nhtsa

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. Description adds that it makes calls to LLMs (Workers AI free, Anthropic byo key) and returns per-model results with raw_response. Does not detail rate limits or cost implications beyond Anthropic, but sufficient given annotation coverage.

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

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

Single paragraph of four sentences, front-loaded with purpose, then details on models and return format, then use cases. 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 outlines return structure (score, confidence, signals, raw_response, combined view). Input parameters fully documented. Could mention that it makes external API calls, but implicit. Comprehensive for the tool's moderate 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 already covers all four parameters with 100% description coverage. Description adds value by specifying default model (Workers AI) and how _apiKey is passed through to Anthropic, which is not in schema. Baseline 3, uplifted for additional context.

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

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

Description clearly states it probes LLMs for knowledge about an entity and returns visibility scores (0-100) per model. It lists specific use cases like AI-marketing audits and brand checks, and distinguishes from sibling tools like ask_pipeworx which are for asking specific questions.

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

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

Description provides context on when to use: pre-launch brand checks, competitive monitoring. It explains default model and when to provide _apiKey for Anthropic. However, it does not explicitly exclude alternative tools or scenarios where this tool is not appropriate.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: Pipeworx picks the right tool, fills arguments, and returns results. However, it lacks details on limitations (e.g., rate limits, data source availability, error handling) or response format. The description adds value but is not comprehensive for 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by explanatory details and examples. Every sentence earns its place by clarifying functionality or providing concrete use cases, with zero wasted text.

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

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

Given the tool's complexity (natural language querying with backend tool selection) and no output schema, the description is mostly complete: it explains the process and provides examples. However, it lacks details on output format or potential errors, which could be helpful for an AI agent. With no annotations, it compensates well but has minor gaps.

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

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

Schema description coverage is 100%, so the schema already documents the single parameter. The description adds meaning by specifying that the question should be in 'plain English' or 'natural language,' and provides examples that illustrate expected input format, which goes beyond the schema's basic 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's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer from data source'), and distinguishes from siblings by emphasizing natural language querying without needing to browse tools or learn schemas. The examples further clarify the scope.

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

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

The description provides clear context for when to use this tool: for asking questions in plain English to get answers from data sources, without needing to browse tools or learn schemas. It implies an alternative approach (using other tools directly) but does not explicitly name when-not-to-use cases or specific sibling tool 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 indicate readOnlyHint, openWorldHint, and not destructive. The description adds substantial behavioral context: it resolves the market, classifies the bet, fans out to appropriate packs, and returns evidence plus comparison. This goes well beyond what annotations provide.

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

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

The description is concise yet thorough, with no wasted words. Each sentence adds critical information: purpose, inputs, internal behavior, output, and use cases. Front-loaded with the core action, then details.

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

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

Despite no output schema, the description clearly describes the output (evidence packet + comparison). Two parameters with 100% schema coverage. The tool's complexity (market resolution, classification, fan-out) is fully explained, making it complete for an agent to invoke correctly.

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

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

Schema coverage is 100%. The description adds meaning by explaining how the tool internally fans out to packs based on classification, and clarifies that depth controls the number of evidence sources. This adds value beyond the schema's enum and type 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 researches a Polymarket bet by pulling Pipeworx data in one call. It specifies inputs (slug, URL, question text) and outputs (evidence packet, comparison). Distinguishes from sibling tools by focusing on Polymarket bets with a single-call convenience.

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

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

Provides explicit use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. While no direct alternatives are named, the context makes it clear this is for Polymarket-specific research. Lacks explicit when-not-to-use, but the specificity sufficiently guides an agent.

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

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

With no annotations, the description carries the full burden. It discloses return format (paired data + pipeworx:// URIs) and specific data fields for each type, but omits details like rate limits, data freshness, error handling, or authentication requirements, which would be essential for full transparency.

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

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

The description is three sentences, front-loaded with the main purpose, and each sentence adds essential information without redundancy. It is highly efficient.

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

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

Given no annotations or output schema, the description covers inputs, processing, and outputs fairly well, including return structure and two use cases. It lacks only minor details like error scenarios or prerequisites, making it almost complete.

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

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

Schema coverage is 100%, and the description adds meaning beyond the schema by explaining the business context (e.g., revenue, net income from SEC EDGAR) and the purpose of the values array. It does not provide formatting details for values (e.g., ticker case), but overall adds value.

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

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

The description clearly states the tool compares 2–5 entities side by side, specifies two entity types (company and drug) with distinct data points, and highlights efficiency gains by replacing multiple sequential calls. It distinguishes itself from unrelated sibling tools.

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

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

The description explicitly says to use it when comparing multiple entities in one call and notes it replaces many sequential calls, providing clear context. However, it does not mention when not to use it or explicitly exclude alternatives among siblings, though none overlap significantly.

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?

No annotations are provided, so the description carries the full burden. It mentions the action ('Decode') and outputs, but doesn't disclose behavioral traits like error handling, rate limits, authentication needs, or whether the operation is read-only or has side effects. For a tool with zero annotation coverage, this is a significant gap.

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

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose and outputs without any wasted words. It's appropriately sized and front-loaded with the core action.

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

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

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is adequate but has clear gaps. It explains what the tool does but lacks behavioral context and usage guidelines, making it minimally viable but not fully helpful 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 description coverage is 100%, so the schema already documents the single parameter (vin) with a clear description and example. The description adds no additional parameter semantics beyond what's in the schema, meeting the baseline of 3 when schema does the heavy lifting.

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

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

The description clearly states the specific action ('Decode') and resource ('a 17-character Vehicle Identification Number'), and lists the specific outputs ('make, model, year, body style, engine, and other attributes'). It distinguishes from sibling tools (get_makes, get_models) by focusing on decoding rather than listing.

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

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

No explicit guidance on when to use this tool versus alternatives is provided. The description doesn't mention when you would decode a VIN versus using get_makes or get_models, nor does it specify prerequisites or 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'the most relevant tools with names and descriptions' and has a search function, but lacks details about behavioral traits like rate limits, authentication needs, error handling, or how relevance is determined. The description adds some context but doesn't fully compensate for the absence of 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 appropriately sized and front-loaded, with two sentences that efficiently convey purpose and usage guidelines without unnecessary details. Every sentence earns its place by providing essential information for tool selection.

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 moderate complexity (search function with 2 parameters) and no output schema, the description is reasonably complete for guiding usage. It explains the tool's role in a large catalog context and when to use it, though it could benefit from more behavioral details (e.g., response format, limitations) to fully compensate for the lack of annotations and output schema.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description mentions searching 'by describing what you need,' which aligns with the 'query' parameter but doesn't add meaningful semantic information beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and explicitly distinguishes it from siblings by emphasizing its role in finding tools among 500+ available options, unlike the sibling tools which appear to be specific data retrieval functions.

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

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

The description provides explicit guidance on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task'), including a clear condition (500+ tools) and alternative approach (using this as the initial step rather than other 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?

No annotations provided, so description carries full burden. Discloses that it is a read operation returning citation URIs, bundles many calls. Does not explicitly state no side effects, but implies safety. Good transparency, but could explicitly mention idempotency.

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 but quite packed. Front-loaded with main purpose, then details. Could be slightly more concise, but every sentence earns its place. Good structure.

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

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

With no output schema, description explains return data (SEC filings, revenue, patents, news, LEI, URIs). Does not mention pagination or rate limits, but for a single-call bundle, it's fairly complete. Could add more on error handling or size 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 covers 100% of parameters. Description adds meaning: type enum explained (only company), value parameter specifies ticker/CIK and explicitly says names not supported, recommending resolve_entity. Adds significant value beyond schema.

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

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

Description clearly states it returns a full profile of an entity across multiple packs, lists specific data types (SEC filings, revenue, patents, news, LEI), and distinguishes from sibling tools like resolve_entity and usa_recipient_profile. Verb+resource+scope are specific.

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 (company profiles, replaces 10-15 calls) and when not to (federal contracts use usa_recipient_profile). Also advises using resolve_entity for names. Provides clear alternative 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a deletion operation, which implies mutation and potential data loss, but offers no details on permissions, reversibility, error handling, or what happens if the key doesn't exist. For a destructive tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence with zero waste. It's front-loaded with the core action ('Delete') and resource, making it immediately scannable and appropriately sized for a simple tool with one parameter.

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 destructive tool with no annotations and no output schema, the description is incomplete. It lacks crucial context such as what constitutes a 'stored memory', how deletion affects the system, whether the operation is idempotent, or what the response looks like. Given the complexity of a deletion operation, more behavioral and contextual details are 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 description coverage is 100%, with the single parameter 'key' documented as 'Memory key to delete'. The description adds no additional meaning beyond this, simply restating 'by key'. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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

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

The description clearly states the action ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'recall' or 'remember', but the verb 'Delete' strongly implies a destructive operation distinct from retrieval or storage functions.

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

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

No guidance is provided on when to use this tool versus alternatives. While the description implies it's for deleting memories, there's no mention of prerequisites (e.g., whether the key must exist), consequences, or relationships to sibling tools like 'recall' (which likely retrieves memories) or 'remember' (which likely stores them).

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

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

Description adds fetch/extraction/emission details beyond annotations, which already indicate safe, non-destructive, idempotent behavior. No contradictions.

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

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

Two focused sentences with no extraneous text, efficiently communicating purpose and output.

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 explains output format and parameters are documented. Adequate for a simple generation 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%, description does not add extra semantics beyond what schema provides. 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?

Description clearly states it generates an llms.txt file for AI crawlers, specific verb and resource, and distinguishes from sibling tools like 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?

Provides concrete use cases (indexing client site, drafting own file, auditing competitor), but lacks explicit when-not-to-use or alternative tools.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data but does not mention any behavioral traits such as rate limits, authentication needs, response format, or potential errors. This leaves significant gaps in understanding how the tool behaves in practice.

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 sentence that directly states the tool's purpose without any unnecessary words. It is front-loaded with the core action and resource, making it highly concise and well-structured.

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

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

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is adequate for a basic retrieval operation. However, it lacks details on output format or behavioral context, which could be important for an agent to use it effectively, making it minimally complete but with room for improvement.

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 tool has 0 parameters, and the schema description coverage is 100%, so no parameter information is needed. The description does not add param details beyond the schema, but with no parameters, a baseline of 4 is appropriate as there is nothing to compensate for.

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 ('Retrieve') and the resource ('all vehicle makes (brands) registered with NHTSA'), making the purpose specific and unambiguous. It distinguishes itself from sibling tools like 'decode_vin' and 'get_models' by focusing on makes rather than decoding VINs or retrieving 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 implies usage for retrieving vehicle makes, but it does not explicitly state when to use this tool versus alternatives like 'get_models' or provide any exclusions. It lacks guidance on prerequisites or specific contexts, leaving usage inferred rather than clearly defined.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes a read operation ('Get all vehicle models'), which implies it is non-destructive, but it does not address potential behaviors such as error handling, rate limits, authentication needs, or the format of returned data. This leaves significant gaps for a tool with no annotation coverage.

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

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose without any redundant or unnecessary information. It is front-loaded and appropriately sized, making it easy to understand quickly.

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 moderate complexity (2 required parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and inputs but lacks details on behavioral traits, output format, or error conditions, which are important for a read operation with no structured output documentation.

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

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

The schema description coverage is 100%, with both parameters ('make' and 'year') fully documented in the input schema. The description adds no additional meaning beyond what the schema provides, such as examples or constraints, so it meets the baseline score of 3 for high schema coverage without extra value.

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

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

The description clearly states the specific action ('Get all vehicle models') and the target resource ('available for a specific make and model year'), distinguishing it from sibling tools like 'decode_vin' (VIN decoding) and 'get_makes' (retrieving makes rather than models). It uses precise verbs and identifies the exact scope of data retrieval.

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 by specifying the required inputs (make and year), but it does not explicitly state when to use this tool versus alternatives like 'get_makes' or provide any exclusions or prerequisites. The context is clear but lacks explicit guidance on tool selection.

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

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

With no annotations, the description discloses important behaviors: it sends feedback, imposes a rate limit of 5 messages per identifier per day, and includes a privacy instruction about not including the end-user prompt. This is adequate transparency for a feedback tool.

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 plus a rate-limit note. It front-loads the purpose and provides all critical information without redundancy.

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

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

Given the tool's simplicity and the richness of the schema, the description covers everything needed: purpose, usage guidance, rate limit, privacy, and parameter hints. No gaps remain.

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

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

While the schema already describes all parameters (100% coverage), the description adds practical guidance for the 'message' field: be specific, mention tools/data, and character limit. This extra context is valuable for correct use.

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: 'Send feedback to the Pipeworx team. Use for bug reports, feature requests, missing data, or praise.' It distinguishes this tool from sibling tools like ask_pipeworx and discover_tools, which handle queries and discovery.

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

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

The description provides explicit use cases and a key guideline: do not include end-user prompt verbatim. It also mentions rate limiting. However, it does not explicitly contrast with sibling tools like ask_pipeworx for general questions, which would make it more complete.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: data is self-aggregating from CF analytics-engine, no PII, cached 5min-1h depending on window. No contradiction with annotations.

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

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

Three concise sentences with no wasted words. Front-loaded with the return type, then use cases, then source/caching details. Every sentence adds value.

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

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

Given no output schema, the description adequately explains what the tool returns (top tools, top packs, total call volume) and how it's derived. The tool is simple (1 enum param), so completeness is high.

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

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

Schema coverage is 100% (1 parameter fully described). Description adds meaning beyond the schema by explaining the trade-off between shorter and longer windows ('Shorter windows surface what's hot right now; longer windows show steady-state demand'). This helps select the right value.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a window. It uses specific verbs ('returns', 'discovering', 'confirming', 'seeing') and distinguishes itself from siblings by focusing on trending/aggregate signals rather than individual entity lookups or other actions.

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 three concrete use cases (discovering hot data sources, confirming canonical tool choice, checking alignment with common use). While it doesn't explicitly state when NOT to use it, the use cases imply appropriate contexts. No mention of alternatives among siblings, but the tool's purpose is narrow 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?

The description details the tool's behavior: it walks child markets, extracts dates/thresholds, sorts them, and reports violations. It also describes the output structure, adding value beyond the readOnlyHint and openWorldHint annotations. No contradiction with annotations.

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

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

The description is a single paragraph that is concise yet comprehensive. It starts with the core purpose, explains the underlying concept, provides an example, and ends with the output format. No unnecessary words or repetition.

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

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

Given the tool's simplicity (one parameter, no output schema), the description fully covers the required context: usage, behavioral mechanics, and return value structure. It explains the arbitrage condition, the process, and the output fields, making it complete for an AI agent.

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

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

The single parameter 'event' has 100% schema coverage with a description. The tool description adds an example ('when-will-bitcoin-hit-150k') and explains how the parameter is used, providing additional meaning 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 explicitly states the tool's function: 'Find arbitrage opportunities within a Polymarket event by checking for monotonicity violations.' It specifies the resource (Polymarket event) and the action (finding arbitrage), and the explanation of monotonicity distinguishes it from sibling tools like polymarket_edges.

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

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

The description explains when to use the tool (when an event has multiple 'by date' or 'by threshold' markets) and provides a clear reasoning principle. It does not explicitly state when not to use it or name alternatives, but the context is sufficiently clear for an AI agent to decide.

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, destructiveHint=false, and openWorldHint=true, which are consistent with the description's read-only scanning and computation. The description adds behavioral details like lognormal model from FRED and live coinpaprika price, enhancing transparency beyond annotations.

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

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

The description is a single dense paragraph of 4 sentences. It is well-structured: first sentence states purpose, second explains methodology, third details steps, fourth declares output. It could be slightly more concise but is not 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, the description explains the return format: 'top N ranked by edge magnitude with suggested trade direction'. It covers the algorithm steps and external data sources. For a complex tool, it provides sufficient context.

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

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

With 100% schema description coverage, the baseline is 3. The description adds context about 'edge' and algorithm steps, but the parameter descriptions in the schema are already clear. The description does not significantly add meaning beyond the schema.

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

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

The description uses specific verbs like 'scan', 'groups', 'fetches', 'computes', 'ranks', and 'returns' to clearly state the tool's purpose. It distinguishes from siblings like 'polymarket_arbitrage' by focusing on model vs market price disagreement.

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

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

The description explicitly states the tool is 'Built for the 'what should I bet on today' question' and explains that it discovers opportunities without manual paging. It gives clear context but does not explicitly mention when not to use or list alternative sibling tools.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that the tool fetches data from two venues, computes spreads, and notes the typical price difference (2-25pp). This provides useful context 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 moderately long but well-structured, using bullet-like formatting for the two modes. Every sentence serves a purpose, though some repetition could be trimmed. It is front-loaded with the core purpose.

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

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

Given the lack of an output schema, the description adequately explains the return format (leg-by-leg prices and spreads). It covers both modes and parameter usage. It could mention prerequisites (e.g., both venues having the same question), but that is implied.

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. The description adds value by explaining the relationship between topic and explicit parameters, and how they override each other. This goes beyond the schema's individual parameter 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 computes cross-venue spreads between Kalshi and Polymarket for the same resolving question. It distinguishes itself from sibling tools by focusing on cross-venue arbitrage, and not intra-venue signals like polymarket_arbitrage or polymarket_edges.

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

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

The description explains two modes (topic for pre-mapped shortcuts, explicit tickers for custom pairings) and when each is appropriate. However, it does not explicitly state when not to use the tool or mention alternatives, though the sibling list provides context.

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

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

With no annotations provided, the description carries full burden and does well by explaining key behavioral aspects: it retrieves from storage (persistence across sessions), supports both specific retrieval and listing operations, and mentions session context. It doesn't cover error cases or performance characteristics, but provides solid 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?

Two sentences with zero waste. First sentence states core functionality with parameter guidance. Second sentence provides usage context. Every word earns its place, and the structure is front-loaded with the most important information first.

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 provides good coverage of what the tool does, when to use it, and parameter semantics. It doesn't describe return format or error behavior, but given the tool's relative simplicity and lack of annotations, it's mostly complete. The absence of output schema description keeps it from a perfect score.

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% description coverage, so baseline is 3. The description adds meaningful context beyond the schema by explaining the semantic effect of omitting the key parameter ('omit to list all keys') and connecting parameters to the tool's purpose ('retrieve context you saved earlier'). This elevates the score above baseline.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings by mentioning session context and explicitly differentiating between retrieval by key vs. listing all keys.

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 this to retrieve context you saved earlier in the session or in previous sessions' establishes the primary use case. It also specifies when to omit parameters: 'omit key' to list all keys, creating clear alternative usage patterns within the same tool.

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

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

No annotations are provided, so the description must fully disclose behavior. It explains the parallel fan-out to SEC EDGAR, GDELT, USPTO, input formats for 'since,' and the output structure (structured changes + total_changes + URIs). This is comprehensive.

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

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

The description is two sentences long, well-structured, and front-loaded with the core purpose. Every part adds information without redundancy.

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

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

Given the complexity of fanning out to multiple sources and no output schema, the description adequately explains the behavior, input formats, and output components. It could mention potential limits or pagination but is largely sufficient.

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

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

The input schema covers all three parameters (100% coverage), but the description adds valuable context: the 'since' parameter accepts ISO dates and relative formats with examples, and 'value' can be a ticker or CIK. This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It explains the fan-out behavior for company type and describes the output. It is distinct from sibling tools like 'entity_profile' or '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 explicitly provides use cases: 'Use for brief me on what happened with X or change-monitoring workflows.' It does not explicitly exclude scenarios or name alternatives, but the context is clear.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully describes important behavioral traits: the persistence characteristics ('Authenticated users get persistent memory; anonymous sessions last 24 hours') and the cross-tool nature of the memory ('across tool calls'). However, it doesn't mention potential limitations like storage capacity, key constraints, or error conditions.

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 perfectly sized with two sentences that each earn their place. The first sentence states the core functionality with examples, and the second provides crucial behavioral context about persistence. There's zero waste or redundancy, and the most important information is front-loaded.

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

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

For a 2-parameter tool with no annotations and no output schema, the description provides good contextual coverage. It explains what the tool does, when to use it, and important behavioral characteristics. The main gap is the lack of information about return values or error conditions, but given the tool's relative simplicity, the description is reasonably complete.

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

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

With 100% schema description coverage, the input schema already fully documents both parameters with good descriptions. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline score of 3. The description focuses on usage context rather than parameter details.

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 specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'forget' (remove) and 'recall' (retrieve). It provides concrete examples of what can be stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose immediately understandable.

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

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

The description provides clear context for when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but doesn't explicitly mention when not to use it or name specific alternatives. While it implies differentiation from 'recall' (retrieval) and 'forget' (deletion), it doesn't explicitly state these as alternatives for different operations.

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?

No annotations are provided, so the description carries the full burden. It discloses versioning (v1), supported entity type (company), and return fields (ticker, CIK, name, resource URIs). However, it lacks details on error handling, idempotency, or side effects.

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, front-loaded with the core purpose, and contains no redundant information. 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?

For a tool with 2 simple parameters and no output schema, the description covers purpose, inputs, output, and provides examples. It is complete enough for an agent to use correctly, though it could mention potential error responses.

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 value by providing concrete examples for the 'value' parameter (e.g., 'AAPL', '0000320193', 'Apple') and explaining the output fields, which goes beyond the schema.

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

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

The description clearly states the tool resolves entities to canonical IDs, specifies the supported type (company), and provides input examples. It distinguishes from sibling tools like 'ask_pipeworx' and 'decode_vin' by focusing on entity resolution.

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

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

The description explains the tool replaces 2-3 lookup calls and lists acceptable inputs (ticker, CIK, name). However, it does not explicitly state when not to use this tool or suggest alternatives among siblings.

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

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

Annotations already indicate a safe, read-only operation. The description adds behavioral detail: it calls ai_visibility_check internally, ranks results by score, and surfaces most/least recognized entities. 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?

Three sentences with no wasted words. The first sentence provides the core purpose, followed by details and a concrete use case. Well-front-loaded and efficient.

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

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

The description covers purpose, internal mechanism, and return value (ranked list with score, confidence, signal density). No output schema exists, so the description adequately compensates. All required parameters are addressed through schema and description.

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

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

With 100% schema description coverage, baseline is 3. The description adds value by explaining the 'entities' parameter treats the first entry as the subject, and clarifies the role of 'models' and '_apiKey'. This surpasses the baseline.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, using ai_visibility_check for each and ranking results. It distinguishes from the sibling ai_visibility_check which operates on a single entity.

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 concrete use case ('competitive AI-marketing audits') and implies when to use this comparative tool over a single-entity check. However, it does not explicitly state when not to use it or mention alternatives, but the context is clear.

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

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

With no annotations, description carries full burden. It lists return values (verdict types, citation format) and notes it replaces multiple calls. Lacks mention of rate limits or handling of out-of-domain claims.

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 with front-loaded purpose, specific domain, output details, and value proposition. 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 no output schema, description adequately explains return format and domain. It covers all essential aspects for a single-parameter tool.

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

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

Schema coverage is 100% with a well-described parameter. Description adds context on claim types and examples, providing 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?

Description clearly states action (fact-check), resource (authoritative sources), and domain (company-financial claims for US public companies). It distinguishes from sibling tools by noting it replaces 4-6 sequential agent calls.

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

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

Explicitly says it supports company-financial claims and provides examples, implying when to use. However, it does not explicitly state when not to use it or compare to sibling tools like ask_pipeworx or compare_entities.

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