' gives clear context for when to use the tool. It does not explicitly mention alternatives or exclusions, but the use case is well implied. This is clear context without explicit when-not guidance, so a 4 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 declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context by mentioning deduplication across strengths/products and the chronological summarization, which are not evident from annotations alone.

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 front-loads the action and packs all necessary details without redundancy. It avoids filler and remains concise while covering key scope and return fields.

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 rich annotations (read-only, idempotent, open-world) and the presence of an output schema, the description sufficiently covers the tool's scope and return fields. It does not need to explain return values in detail, as the output schema handles that. No critical gaps remain.

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

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

Schema description coverage is 67% (limit and include_discontinued are described). The description compensates for the undocumented 'drug' parameter by specifying it matches an ingredient or trade name. This adds value beyond the schema, though limit and include_discontinued are not further elaborated.

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 summarizes FDA-approved ANDA applications chronologically, with deduplication across strengths/products. This distinguishes it from sibling tools like product_search or application_detail, and the specific verb 'summarize' plus the resource (ANDA applications) makes its purpose unambiguous.

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

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

The phrase 'for generic-entry analysis' provides some context on when to use it, but there is no explicit guidance on when not to use it or which alternatives to prefer. It does not name sibling tools as alternatives for different scenarios, so usage guidance is implied rather than explicit.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds useful behavioral context about the output composition: it returns reference applications, ANDA counts, and date ranges, and clarifies the 'active versus elapsed' distinction tied to the as_of_date parameter. No contradiction exists.

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 dense sentence that packs multiple pieces of information without being overly verbose. It is front-loaded with the main purpose and then lists specifics. No filler; it earns its place despite being longer than typical tool descriptions.

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

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

With a rich output schema present, the description does not need to detail return formats. It provides sufficient context for an agent to understand what the tool aggregates and how the as_of_date affects active/elapsed listings. It could mention sibling comparisons, but the overall information is adequate for correct invocation.

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

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

The input schema provides complete descriptions for all three parameters (drug, limit, as_of_date). The description adds little beyond restating that 'active and elapsed' depends on the cutoff, which the schema already documents. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states the tool builds an FDA Orange Book loss-of-exclusivity evidence profile and enumerates the key data returned (reference applications, ANDA counts, patent/exclusivity date ranges), which distinguishes it from sibling tools like orange_book_patent_expirations or orange_book_product_search. It does not explicitly name an alternative, but the scope is specific enough for an agent to identify its unique role.

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

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

The description implies usage for loss-of-exclusivity diligence but provides no explicit guidance on when to choose this over the many overlapping Orange Book sibling tools (e.g., generic entry timeline, exclusivity expirations). It lacks 'use this when' or 'instead of' statements, making it only moderately helpful for tool selection.

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

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses that filtered results count excluded entries ('anything the window excludes is counted and dated back to you'). It also adds a caveat that dates are sponsor-submitted and do not guarantee launch dates or freedom to operate.

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

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

The description is moderately long but every sentence contributes: purpose, usage guidance, scope, and data caveat. It is front-loaded with the core question 'When do the patents on a drug expire', making it easy to scan.

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

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

With an output schema present and rich annotations, the description covers purpose, usage, scope, and caveats. It fully addresses the tool's functionality for both full-schedule and filtered queries, and provides necessary context about data reliability.

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 all four parameters at 100% coverage, giving a baseline of 3. The description adds value by clarifying the semantic intent of from_date/to_date as filtering windows where excluded items are still accounted for, and by emphasizing drug-alone queries for the full schedule. It doesn't add much beyond that, so a 4 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 lists Orange Book patent expiration dates for a drug, with ordering and earliest/latest. It distinguishes from sibling tools by focusing on patent expirations and explicitly notes biologics belong in the Purple Book.

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

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

It gives explicit guidance: ask by drug alone for full schedule, add from/to_date only for a window, and warns that excluded items are still counted. It also directs biologics users to the Purple Book, serving as an alternative resource.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds behavioral value by listing the returned fields (NDA/ANDA status, therapeutic-equivalence codes, RLD flags, etc.), which is beyond what annotations provide.

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

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

Two sentences, front-loaded with the verb, and every word adds value. It avoids redundancy and presents the key information efficiently.

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

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

With a 4-parameter schema that is 100% covered, an output schema present, and annotations providing safety and idempotency, the description is complete for its purpose. It covers the main search use cases and return fields without needing to repeat structured data.

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 every parameter has a description. The tool description also lists search criteria that map to query and application_number, but it does not add meaning beyond the existing parameter descriptions, 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 uses a specific verb ('Search') and clearly identifies the resource (FDA Orange Book products) and the search criteria (ingredient, trade name, applicant, or application number). This fully distinguishes it from more specialized sibling tools like orange_book_patent_expirations or orange_book_competition_profile.

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

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

Clear context is provided: the tool is used to search Orange Book products by common identifiers and returns basic product and approval data. It does not explicitly mention exclusions or direct users to alternative sibling tools, but the intended use case is well 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 declare read-only, idempotent, and non-destructive behavior. The description adds behavioral context by clarifying that it maps to 'approved' ANDA competition and returns TE-code groupings, which helps set expectations about scope. It does not contradict annotations and provides value beyond the structured flags.

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

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

The description is two sentences, front-loaded with the primary action and resource, followed by return contents. Every word serves a purpose, with no repetition of schema or annotations.

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

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

Given the tool has moderate complexity, an output schema, and rich annotations, the description is complete: it states what it does, what it returns, and the mapping dimensions. An agent can confidently invoke it based on this description alone.

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 67% (limit and include_discontinued have descriptions; drug does not). The description adds some meaning by indicating the drug parameter refers to 'reference listed drugs and reference standards,' but this is partial compensation. The baseline of 3 is appropriate given the schema covers most parameters.

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

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

The description uses a specific verb 'Map' with a clear resource: 'FDA Orange Book reference listed drugs and reference standards' and an explicit outcome: 'approved ANDA competition by dosage-form/route and therapeutic-equivalence code.' It also lists return contents, distinguishing it from siblings like orange_book_product_search (search) and orange_book_competition_profile (focused profile).

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

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

The description implies use when one needs a landscape view of reference drugs mapped to ANDA competition, but it does not explicitly state when to use this tool over alternatives like orange_book_competition_profile. No exclusions or comparisons are provided, so usage is inferred rather than directed.

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

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

Annotations provide no useful hints (all false), so the description carries the burden—and it delivers. It discloses rate limiting (5/day), quota impact (free, no call quota), the token-return behavior, how to read the resolution status, and the scope boundary. This goes well beyond what annotations offer.

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

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

Although the description is long, every sentence carries actionable information: use cases, scope exclusions, token mechanics, rate limit, and quota note. It is front-loaded with the purpose and never wanders into filler. The structure follows a logical flow from 'what' to 'when' to 'how'.

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

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

There is no output schema, yet the description covers the essential output behavior: filing returns a claim_token, and passing that token later returns status/fix information. It also addresses rate limits, scope boundaries, and content best practices. For a feedback tool with four optional params, this is fully 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%, so baseline is 3. The description adds meaningful context beyond the schema: it explains that filing without an account returns a claim_token and how to use it later with the example pipeworx_feedback({claim_token:"pwfb_…"}). It also gives content guidance ('describe in terms of Pipeworx tools/packs') that clarifies message and context parameters.

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

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

The description opens with a specific verb and resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It enumerates distinct feedback categories (bug, feature/data_gap, praise), and this clear scope differentiates it from sibling tools like ask_pipeworx or forget, none of which serve as a feedback channel.

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

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

It gives explicit 'Use when' conditions (wrong/stale data, missing tool, praise) and explicit exclusions: ONLY for Pipeworx-served tools, not other MCP servers, with a named alternative ('file it with that server instead'). It also explains when to use claim_token for follow-up, leaving no ambiguity about when to select 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 valuable details: data source (CF analytics-engine), no PII, and caching behavior (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?

Four tight sentences, front-loaded with main purpose, every sentence adds value. No redundancy or fluff.

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

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

With one optional parameter, no output schema, and rich annotations, the description fully covers return values, privacy, caching, and 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 coverage is 100%, and schema description already explains parameter meaning. The description adds minimal extra context (e.g., shorter windows for hot, longer for steady), but this is not substantial 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, packs, and call volume over recent windows, distinguishing it from siblings like discover_tools or entity_profile by focusing on aggregated trending data from other agents.

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

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

Three explicit use cases are provided, but no when-not-to-use or alternative tools are mentioned. Context is clear enough for effective selection.

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

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

Annotations indicate read-only, idempotent, non-destructive behavior. The description adds extensive behavioral context: scan logic, partition filter, placeholder rejection, fill check, and return structure. 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 detailed and well-structured with clear sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). It is front-loaded with the main purpose. However, it is somewhat lengthy and could be slightly tighter without losing essential information.

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

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

Given the complexity of the tool (multiple modes, algorithmic filters, output structure) and the lack of an output schema, the description covers all necessary aspects: input modes, algorithm, filters, return format, and even a caution about fill check. No gaps.

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

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

Schema coverage is 100%, and the description adds significant meaning beyond the schema: clarifies mode differences, provides example inputs (slugs, URLs), and explains the semantic anchor and partition filter. This helps the agent select and invoke the correct 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's purpose: finding arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) and contrasts with siblings like polymarket_edges and polymarket_fill_risk.

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

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

Explicit guidance is given for each mode: no args for trending scan, event for specific markets, topic for cross-event scanning. It also explains when to use polymarket_fill_risk for custom sizing and warns against trading when fill check shows zero realizable edge.

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

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

The description discloses extensive behavioral traits beyond annotations: three segments with model details, response structure including diagnostics, caching behavior (1h KV-level), Fed bet exclusion rationale, and edge/Kelly field definitions. All consistent with readOnlyHint, idempotentHint, and openWorldHint annotations.

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

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

The description is thorough and well-structured with sections, but somewhat verbose. Every sentence serves a purpose, providing necessary detail for a complex tool. Could be tightened slightly but maintains clarity.

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

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

For a complex tool with 9 parameters and no output schema, the description is remarkably complete. It covers purpose, all parameters with usage advice, response structure with segments and diagnostics, caching, and edge cases like empty segment reasons. No gaps identified.

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 meaningful extra context for parameters like slippage_pp (suggesting bump for thin partitions), min_liquidity, max_spread_pp, and min_partition_leg_kelly (explaining behavior with partition arbs). This enhances understanding beyond the schema.

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

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

The description clearly states the tool scans top Polymarket markets and returns opportunities where Pipeworx data disagrees with market price, using a specific verb and resource. It distinguishes from siblings by highlighting its unique discovery purpose for 'what should I bet on today' without paging hundreds of markets.

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 context for when to use the tool (opportunity discovery) and includes detailed guidance on tradeable-edge knobs and parameter settings. However, it does not explicitly state when not to use it compared to alternative tools like polymarket_arbitrage or polymarket_fill_risk.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive behavior. The description adds significant context by explaining the return structure (tracked, expired, snapshot_dates) and limitations (60-day TTL, daily closes). 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 comprehensive but slightly verbose. It is well-structured with clear sections for args, response, and limits. However, some sentences could be merged for brevity.

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

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

Despite lacking an output schema, the description fully details the return format (tracked, expired, snapshot_dates) and covers limitations. It provides all necessary context for a tool with two optional parameters and a rich response.

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

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

Schema coverage is 100%, but the description adds meaning by explaining the 'days' parameter as lookback with default and max, and 'window' as snapshot family with examples. It also mentions defaults, adding value beyond the schema.

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

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

The description states that the tool provides 'edge persistence and decay telemetry' and answers a specific question about edge freshness. It clearly distinguishes from siblings like 'polymarket_edges' by focusing on historical time-series and decay analysis.

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 (e.g., to differentiate between a fresh edge and an old one) but does not explicitly mention when not to use it or provide direct alternatives. The context of sibling tools provides implicit differentiation.

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

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

Annotations (readOnlyHint: true, etc.) already indicate a safe read operation. Description adds useful behavioral context: walks the order-book ladder, returns fill details, slippage, and risk warnings like forced_directional_risk. No contradictions with annotations.

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

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

Description is long but well-structured with clear section labels (SINGLE-MARKET, BASKET). All sentences add value; no filler. Could be slightly shorter but complexity justifies length.

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

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

Despite no output schema, description thoroughly lists return fields for both modes, including risk indicators and verdicts. It covers prerequisites (requires market or event), explains edge cases (thin books, partial fills), and provides enough detail for an agent to use the tool correctly.

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

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

Schema description coverage is 100%, but description adds significant value: clarifies market vs event mutual exclusivity, explains side defaults per mode, and defines size_usd interpretation (max spend vs target proceeds vs settlement notional). This goes well 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: 'Realizable-vs-theoretical edge check against live CLOB order-book depth'. It distinguishes from siblings by explicitly directing use before acting on polymarket_arbitrage signals or polymarket_edges trades above ~$500, differentiating it from those 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?

Description provides explicit when-to-use guidance: before acting on polymarket_arbitrage signals or polymarket_edges trades above ~$500. It explains why (unrealizable edge, unhedged directional risk) and implicitly defines when not needed (small trades). Alternatives are named in 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 declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. Description adds extensive detail: two modes, response structure (leg prices, top spreads, safety fields), explanation of compatibility_warning cases, temporal_alignment, and skipped counters. No contradiction with annotations.

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

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

Description is dense but well-structured: starts with purpose, then modes, then response fields, then safety notes. Every sentence adds information, but could be slightly tighter. Front-loaded effectively.

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

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

Despite no output schema, the description thoroughly covers response fields (prices, spreads, safety fields like compatibility_warning and temporal_alignment). Explains non-obvious cases (e.g., matched_pairs=0 scenarios). Fully informative for an agent.

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

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

Schema coverage is 100% with descriptions for all three parameters. Description adds value by listing the 10 pre-mapped topics, explaining override behavior, and providing examples. This adds moderate 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?

Clearly states cross-venue spread between Kalshi and Polymarket for the same resolving question. Two modes (topic and explicit) are described, distinguishing it from sibling tools like polymarket_arbitrage which focus on intra-venue arbitrage.

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

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

Provides explicit guidance on when to use: comparing same question across venues. Mentions typical spread range (2-25pp) and warns that real spreads are rare. Does not explicitly contrast with specific sibling tools, but context makes differentiation clear.

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

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

Annotations already declare this as read-only, idempotent, and non-destructive. The description adds valuable context by noting the chronological order, the uniqueness of BLAs, and the specific designations returned (biosimilar vs. interchangeable), which goes beyond the annotation-only baseline.

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 sentence with a clear main clause and a compact enumeration of return fields. Every phrase earns its place, and there is no fluff or redundancy.

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

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

Given the rich output schema, complete parameter descriptions, and strong annotations, the description is sufficiently complete for an agent to select and invoke the tool. It clearly communicates purpose, scope, and key output categories, though it doesn't discuss edge-case behavior such as limits or sorting, which are partially covered by schema.

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

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

Schema description coverage is 100%, so parameters 'reference_product' and 'limit' are already well-documented. The description does not add much parameter-level detail beyond listing the output fields, which aligns with the baseline score for fully covered schemas.

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 a specific verb ('chronologically summarize') and resource ('unique FDA-licensed 351(k) BLAs associated with a reference biologic'), clearly distinguishing it as a timeline-focused tool among Purple Book siblings. It also names exact output fields, 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?

The phrase 'for entry analysis' and the specification of 'associated with a reference biologic' provide clear contextual guidance on when to use this tool. However, it does not explicitly name alternative tools or state when not to use it, stopping short of a full 5.

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 a valuable caveat that approval/licensure does not establish commercial launch or current availability, which helps agents interpret results correctly. No contradiction.

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

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

Two sentences with no filler. The first sentence conveys the core purpose and filters, while the second provides a necessary data caveat. The structure is front-loaded and efficient.

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

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

The description is sufficient for a simple filtered list with an output schema, and the caveat adds important context. It could be slightly stronger by explicitly stating that omitting filters returns all products, but it is not a major gap.

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

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

Schema coverage is 67% and the query parameter has no schema description. The description fills this gap by explaining that query can refer to a reference product, applicant, or product name, and that all filters are optional.

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 'List FDA-licensed 351(k) biosimilar and interchangeable products' which is a specific verb and resource. It also names the optional filter dimensions (reference product, applicant, product name), making the purpose clear and distinct from sibling timeline/search 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?

Usage is implied: this is a listing tool with optional filters. However, it does not explicitly state when to use this tool versus alternatives like purple_book_product_search or purple_book_biosimilar_entry_timeline, nor does it mention 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 safe, read-only, idempotent behavior. Description adds valuable context: counts unique BLAs, separates categories, and clarifies exclusions. No contradiction.

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

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

Two sentences, no wasted words. Implicit front-loading: first sentence defines action and resource, second clarifies what it doesn't do.

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?

Output schema exists, so return values are covered. Description explains core functionality and exclusions. Could mention data limitations or update frequency, but annotations (openWorldHint) and sibling tools provide context. Sufficient for a profiling 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 50% (only 'limit' described). Description does not add meaning for 'biologic' parameter (type, acceptable values, format). Agent must infer from example 'Humira', which is insufficient.

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

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

States clearly it profiles FDA-licensed biologic competition by BLA count and classification, and explicitly lists what it does and does not cover. Distinguishes from sibling tools like 'purple_book_biosimilars'.

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 scope and exclusions (no launches, sales, etc.), which implies when to use. Does not explicitly name alternative tools for missing capabilities, but the clear scope 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 declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds a legal caveat about interchangeability and pharmacy substitution, which is useful contextual information but does not disclose additional behavioral details like pagination or data freshness.

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

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

The description is two sentences long, with the first conveying the core purpose and optional filters, and the second adding an important caveat. Every sentence earns its place without redundancy or irrelevant detail.

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

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

For a simple list tool with two optional parameters, an output schema, and comprehensive annotations, the description provides sufficient context. It names the data source (FDA Purple Book), the specific designation (351(k) Interchangeable), and the filtering dimensions, making it 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?

With 0% schema description coverage, the description partially compensates by explaining that the query parameter can filter by reference product, applicant, or product name. However, the 'limit' parameter is not described, and the description does not fully clarify query matching semantics.

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 lists FDA Purple Book products designated as 351(k) Interchangeable, using the specific verb 'List' and identifying the exact resource. This distinguishes it from sibling tools like purple_book_biosimilars by focusing on the interchangeable subset.

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 context for when to use the tool: to retrieve interchangeable products with optional filtering. While it does not explicitly name alternatives or exclusions, the scope is well-defined enough to guide selection among the many related purple_book 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 cover readOnly, openWorld, idempotent, and non-destructive behaviors. The description adds a valuable domain caveat about biosimilars not necessarily being launched/interchangeable, which is behavioral context beyond the annotations. It also states what the tool returns, but no mention of pagination or rate limits.

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

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

Three concise sentences: first states action and search fields, second states return type, third adds an important caveat. No redundancy, front-loaded with the most critical information.

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

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

Given the output schema exists and annotations are rich, the description is complete enough. It tells users what the tool searches, what it returns, and includes a critical interpretation caveat. Sibling tools exist, but the description sufficiently orients the user for this search tool.

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

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

Schema description coverage is 75% (3 of 4 params), which is high but below the 80% baseline. The description's mention of search fields mirrors the 'query' parameter description exactly, adding no new semantic value. It does not address 'license_type' or 'include_discontinued' beyond the schema.

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

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

The description clearly states the verb 'Search' with a specific resource 'FDA Purple Book biologic products' and lists search dimensions (proper/proprietary name, applicant, reference product, or BLA). It also indicates return type (licensure and product-level attributes), distinguishing it from other purple_book tools that are more specialized.

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 context for when to use this tool (searching by various identifiers) and implicitly positions it as the general search entry point versus siblings like purple_book_biosimilars. However, it does not explicitly mention alternatives or exclusions, so it falls short of the highest bar.

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 the tool is read-only, idempotent, and non-destructive. The description adds the important caveat that 'Updated or added does not necessarily mean newly licensed,' which is interpretive context beyond the annotation flags. 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 two short sentences that immediately state the tool's purpose and define the change codes. The second sentence adds a valuable caveat without unnecessary length, and the structure is front-loaded and efficient.

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

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

The description covers the core function and interpretive nuance, and since an output schema exists, return values are already specified. However, it does not explain how limit and query parameters affect results, nor how this tool relates to the broader Purple Book family. Given the read-only annotations and output schema, it is adequate but not exhaustive.

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

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

The input schema has no parameter descriptions (0% coverage), but the description explains the meaning of change_type values (N, R, U). However, it does not address the limit or query parameters, leaving their semantics unclear. This partial compensation earns a middle score.

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

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

The description clearly states that the tool returns the changes section of the FDA's latest monthly Purple Book report, and it defines the meanings of the N, R, and U codes. This specificity distinguishes it from sibling Purple Book tools like product search or biosimilars by focusing on the changes section.

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 the tool is for accessing the changes section of the monthly report, but it does not provide explicit when-to-use or alternative recommendations. Given the many sibling Purple Book tools, additional guidance such as 'for product search, use purple_book_product_search' would improve clarity.

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 safety traits (readOnlyHint, idempotentHint), but the description adds a critical caveat that product rows and BLA counts must not be interpreted as market participants, launches, or market share, which is behavioral context beyond annotations.

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

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

Two sentences: the first front-loads the purpose and method, the second adds an important caveat. No wasted words; highly efficient.

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

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

The description covers the core function and a key interpretation warning. The output schema exists, so return values are not needed. However, the limit parameter's role (e.g., pagination) is missing, and the interaction with the output schema is not clarified. Still, it's adequate for a simple search tool.

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

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

Schema description coverage is 0%. The description explains that reference_product is used as a proper or proprietary name, but provides no information about the limit parameter. Thus, only one of two parameters gets any semantic clarification.

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

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

The description clearly states the tool finds a reference biologic and its FDA-licensed 351(k) products by name, distinguishing it from sibling tools like purple_book_biosimilars or purple_book_interchangeables.

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 via name search but does not explicitly state when to use this tool vs. alternatives like purple_book_biosimilars or purple_book_product_search. No when-not-to-use guidance is provided.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the tool's safety is clear. The description adds value by explaining scoping ('scoped to your identifier') and the behavior of omitting the key to list all keys, which is not covered by annotations.

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

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

The description is concise (three sentences) and front-loaded with the core action. Every sentence adds value: purpose, usage guidance, and scoping/pairing. No redundant or extraneous 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 nature (one optional parameter, no output schema), the description covers the tool's purpose, usage context, scoping, and relationship with siblings. It provides enough information for an agent to correctly invoke the tool without needing additional details.

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 parameter schema has 100% coverage with a description for 'key'. The description adds meaningful context by providing examples of what keys might contain (target ticker, address, research notes), enhancing understanding beyond the schema.

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

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

The description clearly states the tool retrieves values saved via 'remember' or lists all keys when the key argument is omitted. It specifies the verb 'retrieve' and resource 'values saved via remember', distinguishing it from sibling tools 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?

The description provides explicit use cases (e.g., retrieving a target ticker, address, research notes) and explains why to use it (avoid re-deriving context). It also mentions pairing with remember and forget. However, it does not explicitly state when not to use this tool or compare it to other siblings.

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

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

Annotations already indicate readOnly, idempotent, openWorld. Description adds critical behavioral detail: setting mark_read:true flags events as read to affect future calls. No contradiction with annotations.

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

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

Three sentences: first states purpose, second details features, third adds usage tips. Every sentence adds value, front-loaded, 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 no output schema, description explains return structure (source, citation_uri, raw payload) and key behaviors (filtering, mark_read, polling). Completely adequate for a read-only retrieval 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 covers all 5 parameters with descriptions (100% coverage). Description adds examples (e.g., 'sec_8k' for type, ISO timestamps) and clarifies mark_read effect, providing 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?

Clearly states it pulls fired events from the subscription feed, specifies returned fields (source, citation_uri, raw payload), and mentions filtering options. Distinguishes itself from sibling tools like 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?

Explicitly states polling works fine and provides an alternative REST endpoint for scripts/dashboards, guiding when to use the tool vs. direct API. Does not explicitly compare to siblings but offers clear usage context.

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

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

Discloses that the tool fans out to multiple sources, with a fallback from GDELT to GNews when rate-limited or on 5xx, and a soft-fail for USPTO due to API sunset. Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so the description adds useful behavioral context beyond annotations.

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

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

The description is well-structured with a clear purpose statement, parameter explanations, usage examples, and a sibling differentiation. Every sentence adds value without unnecessary fluff, making it both informative and concise.

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

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

Given the tool's complexity (multiple data sources, fallback logic, parameter options, no output schema), the description is remarkably complete. It covers data sources, fallback behavior, parameter formats, and provides an explicit alternative tool, leaving no major gaps.

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

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

Schema coverage is 100%, but the description adds significant meaning: explains `since` accepts ISO dates or relative shorthand (e.g., '7d', '1y'), `value` can be ticker or CIK, and `type` is currently limited to 'company'. This goes well beyond the schema descriptions.

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

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

The description clearly states that the tool provides a change feed for a company over a time window, aggregating from SEC EDGAR, GDELT/GNews, and USPTO. It gives example queries like 'What's new with X' and distinguishes from the sibling tool 'entity_profile'.

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

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

Explicitly tells when to use this tool vs. 'entity_profile' (static profile). Provides parameter guidance for `since` (ISO date or relative shorthand) and examples for `value` (ticker or CIK). Also explains 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?

Discloses key traits beyond annotations: 'Stored as a key-value pair scoped by your identifier', 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours.' Adds context on scoping and retention that annotations alone don't provide.

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

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

Multiple sentences but tightly written with no waste. Front-loaded with core purpose, followed by usage triggers, then storage details, then pairing instructions. Every sentence earns its place.

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

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

For a simple write-only tool with no output schema and clear annotations, the description provides all necessary context: purpose, when to use, storage behavior, scoping, retention, and sibling relationships. No gaps.

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

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

Schema coverage is 100%, so baseline is 3. Description adds extra meaning: explains that key-value is scoped by identifier, gives example patterns in key description ('subject_property', 'target_ticker'), and defines value as 'any text'. This goes beyond bare schema.

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

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

The description clearly states 'Save data the agent will need to reuse later', specifying the verb 'save' and resource 'data'. It distinguishes from sibling tools 'recall' and 'forget' by naming them for retrieval and deletion.

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

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

Explicitly tells when to use: 'when you discover something worth carrying forward'. Provides clear alternatives: 'Pair with recall to retrieve later, forget to delete.' This gives solid guidance on selection 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?

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description discloses rich behavioral details: sources of identifiers, explicit handling of unresolved identifiers, graceful degradation when external services are unavailable, and internal cascade of multiple lookup endpoints. This goes well beyond the structured safety hints.

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

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

The description is long but front-loaded with practical query examples and structured by supported types. Every sentence provides valuable detail for a complex tool, though some parentheticals (e.g., the ISIN explanation) add length. It is appropriately sized for the tool's complexity.

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

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

Given the tool's complexity and absence of an output schema, the description covers input types, output labeling, unresolved handling, fallback behavior, and internal cascading. It could briefly mention the exact response structure, but the description is thorough enough for an agent to select and invoke correctly.

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

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

Even though schema coverage is 100% (baseline 3), the description significantly enhances parameter understanding: it details accepted input formats (ticker, CIK, ISIN, company name), explains ISIN-to-LEI mapping, and clarifies the behavior for drug type (brand vs generic). This is substantive added meaning, not repetition of 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 resolves user-spoken names to canonical/official identifiers, with specific verb 'resolve' and resource 'entity'. It distinguishes itself from siblings by noting these identifiers are required as input by other tools, and provides concrete query examples.

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

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

Explicit guidance is given: 'Use FIRST whenever you have a name but need an ID.' This directly tells the agent when to invoke this tool versus alternatives, and the description of supported types and fallback behavior further clarifies appropriate usage contexts.

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 that the tool internally calls ai_visibility_check for each entity, ranks results, and surfaces most/least recognized. Adds behavioral context beyond annotations (readOnlyHint, openWorldHint, etc.) such as the probing process and output format. 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, each serving a distinct purpose: purpose, process, use case, output. No redundancy. Front-loaded with the core action.

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

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

With 4 parameters all described in schema and enriched by description, and no output schema needed because return type is clearly stated (ranked list with score, confidence, signal density per entity), the description is fully sufficient.

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

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

Adds meaning beyond the schema: explains that the first entity is the 'subject' for narrative purposes, gives examples for the context parameter, clarifies free default for models, and notes API key usage. Schema coverage is 100%, and description enriches it.

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

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

Defines the tool as comparing AI visibility across multiple entities side-by-side, with clear verbs (compare, scan, probe) and a specific resource (AI presence). Distinguishes from sibling ai_visibility_check by emphasizing multi-entity comparison and ranking.

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

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

Provides an explicit use case (competitive AI-marketing audits) and a concrete example question. Implies when to use this tool over ai_visibility_check (single vs. multiple entities), though does not explicitly list 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 declare readOnlyHint, idempotentHint, destructiveHint. Description adds valuable behavioral details: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, sources_failed lists timeout. No contradiction with annotations.

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

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

Description is front-loaded with purpose, but slightly verbose with inline details. However, every sentence adds value. Could be tightened slightly, but still efficient.

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

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

Despite no output schema, the description thoroughly explains return format: summary block with specific fields, per-advisory detail, links, alternative versions. Also covers ecosystem scope, partial failure, and timing. Complete for complexity.

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

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

Schema description coverage is 100%, so baseline is 3. Description adds no additional meaning beyond what schema provides (package name and version). No extra guidance on default behavior or formatting.

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

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

The description clearly states the tool is a composite check for npm packages covering license, advisories, version history, bundle size, etc. It uses specific verb 'scan' and resource 'dependency', and distinguishes from related tools like deps.dev:version.

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

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

Explicit when-to-use: 'whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me''. Also specifies limitations: '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 readOnlyHint, openWorldHint, idempotentHint as true. The description adds beyond annotations: BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, 200K char cap with truncation flag. 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 4-5 sentences, front-loads purpose and usage, then pairs with a sibling, then adds technical details. Every sentence adds value. 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 3 parameters, no output schema, the description covers purpose, usage, behavioral specifics, and parameter semantics. Mentions what is returned (passages with offsets and similarity scores). Adequate for a complete understanding.

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

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

Schema coverage is 100%. Description adds examples for the query parameter and explains the limit parameter's range and default. Adds nuance like 'text to search inside' and 'natural-language query' beyond the schema descriptions.

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

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

The description states 'Semantic search INSIDE a fetched record' and provides specific examples (SEC 10-K, article, long tool result). It distinguishes itself from siblings by mentioning its pairing with ask_pipeworx_grounded, differentiating it from general search or retrieval 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 when the record is too big to cram into the prompt' and explains that it returns top-N passages with offsets. It also pairs with ask_pipeworx_grounded for grounding. No explicit when-not or alternatives, but the context is clear.

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

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

Discloses key behavioral traits: idempotentHint from annotations is consistent with 'create' semantics. Adds context about delivery limits (10 SMS/day), webhook auto-disable after 10 failures, and verification requirements for phone. No contradiction with annotations.

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

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

The description is a single dense paragraph, which packs a lot of information. Every sentence adds value, but it could benefit from bullet points or section breaks for readability. Still, front-loading with the purpose is good.

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

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

Given the tool's complexity (3 params, nested objects, no output schema), the description covers all necessary aspects: requirements, supported types with examples, delivery options with limits and verification, and return value. It is self-contained and leaves no major gaps.

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

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

Even though schema coverage is 100%, the description adds substantial meaning: concrete examples for each type's params (e.g., sec_8k items codes, polymarket_edge topics) and detailed delivery channel behavior including webhook HMAC signing. This goes well beyond the schema's basic descriptions.

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

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

The description starts with 'Create a proactive monitoring subscription to a live-data event stream'—a specific verb+resource combo. It clearly distinguishes from sibling tools like 'list_subscriptions' and 'unsubscribe' by focusing on creation.

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

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

Explicitly states that a Pipeworx OAuth account is required and that anonymous/BYO cannot persist subscriptions. Provides examples for each subscription type and delivery channel. However, it doesn't explicitly advise against using this tool vs. alternatives like 'recent_alerts' for simple polling.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context: it returns 'category-bucketed example questions' with 'exact tool + argument shape', drawn from the live catalog, and that calling with no arguments gives a full spread. 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 somewhat long but front-loaded with example queries and uses bullet-like structure (categories in parentheses). Every sentence adds value, though 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 no output schema, the description fully explains what the tool returns: category-bucketed example questions with tool and argument shapes. It covers the onboarding use case and how to focus via topic, making it complete for an agent.

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

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

Schema coverage is 100% with a clear description of the topic parameter. The description adds meaning beyond the schema by listing example topic values and explaining that omitting yields a cross-category spread, which is useful for an agent.

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 returns category-bucketed example questions for what to ask Pipeworx, listing specific categories. It clearly identifies the verb-resource 'returns example questions' and distinguishes from sibling tools like ask_pipeworx by positioning it as an onboarding entry point.

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

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

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 also explains when to omit the topic argument for a full spread or pass a specific topic to focus, providing clear 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?

Discloses that the row is deactivated not deleted, and historical events remain available via recent_alerts. Annotations already indicate non-destructive and idempotent, so description adds valuable context without contradiction.

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

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

Two succinct sentences front-loaded with core action, followed by key constraints and behavior. No wasted words.

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

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

For a simple one-parameter tool with complete annotations and no output schema, the description fully covers purpose, usage condition, and behavioral side-effect. 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% for the single parameter 'id'. Description adds no extra meaning beyond the schema's own description, meeting baseline for high coverage.

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

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

Clearly states 'Cancel a subscription by id'. Verb and resource are specific. Distinguishes from sibling '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?

Explicitly states ownership enforcement: 'you can only cancel your own subscriptions'. Provides clear usage condition but doesn't explicitly mention alternative tools for other cases.

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

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

The description goes far beyond the readOnlyHint and idempotentHint annotations by disclosing nuanced behavior: the distinction between 'could_not_verify' (verification did not happen, not evidence) and 'unsupported' (no source exists), the two routing paths, the return format with citations, and the tolerance math. This is critical behavioral context that the annotations do not provide, and it is consistent with 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 longer than average but well-structured: it leads with natural-language triggers, then explains the purpose, routing, output, and a clearly flagged 'IMPORTANT' caller note. Every sentence adds value, and the formatting (dashes, capitalization) improves scannability. It could be trimmed slightly, but it is not bloated.

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?

Since there is no output schema, the description fully specifies the return value: verdict types, actual value with citation, reasoning, and the meaning of two ambiguous verdicts. It also explains the two execution paths and covers the tool's scope. For a complex verification tool with nuanced outcomes, this is highly 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 the baseline is 3. The description does not add extra meaning beyond the schema; it mentions 'exact percent-delta math' and 'tolerance implied by the claim wording' which are already captured in the parameter descriptions. The schema itself fully documents both parameters, so no additional semantics are needed.

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

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

The description clearly states the tool's function: 'natural-language claim verification against authoritative sources.' It names specific verbs ('verify', 'confirm or refute', 'true or false') and the resource (claims), plus distinguishes two distinct processing paths (SEC EDGAR for company-financial claims, grounded pipeline for all others). This is a specific, actionable purpose that stands apart from generic research 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 whenever the agent needs to check whether something a user said is factually correct,' providing a clear trigger. It also explains routing for different claim types and notes that it 'Replaces 4–6 sequential calls,' implying efficiency. However, it does not explicitly name alternative tools to use instead for different scenarios, so it lacks the when-not-to-use exclusions that would earn a 5.

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