Cms Open Payments

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds significant behavioral context: default model cost (free Workers AI), billing implication for Anthropic (BYO key, pay directly), and per-model return structure (score, confidence, signals, raw_response). 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 three sentences long, with key information front-loaded and no filler. Every sentence adds value: purpose, default behavior, optional parameter implications, and use cases. Highly efficient.

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

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

Given 4 parameters and no output schema, the description covers purpose, default, optional parameters, return structure (per-model fields + combined view), and use cases. It could add error handling or limits but is sufficient for an AI agent to use 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%, so baseline is 3. The description adds useful meaning beyond schema: it clarifies the default model for the 'models' parameter, explains when _apiKey is needed, and describes context's disambiguation role. This lifts the score to 4.

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

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

The description clearly states the tool's purpose: probe LLMs about a business/brand/product/topic and score visibility (0-100) per model. It specifies a specific verb ('probe'), resource ('LLMs'), and outcome, and distinguishes from sibling tools like ask_pipeworx and scan_competitor_ai_presence by focusing on AI visibility scoring across multiple models.

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 use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It explains optional parameters like _apiKey for Anthropic probing. While it doesn't explicitly state when not to use it, the context is sufficient to distinguish from alternatives, making it a 4.

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 and idempotent, so the description adds context about routing behavior, filling arguments, and returning stable pipeworx:// citation URIs. It also mentions 'one fast call' and that it works on every tier, which are useful operational traits 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 long but front-loaded and dense with actionable guidance, including examples and alternative tools. Each sentence adds information, though it could potentially be tightened, so a 4 is warranted.

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 purpose, usage, alternatives, domains, and return format (structured answer with citations). It does not specify failure behavior when no tool matches, but given the complexity and annotations, it is sufficiently complete for an agent to use effectively.

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

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

Schema coverage is 100% with aliases documented, so baseline is 3. The description adds value by giving example questions and explaining that the tool fills arguments automatically, which clarifies how to frame the question 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 that the tool routes questions to 5,558 tools across 1,461 verified sources and returns structured answers with citation URIs. It lists specific domains (SEC filings, FDA, FRED/BLS, etc.) and distinguishes itself from siblings by naming ask_pipeworx_grounded and deep_research as alternatives.

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 'PREFER OVER WEB SEARCH' and instructs to use for factual questions, providing trigger phrases like 'what is', 'look up', 'get the latest'. It also gives clear when-not-to-use guidance: use ask_pipeworx_grounded for hallucination-resistant single answers and deep_research for broad/multi-part questions.

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

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

Although annotations already declare readOnlyHint=true and idempotentHint=true, the description adds rich behavioral context: it explains that candidate routing improvements are enabled live during tests, that no candidate is currently active, that the last was retired on a specific date, and that results are compared against the stable router. It also clarifies that this is a full working router with no fallback, which is important for agent expectations.

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 four sentences long, but each sentence delivers distinct value: beta status, current inactive state, usage directive, and reassurance that it's a full router. It is front-loaded with the core identity and avoids fluff, though it is slightly denser than strictly necessary.

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 universal router with no output schema, the description adequately covers what the tool is, how it relates to ask_pipeworx, when to use it, and its current behavior. It mentions the same response shape as the stable router, which suffices in place of an output schema. It could elaborate on example use cases but does not need to given the close alignment with ask_pipeworx.

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 100% coverage with detailed descriptions for all six parameters, all documented as aliases for 'question'. The description adds no new parameter semantics beyond noting 'same arguments' as ask_pipeworx, so the baseline of 3 is appropriate given the schema's completeness.

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 this as a beta version of ask_pipeworx, a 'universal router' accessing 5,558 tools, and distinguishes it from siblings by emphasizing the 'experimental edge' and candidate routing improvements. It specifies exactly what the tool does and how it differs from the stable ask_pipeworx.

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

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

The description provides explicit usage guidance: 'Use it exactly like ask_pipeworx when you want the newest routing.' This clearly indicates when to prefer this beta over the stable router, and the comparison against the stable router implies when not to use it. It doesn't explicitly name all alternatives (e.g., ask_pipeworx_grounded), but the key distinction is covered.

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 readOnly/openWorld/idempotent, all consistent. Description adds rich behavioral detail: exact return shape, refusal reasons, one extra LLM call cost, and the fact that it only uses tool result content. Could mention rate limits or latency but the provided disclosure 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?

Dense but well-structured: mode definition, routing summary, return format, refusal cases, use cases, cost trade-off. Only minor excess is listing all refusal reasons in detail which could be trimmed, but this serves behavioral transparency. Two compact sentences that front-load the key purpose.

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

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

For a tool with no output schema, the description fully specifies return value shape, error/refusal cases, and cost/use trade-offs. The 1-param schema is trivial, and the description covers all contextual gaps that structured fields don't provide.

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

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

Schema has 100% coverage with clear descriptions including alias list. Description focuses on usage context rather than repeating parameter info, which is appropriate since schema already fully documents parameters. Baseline 3 is correct; description adds the semantic context of what kind of question 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 this is a 'hallucination-resistant answer mode for high-stakes reads' with a specific verb ('EXTRACTS the answer') and resource (data from tool routing). It distinguishes itself from sibling ask_pipeworx by emphasizing grounded extraction and explicit refusal behavior, 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?

Explicitly states when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also names the alternative (ask_pipeworx) and gives a clear exclusion: 'prefer ask_pipeworx for casual lookups' due to cost. This is exemplary usage 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?

The description provides extensive behavioral details beyond the annotations (readOnlyHint, openWorldHint, etc.). It explains fan-out logic with concrete examples, response shapes with specific fields, error handling (e.g., low confidence short-circuit, closed market blocking), and resolution-rule 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?

The description is thorough but overly verbose, mixing high-level purpose with detailed field explanations and edge cases. While the front-loading is good (purpose first), the length may overwhelm some agents. A more structured breakdown (e.g., sections for input, process, output) could improve conciseness without losing information.

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

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

Given the tool's complexity (multiple classifiers, fan-out patterns, response shapes, edge cases like closed markets or low confidence), the description is remarkably complete. It covers all major aspects beyond the schema and annotations, including resolver contract, parent event extraction, and resolution-rule risk. Without an output schema, the description fully compensates.

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

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

All three parameters are described in the schema with good coverage (100%). The description goes further by explaining the `market` parameter with examples, clarifying `depth` options (quick vs. thorough), and adding context on `include_raw` (when to use false vs. true). This adds meaningful value beyond the schema alone.

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 purpose: researching a Polymarket bet by pulling Pipeworx data. It specifies acceptable input formats (slug, URL, question text) and outlines the process (resolve, classify, fan out, return evidence). This distinguishes it from sibling tools like polymarket_edges or polymarket_arbitrage, which focus on different aspects.

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 recommends using the tool for queries like 'should I bet on X' and 'what does the data say about Y'. It also details various scenarios (low confidence, closed markets, wide spreads) and how the tool handles them. However, it does not explicitly state when to avoid using this tool, which would strengthen guidelines.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, etc. The description adds specific behavioral details: data sources (SEC EDGAR/XBRL, FAERS), correct fiscal year handling, sorted results, citation URIs. No contradiction with annotations.

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

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

The description is front-loaded with example queries and imperative guidance. Every sentence adds value. Despite length, no wasted words. Structure is logical: trigger examples, then behavior per type, then output format.

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 adequately describes return format (paired data + citation URIs). With full schema coverage, annotations, and detailed parameter behavior, the description is complete for a tool of this complexity. Nothing essential is missing.

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% (2 params described). The description adds meaning beyond schema: for 'company' type it lists pulled metrics (revenue, net income, cash, debt), for 'drug' type it lists adverse events, approval counts, trials. Greatly aids selection and invocation.

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 a specific verb+resource: compares 2-5 entities in one call. It distinguishes from sequential lookups and lists example queries users might type. The purpose is immediately evident and differentiates from siblings.

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

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

Explicitly says 'ALWAYS PREFER over sequential single-pack lookups' and gives a list of trigger phrases. It tells when to use (comparison) and implies when not to (single entity). No ambiguity.

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 readOnly, openWorld, idempotent, and non-destructive hints, and the description adds substantial context beyond them: account/payment requirements, parallel decomposition, findings packet contents, explicit gaps[] for unanswered facets, contradictions[] details, semantic excerpting behavior, citation availability, and expected runtime. This rich disclosure goes far beyond the annotation 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 long but each sentence carries substantive guidance—auth requirements, alternatives, return format, gaps, citations, timing. It is front-loaded with critical account/usage caveats. It loses one point for being a dense single wall of text that could benefit from clearer segmentation, but no sentence is wasted.

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 no output schema, the description covers auth, depth differences, expected response structure, failure modes, citations, contradictions, and runtime. It also addresses when the tool may underperform and explicitly redirects to siblings. This is as complete as a description can reasonably be.

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

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

Input schema covers both parameters 100%, so the baseline is 3. The description still adds meaning by explaining depth levels behaviorally (standard "re-angles unanswered facets", thorough "chases the best leads") and by noting that broad/multi-part questions are acceptable because decomposition is the point. This adds clarity beyond schema descriptions, though some detail is duplicated.

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 and resource: "Grounded multi-source research across Pipeworx's 1461 STRUCTURED data sources..." and explicitly notes "this is NOT open-web search." It clearly distinguishes itself from sibling tools by contrasting with ask_pipeworx and describing the decomposition/parallel routing behavior.

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

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

The description gives explicit when-to-use guidance: "Best for broad/multi-part questions over structured data" and for single lookups says "use ask_pipeworx." It also provides exclusions for breaking/current-news topics and alternative guidance for unsigned-in users, making this exceptionally actionable.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds behavior beyond annotations, such as returning top-N tools with full schemas ready to call, no second lookup needed. 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 informative but slightly verbose. It front-loads the purpose and provides examples, but could be shortened without losing value. Still, it is well-structured and each sentence contributes.

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

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

Despite no output schema, the description fully explains the return format (top-N tools with schemas) and usage context. It is complete for a discovery tool with rich annotations and sibling context.

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

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

Schema description coverage is 100%, including examples and aliases. The description does not add meaning beyond the schema; it restates the purpose of the query parameter. 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 'Find tools by describing the data or task,' using a specific verb and resource. It provides concrete examples of tasks (e.g., SEC filings, FDA drugs) and distinguishes from sibling tools like deep_research or recall by emphasizing discovery and browsing.

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

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

Explicitly advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This tells the agent when to use it and implies when not to use it (when a specific answer is needed).

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

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds context: data sources (SEC, XBRL, USPTO, news, GLEIF), return fields, and a note about the USPTO API sunset causing soft-fails. This goes beyond annotations without contradiction.

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

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

The description is verbose with multiple examples and bulleted return details. It does front-load key information but contains some redundancy and could be more compact.

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

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

No output schema exists, so the description must compensate. It details the return structure (CIK, filings with URIs, fundamentals, patents, news, LEI) and notes limitations (patent API sunset). This is sufficient for a complex tool with multiple data sources.

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

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

Schema description coverage is 100%, and the description adds substantial value: it clarifies that only 'company' is supported for the type parameter and that value expects a ticker or CIK (not names), directing users to resolve_entity for names. This meaningfully extends the schema.

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

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

The description explicitly states the tool provides a 'full cross-source profile of a US public company in ONE parallel call' and includes example queries like 'Tell me about X' and 'company profile for Microsoft'. It distinguishes from siblings by noting that names require resolve_entity first and compares favorably to chaining single-source lookups.

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

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

Clear guidance is given: always prefer this tool for holistic views, and use resolve_entity if only a name is available. It does not explicitly exclude other scenarios, but the context is sufficient for typical use cases.

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

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

Annotations already declare destructiveHint=true and idempotentHint=true. The description adds behavioral context (clearing sensitive data) without contradicting annotations. It could mention the idempotent nature but that is already implied 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?

Extremely concise with two sentences: the first delivers the core action, the second provides usage guidance. No wasted words.

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

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

Given the tool's simplicity (1 parameter, no output schema), the description fully covers purpose, usage, and paired tools with 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?

The schema covers the single parameter 'key' with full description. The main description does not add further detail beyond what the schema provides, so baseline score 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 clearly states the action (Delete), the resource (memory), and the mechanism (by key). It distinguishes from siblings like 'remember' and 'recall' by focusing on 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 states when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. Also mentions pairing with 'remember' and 'recall', providing clear context and 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?

The description discloses fetching the page, extracting title/description/key links, and emitting standard markdown. This aligns with annotations (readOnlyHint, idempotentHint) and adds concrete behavioral details.

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

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

The description is a single paragraph of four sentences, front-loading the main action. It is clear and efficient, though could be slightly more concise.

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

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

Given the simplicity (2 params, no output schema, strong annotations), the description covers purpose, use cases, and output format. It lacks details on error handling or accessibility but is adequate for the tool's complexity.

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

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

Schema coverage is 100%, both parameters are described in the schema. The description adds a concrete example for 'url' but does not elaborately extend parameter understanding beyond the schema.

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

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

The description clearly states the tool generates an llms.txt file for a URL, specifying the verb and resource. It mentions key capabilities (fetch, extract, emit) but does not explicitly differentiate from sibling tools like ai_visibility_check or scan_competitor_ai_presence.

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

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

The description lists three use cases (client indexing, own project drafting, competitor auditing) which provide context. However, it does not mention when not to use the tool or compare with alternatives.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds useful context about default behavior (only active subscriptions) and returned fields, but does not contradict annotations.

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

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

The description is two sentences, front-loaded with the core purpose and immediately followed by a usage directive. Every word adds value; no 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?

While there is no output schema, the description enumerates the return fields (id, type, etc.), making the tool's output predictable for a simple list operation. The single optional parameter is well-documented.

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 the parameter include_inactive fully described. The description does not add extra parameter details, 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 'List the caller's active subscriptions' and specifies the returned fields (id, type, etc.). This action is distinct from sibling tools like 'subscribe' and 'unsubscribe'.

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

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

The description provides explicit guidance on when to use the tool: 'to review what you're monitoring before adding more or to find an id to cancel.' This helps the agent decide between this and alternative subscription 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 indicate readOnly, openWorld, idempotent, non-destructive. The description adds useful behavioral details: search results may combine similarly named legal entities and matching is case-insensitive. This goes beyond annotations, though it does not cover pagination or result limit behavior.

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

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

Two sentences: first clearly states purpose, second adds critical matching details. No unnecessary words, information is front-loaded and directly useful.

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

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

Given the tool's complexity (6 parameters, multiple sibling tools), the description lacks details on parameters like payment_type, minimum_amount, and pagination. It also does not help differentiate when to use this vs other open_payments tools. The output schema is present but the description should still contextualize return values.

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%, so description must carry the burden. It explains only the 'company' parameter behavior (case-insensitive substring, combining entities). Other parameters (year, limit, offset, payment_type, minimum_amount) are not described. The description does not fully compensate for the lack of 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?

Description clearly states the action ('Search payments reported by a manufacturer or GPO') and scope ('in one CMS Open Payments program year'). It distinguishes from sibling tools like open_payments_physician or open_payments_product by focusing on payments by company rather than recipient or product.

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 partial guidance on matching behavior (case-insensitive substring, combining similar entities) but does not explicitly state when to use this tool versus alternatives such as open_payments_search or open_payments_company_history. No comparative usage advice.

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 critical behavioral traits beyond annotations: annual counts are authoritative, dollar figures cover only a bounded sample, and company aliases may split or combine legal entities. Annotations (readOnlyHint, openWorldHint, etc.) are consistent and do not contradict.

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

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

The single-sentence description is concise, front-loads the action and input, and includes essential caveats (authoritative counts, sampled dollars, alias splitting) without excess verbiage.

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 purpose, sample behavior, and alias handling. With an output schema present, missing return value explanations are acceptable. However, parameters 'payment_type' and 'sample_per_year' are not addressed, leaving a gap in completeness for a 5-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 description coverage is 0%, but the description only implicitly explains 'company' (substring) and 'from_year/to_year' (cross-year). The parameters 'payment_type' (enum with values) and 'sample_per_year' are not explained. The mention of 'bounded sample' hints at sampling but does not specify the 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 builds a 'cross-year CMS Open Payments history for a company-name substring', specifying the action (build history), data source (CMS Open Payments), and key input (company-name substring). It distinguishes from siblings like open_payments_company (likely single year) and open_payments_compare_companies by emphasizing the cross-year aspect.

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

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

The description implies usage for multi-year historical queries via 'cross-year', but does not explicitly state when to use this tool over siblings (e.g., open_payments_company for single year). No exclusions or alternative conditions are provided.

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

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

Annotations already declare readOnlyHint and idempotentHint, so the description adds value by disclosing the sampling behavior and the condition under which complete spend is compared. This is beyond what annotations provide and is important for correct invocation.

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

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

The description is extremely concise—two sentences with no wasted words. It front-loads the core purpose and adds a critical limitation, making it easy to parse.

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

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

Given the tool has an output schema and 5 parameters, the description covers the main behavior and a key limitation. However, it omits details about optional parameters like payment_type, which could be relevant for filtering. Still, it is fairly complete for a tool with rich structured metadata.

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 should compensate but only provides high-level context for companies and years. It does not explain parameters like payment_type or sample_per_year. Baseline 3 is appropriate given the partial 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 that the tool compares 2-5 company-name queries across CMS Open Payments program years using match counts and bounded samples. This distinguishes it from sibling tools that focus on single entities or other breakdowns.

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

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

The description provides a key caveat about sample size limiting completeness, but it does not explicitly guide when to use this tool versus alternatives like open_payments_company or open_payments_search. Usage context is implied but not fully 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 declare readOnlyHint=true; description adds that counts/dollars are sample statistics, not authoritative, providing useful behavioral context.

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

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

Two sentences, no extraneous words, efficient information delivery.

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 6 parameters and existing output schema, description gives basic function but doesn't fully clarify parameter combinations or constraints.

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

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

Schema coverage 0%, description provides context (recipient/company/product, years, sample) but lacks detailed parameter explanations.

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

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

Description uses specific verb 'break down' and resource 'bounded payment sample', clearly distinguishing from sibling open_payments_* 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 implies use for breakdown by nature of payment but offers no explicit comparison to siblings or when-not-to-use scenarios.

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

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

Beyond the annotations indicating a safe read operation, the description explains that the tool returns a bounded payment sample and includes a disclaimer that reported relationships do not imply misconduct, providing valuable behavioral context.

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

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

The description is concise with two sentences: the first states the main function and result, the second provides usage guidance. 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 simplicity, the description covers important context: bounded sample, disclaimer, and alternative tools. The output schema exists, so return values need not be detailed. Annotations and schema together provide sufficient completeness.

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

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

Schema coverage is 75% with three of four parameters described. The description does not add parameter-level details beyond what the schema provides; it mentions 'exact NPI' and 'one program year' but does not elaborate on payment_type.

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 CMS Open Payments records by exact NPI for one program year. It distinguishes itself from the sibling tool open_payments_search, which is used for name-based searches.

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

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

The description explicitly states when to use the tool (when exact NPI is available) and provides clear alternatives for name-based lookup: resolve via NPPES or use open_payments_search.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description doesn't need to repeat those. The description adds valuable behavioral context: product association is reporting-entity supplied and not proof of exclusivity, and it searches across all five CMS product slots, which extends 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 two sentences with no redundancy. The first sentence conveys the core purpose and scope, and the second adds a crucial caveat. Every phrase earns its place.

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

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

Given the tool has 5 parameters, no schema descriptions, and an output schema exists, the description covers the main search function but omits details like accepted year ranges, pagination behavior, or the meaning of CMS product slots. It is adequate but not thorough for a tool with low schema coverage.

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%, so the description must compensate. It only explains the 'product' parameter by listing example types (drug, biologic, device, medical supply). The other four parameters (year, limit, offset, company) are not addressed, leaving a significant gap in semantic meaning for the 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 clearly states the tool searches general-payment records for a named drug, biologic, device, or medical supply across all five CMS product slots. The verb 'search' and resource 'general-payment records' are specific, and the scope distinguishes it from sibling tools like open_payments_company or open_payments_physician.

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 payments linked to a product) and includes a caveat about association not proving exclusivity. However, it lacks explicit guidance on when not to use it or direct comparison to sibling tools, though naming implies product focus.

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

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

The description discloses that product association is reporter-supplied and sample dollar amounts are not complete annual totals. This adds behavioral context beyond the annotations (readOnlyHint, etc.), alerting the agent to data reliability issues.

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 function. Every sentence adds value without waste. The second sentence provides crucial caveats.

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 gives a high-level understanding but lacks details on parameter constraints (e.g., inclusive years, sample_per_year behavior, output format). The presence of an output schema mitigates some completeness concerns, but for a 5-parameter tool, more context is needed.

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

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

With 0% schema description coverage, the description must compensate. It explains 'product' as a name substring and implies year range parameters, but it does not explain the 'company' or 'sample_per_year' parameters. The meaning of sample_per_year is only loosely hinted.

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 builds a cross-year payment history for a product name substring. It distinguishes itself from siblings by focusing on product history across years. The added detail about reporter-supplied association adds specificity.

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 product history across years but does not explicitly contrast with alternatives like 'open_payments_product' or 'open_payments_company_history'. No when-not or usage exclusions are provided.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds critical behavioral context: the tool returns bounded samples and explicitly avoids full-dataset totals. No contradictions with annotations.

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

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

Two sentences with no filler. The purpose is stated first, followed by essential output behavior. Every sentence adds value.

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

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

Given the tool has 5 parameters, rich annotations, and an output schema, the description covers the core purpose and key behavioral nuance (sampling caveat). It could mention what happens on invalid NPI or no data, but the output schema likely handles return format.

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 very low (20%, only sample_per_year described). The description adds context for 'exact recipient NPI' and 'cross-year' (implying from_year/to_year) but does not explain npi format, payment_type enum meanings, or bounds. It partially compensates but leaves gaps.

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 ('build') and resource ('cross-year CMS Open Payments history'), and clearly distinguishes the tool from siblings by focusing on exact recipient NPI and cross-year scope. It also states key output characteristics (annual match counts bounded samples) with a caveat about dollar totals.

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 when to use (for exact NPI cross-year history) and warns about output limitations (sampled totals not full). However, it does not explicitly state when not to use or mention alternatives among the many sibling payment tools.

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

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

Adds value beyond annotations by clarifying that amounts 'may represent funding routed through an institution and should not automatically be attributed as personal compensation'. Annotations already indicate read-only, idempotent, non-destructive behavior.

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

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

Two concise sentences, front-loaded with purpose, no redundant or extra 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?

The description covers key behavioral nuance (attribution warning) and main filter options. However, it does not mention pagination or that no parameters are required (year implied but not explicit). Output schema exists, partially mitigating the need for return value 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?

With only 13% schema description coverage, the description compensates poorly; it names only 4 of 8 parameters (company, recipient NPI, study, state) and omits year, limit, offset, minimum_amount. The example in schema helps but the description text should provide more detail.

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

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

The description explicitly states 'Search CMS research-payment records' with specific filter fields (company, recipient NPI, study name, or state), clearly distinguishing this from sibling tools like open_payments_search or open_payments_company.

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 the listed filters and 'for one program year', but does not provide explicit when-to-use or when-not-to-use guidance relative to sibling tools or any 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 (readOnly, openWorld, idempotent, destructive=false), the description reveals a critical behavioral limitation: the 14M-row aggregation times out, so 'which manufacturer paid the most' is not computable live. It also contextualizes the data as company-reported, implying no judgment about wrongdoing or conflict.

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

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

The description is front-loaded with purpose and includes a clear capability limit and alternative. It is a bit verbose with promotional opening ('THE source for') and keyword aliases, but each section earns its place and the structure is logical.

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 12-parameter tool with an output schema and rich annotations, the description covers purpose, filter types, known limitations, alternative tools, and data interpretation. It leaves no critical gaps for an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, so the baseline is 3. The description mentions filter categories (recipient, company, product, geography, payment-nature, amount) which roughly map to parameters, but it does not add per-parameter meaning beyond what the schema already provides. No additional semantic enrichment is present.

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 searches one CMS Open Payments dataset with specific filter types (recipient, company, product, geography, payment-nature, amount). It distinguishes itself from siblings by explicitly naming a capability limitation and pointing to open_payments_company as the alternative for ranking queries.

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

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

The description gives explicit when-not guidance: it cannot rank all manufacturers by total spend, and directs users to open_payments_company instead. It also frames the tool as appropriate for structured lookups and filters, which clarifies when to use it over other Open Payments 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 provide no safety hints (all false), so the description carries the full burden and exceeds it. It discloses rate limiting ('5 per identifier per day'), the claim_token lifecycle, that the team reads digests daily, and that it's free and doesn't count against quota—valuable operational context.

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

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

The description is well-structured and front-loaded with the core purpose, but it is long and could be tightened. Every sentence earns its place, yet some clauses (e.g., 'the team reads digests daily and signal directly affects roadmap') could be shortened without losing meaning.

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 (nested context object, no output schema, nuanced usage), the description is remarkably complete. It covers purpose, when to use, exclusions, message guidelines, claim_token behavior, rate limits, and cost implications—all without requiring additional clarification.

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 crucial parameter usage: how to use claim_token to read later replies, and what to put in message ('Describe the issue in terms of Pipeworx tools/packs—don't paste the end-user's prompt'). This goes beyond the schema's field descriptions.

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

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

The description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It enumerates specific feedback categories (bug, feature, data_gap, praise) and explicitly distinguishes it from tools from other MCP servers, making its scope unambiguous.

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

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

The description gives explicit when-to-use guidance ('Use when a tool returns wrong/stale data...'), when-not-to-use ('if the tool came from a different MCP server... file it with that server instead'), and even offers a disambiguation heuristic ('Not sure? Pipeworx tool names are the ones this connection lists').

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

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

Beyond annotations (readOnly, idempotent, etc.), the description reveals key behaviors: self-aggregating from CF analytics-engine, no PII, caching duration (5min-1h), and data structure (pack, tool, count). 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?

Five sentences, front-loaded with a clear headline ('What other AI agents are calling on Pipeworx right now'), followed by specific outputs and a bullet-style list of use cases. No wasted words.

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

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

For a simple read-only tool with one optional parameter and no output schema, the description covers everything: purpose, returned data, data source, privacy, caching, and use cases. Annotations cover safety. 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?

Only parameter 'window' is fully described in schema (100% coverage). The main description reinforces semantics by linking window duration to use case ('shorter windows surface what's hot right now; longer windows show steady-state demand'), adding value beyond the schema enum.

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

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

The description clearly states the tool returns 'top tools, top packs, and total call volume' over configurable windows (24h, 7d, 30d) with the specific verb 'returns' and resource. It distinguishes itself from sibling tools like ask_pipeworx or discover_tools by focusing on trending/aggregate data.

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

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

Explicitly lists three concrete use cases (discovering hot sources, confirming canonical choices, aligning use cases). Provides context on window selection. Lacks explicit when-not-to-use or comparisons with alternatives like discover_tools, but the use cases are strong.

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

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

Beyond annotations (readOnly, idempotent, non-destructive), the description discloses critical behavioral traits: fill check against live CLOB depth, semantic anchor for cross-event pairs, partition filter for placeholder slugs, and when signals are not executable. This exceeds 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?

The description is lengthy but well-structured: begins with core purpose, then breaks down modes. Each sentence adds value (e.g., semantic anchor, fill check). Slightly verbose but efficient for the complexity.

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

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

No output schema is provided, but the description details the response structure (opportunities array, partition_check object) and covers edge cases (similarity filter, placeholder filtering, fill check). It is fully self-contained for an agent to understand inputs, behavior, 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% with descriptions, so baseline is 3. The description adds meaning by explaining how each parameter drives different analysis flows (e.g., event slug walks child markets, topic searches related events), and provides examples. This addition earns 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 finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) with specific actions, making the purpose unambiguous.

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

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

The description provides explicit guidance on when to use each mode: no args for trending scan, event for specific markets, topic for cross-event scanning. It also recommends alternatives like polymarket_fill_risk for custom sizing, and explains situations to avoid (e.g., missed fills).

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 detailed behavioral traits beyond annotations: caching (1h at KV level), internal model logic (FRED log-returns, GDELT ratio), diagnostics when segments are empty, and handling of Fed bets. This exceeds what annotations (readOnlyHint, idempotentHint) convey, providing rich context for agent decision-making.

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-organized with clear sections and front-loaded purpose. Every sentence adds value, though it could be slightly more compact for an AI agent. The structured breakdown of model families and filters aids readability.

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

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

Given the absence of an output schema, the description fully compensates by detailing the response structure (by_segment, fed_note, diagnostics), explaining edge cases (empty segments, knob effects), and covering all 9 optional parameters. It provides sufficient information for an agent to understand the output.

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

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

With 100% schema coverage, baseline is 3, but the description adds significant value by explaining each parameter's real-world trade-offs (e.g., slippage_pp notes Polymarket fees, min_kelly as bankroll percentage, category_filter syntax). It provides usage context and examples, enabling correct invocation.

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 Polymarket markets for opportunities where Pipeworx data disagrees with market price, specifically for daily betting decisions. It uses a strong verb ('scan') and specific resource ('top Polymarket markets'), and the purpose is distinct from sibling tools by focusing on arbitrage identification without paging.

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

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

The description provides a clear use case ('what should I bet on today'), but lacks explicit guidance on when not to use this tool versus siblings like polymarket_arbitrage or polymarket_edge_tracker. No exclusions or alternative tool mentions are given.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds extensive behavioral details: data source (daily snapshots), response structure (tracked[], expired[], snapshot_dates[]), computed fields (trend, decay_pp_per_day), interpretation notes (negative = SELL YES), and limitations (60-day TTL, cache-miss gaps). No contradiction with annotations.

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

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

The description is dense with information but front-loads the core question. It is slightly long but every sentence adds value. Could be improved with bullet points or clearer separation of response 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?

Despite no output schema, the description thoroughly explains the response format (tracked, expired, snapshot_dates with their sub-fields), interpretation of metrics, and limits. This fully compensates for the missing output schema in helping the agent understand what to expect.

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 parameter descriptions. The description adds context like 'default 14' and 'snapshot family' but mostly reiterates schema content. Baseline of 3 is appropriate as the description adds minimal value beyond the schema.

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

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

The description clearly states the tool's purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers a specific question about edge longevity and distinguishes itself from sibling tools like polymarket_edges by focusing on time-series analysis rather than current snapshots.

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 when to use (to understand edge persistence) and contrasts fresh vs. old edges, but does not explicitly state when not to use this tool or provide direct alternatives. The sibling list includes polymarket_edges, but the description assumes the user will infer the distinction.

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, destructiveHint, idempotentHint. Description adds behavioral context: walks the ladder, returns verdict, describes forced_directional_risk and thin_legs. No contradiction. Adds value beyond annotations.

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

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

Description is long but front-loaded with purpose. Each sentence adds value. Could be broken into clearer sections for modes, but no wasted words. Appropriate length for complexity.

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

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

Despite no output schema, description lists return fields for both modes. Covers single-market and basket modes thoroughly. Provides usage guidance and risk rationale. Complete for a risk-check 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%, baseline 3. Description adds meaning: explains default size_usd behavior for buys vs sells, side auto-detection for basket, and interprets size_usd for basket as settlement notional. Exceeds 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?

Description clearly states verb+resource: 'realizable-vs-theoretical edge check against live CLOB order-book depth'. Distinguishes single-market and basket modes with specific return fields. Differentiates from siblings by explicitly referencing polymarket_arbitrage and polymarket_edges.

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

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

Explicitly states when to use: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Provides rationale: theoretical overround on thin books is not capturable, and partial basket fills convert arb into unhedged directional position.

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, idempotentHint, and openWorldHint. The description adds substantial behavioral context: response structure, safety fields (compatibility_warning, temporal_alignment), and skipped cross-type counters. It explains when spreads are meaningless (e.g., temporal misalignment) and does not contradict any annotations.

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

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

The description is long but well-structured with section headers (TWO MODES, RESPONSE, SAFETY FIELDS) and clear bullet points. Every sentence adds useful information, though some technical details (e.g., skipped_cross_type counters) could be condensed slightly without loss of 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?

Despite no output schema, the description thoroughly documents the response fields (leg prices, top_spreads_pp, compatibility_warning, temporal_alignment, counters). For a tool with 3 optional parameters, it provides all necessary context for correct invocation and interpretation of results.

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

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

Input schema has 100% description coverage, so baseline is 3. The description adds value by providing concrete examples (e.g., 'fed', 'btc'), explaining the overriding behavior of explicit parameters, and listing the exact topic shortcuts. This enriches the schema definitions.

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

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

The description clearly states it computes cross-venue spreads between Kalshi and Polymarket, specifying two modes (topic shortcuts and explicit pairings). It distinctively positions itself among sibling tools by focusing on cross-venue arbitrage, differentiating from single-venue tools like polymarket_arbitrage.

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

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

The description provides extensive usage guidance, including two modes, example inputs, and detailed safety warnings (compatibility_warning conditions). However, it does not explicitly contrast with sibling tools or specify when to use this tool over alternatives like polymarket_arbitrage, 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 already declare readOnlyHint, destructiveHint, and idempotentHint. The description adds context about listing all keys when omitted and scoping to identifier, which is useful beyond annotations. No contradictions.

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

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

The description is two sentences with no wasted words. It front-loads the main purpose and is well-structured for quick comprehension.

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 main usage and scope. However, it does not describe return values or error cases, which would be helpful given no output schema. Still, for a simple retrieval tool with good annotations, it is largely complete.

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

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

The schema has 100% coverage for the single optional 'key' parameter, with a description that matches the tool's behavior. The description reinforces the schema but adds only a small extra context about listing behavior.

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 a saved value or lists all keys, using specific verbs 'retrieve' and 'list', and identifies the resource as previously saved context. It distinguishes 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 explains when to use the tool (to look up stored context without re-deriving) and mentions pairing with 'remember' and 'forget'. It also notes scoping to user identifier, but lacks explicit when-not-to-use guidance.

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

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

Annotations already indicate read-only, idempotent, non-destructive. The description adds behavioral details like mark_read flag affecting next call and mentions return fields (source, citation_uri, payload), going 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 paragraphs, front-loaded purpose, each sentence adds value (return fields, filters, mark_read, polling info). No unnecessary words.

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

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

No output schema, but description covers key return fields. Parameters are well-described. Annotations cover safety. Could mention pagination or limit behavior more explicitly, but adequate given complexity.

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

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

Schema coverage is 100%, so baseline 3. Description adds meaning: explains type filter, since as ISO timestamp, mark_read effect, unread_only as null read_at, thereby enriching the 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 clearly states 'Pull fired events from your subscription feed' and specifies filtering by type and since. It distinguishes itself from sibling tools like list_subscriptions by mentioning polling and an alternative endpoint for scripts/dashboards.

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 provides usage context: 'Polls work fine' and notes an alternative endpoint for scripts/dashboards. However, it lacks explicit when-not or comparison to other similar 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 goes far beyond the annotations by detailing the parallel fan-out architecture, the fallback chain from GDELT to GNews with specific failure conditions, the USPTO soft-failure status, accepted date formats, and the structure of the return value. Annotations already indicate read-only and idempotent behavior, but the description adds rich behavioral context.

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

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

The description is a single efficient paragraph with no wasted words. Every sentence adds critical information: usage examples, data sources, parameter guidance, return structure, and sibling differentiation. It is as concise as possible given the complexity of the tool.

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

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

Despite having no output schema, the description fully covers the return format (structured changes grouped by source, total_changes count, citation URIs). It addresses parameter nuances, fallback behavior, and a notable limitation (USPTO soft-fail). It also explicitly differentiates from a sibling tool, making the decision boundary clear.

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

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

All three parameters are documented in the schema with adequate descriptions. The tool description reinforces these by showing example values for `value`, explaining accepted formats and typical usage for `since`, and clarifying the limited enum for `type`. This adds modest extra value beyond the schema.

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

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

The description begins with concrete example queries, then clearly states it provides a change feed for a company in a time window. It enumerates the specific data sources (SEC EDGAR, GDELT→GNews, USPTO) and contrasts with the sibling tool entity_profile, making the 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 description gives explicit when-to-use scenarios (e.g., 'What's new with X') and directly advises using entity_profile when a static profile is needed instead. It also provides practical guidance for the `since` parameter ('Use "30d" or "1m" for typical monitoring'). However, it does not enumerate situations where the tool should be avoided beyond that sibling distinction.

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 scoping by identifier, persistence duration (persistent for auth, 24h for anonymous), and is idempotent. Adds significant detail beyond annotations, which already indicate idempotency and non-destructiveness.

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 focused sentences, front-loaded with purpose. The second sentence is somewhat lengthy but packs useful examples. No fluff.

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

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

Complete for a simple two-parameter tool: explains purpose, usage, storage details, and lifecycle with complementary tools. No output schema needed.

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

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

Schema descriptions already cover both parameters (100% coverage). Description adds real-world examples for key (e.g., 'subject_property') and clarifies value as 'any text', enhancing understanding.

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

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

The description clearly states the tool saves data for later reuse across conversations or sessions, with concrete examples like tickers, addresses, and preferences. It distinguishes itself from sibling tools recall and forget by being the write operation.

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

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

Explicitly says when to use ('discover something worth carrying forward') and pairs with recall/forget for full lifecycle. However, it does not explicitly state when not to use it, though the guidance is strong enough.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description goes well beyond these by disclosing internal cascading behavior ('Each call cascades through several lookup endpoints internally'), graceful degradation ('LEI/FIGI enrichment degrades gracefully'), explicit unresolved identifiers ('stated explicitly under `unresolved` rather than omitted'), and source labeling for each identifier. This gives the agent a rich behavioral model without contradicting annotations.

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

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

The description is long but dense with useful information. It front-loads example queries and the core purpose, then details supported types and edge cases. Every sentence adds value, though the text is a single dense paragraph with semicolons that could be more readable as bullet points. For the tool's complexity, this level of detail is justified, so it earns a 4.

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 there is no output schema, the description needs to explain what the tool returns, and it does: canonical identifiers, source labels, unresolved list, and a citation for drug lookups. It covers input handling, supported types, degradation behavior, and the tool's efficiency. This is complete for a complex tool with no output schema and rich annotations.

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

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

The schema provides basic descriptions (type enum and value examples like 'AAPL' or 'ozempic'), but the description adds substantial semantics: for company type, it explains the cross-source identity spine including CIK, LEI, FIGI, and the ISIN-to-LEI mapping for non-US issuers; for drug, it specifies returns of RxCUI + ingredient + brand. It also clarifies input variations (ticker, CIK, ISIN, or name). This far exceeds the schema's information.

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: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It gives concrete example queries ('What's the ticker for…', 'who owns X'), making the action and resource unmistakable. It distinguishes itself from siblings like entity_profile and compare_entities by focusing on name-to-ID resolution.

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

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

Explicit guidance is provided: 'Use FIRST whenever you have a name but need an ID.' This clearly indicates the primary use case. It also explains that it replaces 2-3 manual lookups. While it does not explicitly name alternative tools or state when NOT to use it, the 'FIRST' directive and emphasis on needing an ID imply that other tools are used when IDs are already known. This is strong but not fully exhaustive.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true. The description adds meaningful behavioral context: it probes each entity with ai_visibility_check, returns a ranked list with score, confidence, and signal density. No contradictions.

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

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

Three efficient sentences: first states core action, second explains the underlying mechanism, third provides usage context and return value. Every sentence earns its place with no redundancy.

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

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

Given 4 parameters with full schema coverage and no output schema, the description adequately covers the return format (ranked list with score, confidence, signal density) and explains the models parameter. Could mention result ordering or error handling, but sufficient for a well-annotated 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 100% (baseline 3). The description adds value beyond schema by explaining that the first entity in the array is treated as the 'subject' for narrative, and the context parameter disambiguates common names.

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

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

The description explicitly states it compares AI visibility across multiple entities using ai_visibility_check, ranks by score, and surfaces most/least recognized. It distinguishes from the single-entity sibling ai_visibility_check and the general compare_entities.

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

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

The description provides a clear use case (competitive AI-marketing audits) and an example question. It implies when to use (multiple entities) but does not explicitly state when not to use it or mention alternatives, though the sibling tool ai_visibility_check is available for single entities.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds valuable context: partial failure graceful degradation, bundlephobia's first measurement can take 5-30 seconds, and sources_failed field. This goes beyond annotations.

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

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

Single paragraph but efficiently packed: purpose, usage, behavior, constraints, and output summary. Each sentence provides unique information. Could be slightly more structured (e.g., separate lines for output fields) but not overly verbose.

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

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

Despite no output schema, description thoroughly explains return values (summary block fields, advisories, links, alternative versions), failure modes, ecosystem scope, and timeout behavior. Complete for a composite tool with two data sources.

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

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

Schema coverage is 100% with clear descriptions for both parameters. Description adds that scoped packages are accepted (already in schema) and version defaults to latest (also in schema). Minimal extra value; 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?

Description clearly states it's a composite check for npm package suitability, combining deps.dev and bundlephobia data. It distinguishes from siblings by specifying it's for npm ecosystem and explicitly mentions alternatives for other package managers (PyPI, Maven, etc.).

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

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

Explicitly says when to use: when an agent asks about safety, popularity, size, or cost of adding an npm package. Also clearly states what it does not cover (other ecosystems) and refers to deps.dev:version directly for those, providing clear exclusion guidance.

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

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

Beyond annotations (readOnlyHint, etc.), the description discloses the embedding model (BGE-base-en), chunking strategy (500-char overlapping windows), character cap (200K chars), and truncation behavior (flagged).

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

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

Five well-organized sentences, front-loaded with core purpose, no redundancy, every sentence contributes unique 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 explains return format (top-N passages with character offsets and similarity scores) and mentions pairing with another tool, making it self-sufficient 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?

With 100% schema coverage, the description adds value by providing concrete examples of text sources (SEC 10-K, article) and query examples ('supply-chain risk'), making parameter usage clearer than schema alone.

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 'Semantic search INSIDE a fetched record' with specific examples and distinguishes from siblings by mentioning it pairs with ask_pipeworx_grounded.

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 to use when the record is too large for the prompt, and recommends pairing with ask_pipeworx_grounded for grounding over relevant passages instead of the whole document.

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

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

The description adds substantial behavioral context beyond annotations, including OAuth requirements, return of subscription id, type-specific parameters, delivery constraints (SMS cap, webhook signing, auto-disable after 10 failures), and verification requirements. 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 dense but front-loaded with the primary purpose and return value. It could benefit from bullet points for readability, but it is efficient and every sentence adds value.

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

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

Given 3 parameters, 100% schema coverage, and annotations, the description is thorough. It covers all subscription types, parameter details, delivery options, prerequisites, and edge cases (e.g., phone verification, SMS cap, webhook auto-disable). The only missing element is an explicit output schema, but the description states the return value.

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 detailed descriptions for each parameter. The description adds extra context with examples and constraints (e.g., SMS cap, webhook behavior), enriching the schema but not essential since the schema already covers meaning.

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

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

The description clearly states the tool creates a proactive monitoring subscription and returns the new subscription id. It uses a specific verb ('Create') and resource ('proactive monitoring subscription'), distinguishing it from siblings like 'list_subscriptions' and 'unsubscribe'.

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

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

The description explains when to use the tool (to create subscriptions) and notes prerequisites (Pipeworx OAuth account). It lists supported types and delivery channels, providing context, but does not explicitly state when not to use or offer alternatives.

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

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

Annotations already mark it as read-only, open-world, idempotent, and non-destructive. The description adds behavioral context: it returns example questions and can be called with or without arguments, complementing the annotations without contradiction.

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

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

The description is informative but slightly verbose; however, it is well-structured with a clear purpose and enumeration of topics. It effectively front-loads the main intent.

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 explains the return type (category-bucketed example questions with tool and argument shape) and the effect of the optional parameter, making it self-sufficient for an AI agent.

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

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

Schema coverage is 100%, and the description adds meaningful context for the optional `topic` parameter, listing valid focus areas (e.g., 'finance', 'pharma') and explaining the effect of omitting it. This goes beyond the schema description.

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

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

The description clearly states the tool returns category-bucketed example questions with exact tool and argument shapes. It differentiates itself as the onboarding entry point, distinguishing from siblings like discover_tools and the ask_pipeworx family.

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

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

Explicitly advises using this tool 'FIRST when you do not yet know what Pipeworx can do.' It provides clear context for when to use it, but does not explicitly state when not to use it or name alternatives like discover_tools for tool discovery.

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 mutation (readOnlyHint=false) and non-destructive (destructiveHint=false). Description adds ownership enforcement and deactivation behavior, enriching beyond annotations.

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

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

Three sentences, all essential. Front-loaded with action and resource. No 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?

For a single-parameter tool with no output schema, the description fully covers purpose, ownership, and side effects. Sufficient for agent invocation.

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

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

Schema covers parameter 'id' with description. The tool description mentions 'by id' but adds no new parameter meaning beyond schema. Baseline 3 due to high schema coverage.

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

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

Description clearly states 'Cancel a subscription by id' with specific verb and resource. Distinguishes from sibling tools like 'subscribe' and 'list_subscriptions'.

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

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

Explicitly mentions ownership enforcement and that the row is deactivated not deleted, providing clear context on when and how to use. Could mention alternatives or when not to use, but not required.

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 establish read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond those annotations: it explains the could_not_verify vs. unsupported distinction, warns that could_not_verify is not evidence, and describes the return structure (verdict, actual value, citation, reasoning). This is significant additional transparency without contradicting 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 fairly long but well-structured: it opens with natural-language examples, states the purpose, outlines two processing paths, lists return values, highlights an important error-handling caveat, and concludes with the efficiency benefit. Every sentence carries meaningful information, though it could be slightly tightened without losing substance.

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 there is no output schema, the description fully covers what the tool returns (verdicts, actual value, citation, reasoning) and explains error semantics (could_not_verify vs. unsupported). It also describes the underlying pipelines (SEC EDGAR/XBRL vs. grounded) and the scope of claims, providing a complete picture 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?

While the input schema already describes both parameters with 100% coverage, the description enhances understanding of tolerance_pct by explaining its default behavior ('implied by wording, capped at 5'), its override function, and a concrete use case (set 1–2 for hallucination detection). This adds clear operational guidance beyond the schema.

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

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

The description clearly states the tool's purpose: natural-language claim verification against authoritative sources. It is distinguished from siblings by focusing on fact-checking with a verdict output and mentions replacing multiple sequential calls, making its role 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 description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear invocation context. It also details two distinct processing paths (company-financial vs. any other claim), but it does not explicitly exclude or compare to alternatives like ask_pipeworx_grounded, so it lacks explicit when-not-to-use guidance.

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