Vimeo

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds that scoring is 0-100, returns per-model and combined views, and notes that Anthropic calls require BYO key and direct payment. Adds value 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?

Four well-structured sentences. Front-loaded with purpose and score range. Each sentence adds distinct value: purpose, model option, output format, 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?

Tool has 4 parameters (1 required), no output schema. Description covers all necessary aspects: entity to probe, optional model selection, API key requirement, disambiguation via context, and output structure (per-model scores + combined). Complete for a read-only probing 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 adds context: default model is Workers AI Llama-3.3-70b (free), _apiKey is optional only for anthropic, and context helps disambiguate entities. Provides meaning beyond schema 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?

Description uses specific verb 'probe' and resource 'LLMs for knowledge about an entity', clearly states it scores visibility 0-100 per model, and provides examples. Distinguishes from sibling 'scan_competitor_ai_presence' by focusing on general entity visibility rather than competitor-specific scanning.

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

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

Explicitly states use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Explains when to use the _apiKey parameter for Anthropic model. Lacks explicit when-not guidance or direct alternative comparison, but context is clear 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, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context about routing to 3,745 tools across 884 sources and returning stable pipeworx:// citation URIs. However, it omits potential latency, rate limits, or fallback behavior for unanswered queries.

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

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

The description is front-loaded with a strong usage preference statement and contains informative sentences. While somewhat lengthy, each sentence serves a purpose. Minor redundancy could be trimmed (e.g., listing all data sources), but overall it is 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 complexity and lack of output schema, the description adequately covers the query scope and result format (structured answer with URIs). It provides relevant examples but does not detail error handling or edge cases, which would improve 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 description coverage is 100% with all parameters clearly described. The description supplements the schema by providing usage examples and explaining the tool's question routing behavior. This adds value beyond the schema's 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 routes questions to authoritative data sources and returns structured answers with citations. It distinguishes from web search by specifying preference for factual data. However, it does not differentiate from the sibling tool 'ask_pipeworx_grounded', which may have a similar purpose.

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

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

The description explicitly instructs to prefer this tool over web search for factual queries and provides concrete examples. It gives clear guidance on when to use it but lacks explicit 'when not to use' scenarios or alternatives like deep_research for comprehensive research.

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

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

The description goes beyond annotations by detailing the exact process (routing, fetching, extraction), listing all possible refusal reasons, and noting the additional LLM cost. This provides high behavioral 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 efficiently structured with the core purpose first, followed by process details, refusal reasons, and usage guidance. Every sentence adds value, no redundancy.

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

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

The description fully covers the tool's behavior, input, output format, error/refusal cases, and usage context. It is contextually complete for a high-stakes grounded query 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?

With 100% schema coverage, the schema already fully documents the parameter. The description does not add additional meaning beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose as a hallucination-resistant answer mode for high-stakes reads. It explicitly distinguishes from its sibling 'ask_pipeworx' by noting the extra LLM cost and the behavior of refusing when data doesn't directly answer.

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 (high-stakes, fact-critical scenarios) and when to prefer its sibling (casual lookups). It also mentions the cost trade-off.

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 extensively details behaviors beyond annotations: resolver contract, fan-out per category, response shapes, safety mechanisms (low-confidence short-circuits, closed market handling), news field fallbacks, resolution-rule risk, and tradeability warnings. Annotations only state readOnly, idempotent, not destructive; 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 very lengthy with many sections. While structured with labeled segments (CLASSIFIERS, FAN-OUT EXAMPLES, etc.), it could be more concise for an AI agent. Important information is present, but verbosity reduces efficiency.

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 fan-outs, edge cases, no output schema), the description is remarkably complete. It covers resolver contract, parent event extraction, news fallbacks, safety blocking, wide spread indication, and resolution-rule risk. No obvious gaps remain.

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

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

Schema coverage is 100%, but the description adds significant meaning: market param explains slug/URL/text, depth param enum explained, include_raw param detailed with default and recommendation. Examples and contextual notes enhance understanding beyond schema.

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

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

The description clearly states 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call', which is a specific verb+resource. It distinguishes the tool from siblings like polymarket_edges by focusing on broad research. Examples of use cases are provided.

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

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

Explicit use cases are given: 'Use for "should I bet on X", "what does the data say about Y", "is there edge in Z"'. It also guides inspection of market_match_confidence before trusting analysis. No explicit 'when not to use' but provides context about closed markets and low confidence.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds no additional behavioral context beyond confirming a list operation. It 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 extremely concise (one sentence). For a tool with no parameters and robust annotations, this is efficient, though it could include slightly more context about what categories are listed.

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 (no parameters, output schema present), the description is minimal but sufficient. However, it does not explain what 'categories' refers to (e.g., content categories, platform categories), leaving some ambiguity.

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?

There are no parameters, and schema coverage is 100%. The description aligns with the schema by stating 'List categories', which is adequate for a parameterless tool.

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 ('List') and the resource ('categories'). It implies a plural listing, differentiating from the sibling 'category' tool which likely handles single categories. However, it does not specify the scope or type of categories.

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 like 'category' or 'category_videos'. The description does not mention context or prerequisites.

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

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

Annotations provide readOnlyHint, idempotentHint, etc., but the description adds no behavioral context beyond the structured fields. Does not mention output schema or any 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?

Extremely concise (two words) but under-specified. It sacrifices informativeness for brevity.

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

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

Despite having an output schema, the description gives no hint of what the tool returns or how to interpret results. Completely inadequate 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?

With 0% schema description coverage, the description must explain the 'slug' parameter but provides no information. The name 'slug' is not explained.

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 'Single category.' restates the name and indicates scope but lacks a verb (get, retrieve) leaving the action implied. It distinguishes from sibling 'categories' (plural) only weakly.

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

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

No guidance on when to use this tool versus siblings like 'categories' or 'category_videos'. Agent has no context to select appropriately.

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, and idempotentHint. The description adds no behavioral context (e.g., pagination behavior, response format, or rate limits).

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

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

At 4 words, the description is extremely short but sacrifices clarity and completeness. It is under-specified rather than optimally concise.

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

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

Despite an output schema being present, the description fails to explain input parameters, usage context, or any behavioral traits. The tool is not adequately defined 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 0% and the description does not explain any of the 3 parameters (slug, page, per_page). The meaning of 'slug' (likely category identifier) is missing, and no usage details for pagination are provided.

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 'Videos in a category.' is a tautology of the tool name and title. It lacks a specific verb (e.g., list, get) and does not differentiate from siblings like 'channel_videos' or 'user_videos'.

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

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

No guidance on when to use this tool versus alternatives such as 'category', 'channel_videos', or 'videos_search'. No context on prerequisites or 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 provide readOnlyHint and idempotentHint, so description is not required to repeat safety. However, description adds no extra behavioral context such as output format or side effects, leaving a 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?

Extremely under-specified; 'Channel.' is not concise but instead omits essential information. Every sentence does not earn 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?

Despite having only one parameter and an output schema, the description fails to explain what the tool returns or how it behaves. Completely inadequate for selection.

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%, and description provides no explanation for the parameter 'channel_id_or_slug'. No format, examples beyond a single value, or meaning is given.

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 is just 'Channel.' which is a tautology, restating the name without specifying action or resource scope. It does not distinguish from siblings like channel_videos or category.

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

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

No guidance on when to use this tool vs alternatives. No context for usage, 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive nature. The description adds no behavioral insights (e.g., pagination limits, sorting behavior, or response structure). It 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?

Extremely brief (two words), but this under-specification is not conciseness; it fails to convey necessary information. The description should be expanded to cover tool purpose and key parameters.

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 an output schema, the description omits critical details like pagination, sorting options, and the meaning of 'channel_id_or_slug'. For a tool with 5 parameters and many siblings, this is severely incomplete.

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

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

The input schema has 5 parameters with 0% description coverage, and the tool description provides no explanation for any parameter (e.g., 'page', 'sort', 'per_page', 'direction', 'channel_id_or_slug').

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 ('Channel videos.') is a tautology that merely restates the tool name and title. It does not specify whether the tool retrieves, lists, or manages channel videos, nor does it differentiate it from sibling tools like 'category_videos'.

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

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

No guidance on when or why to use this tool over alternatives such as 'channel', 'category_videos', or 'user_videos'. The description lacks any contextual or conditional 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?

Beyond the readOnlyHint=true and other annotations, the description adds key behavioral details: pulls LATEST 10-K data from SEC EDGAR/XBRL, handles off-calendar fiscal years, sorts results by primary metric, and returns paired data with 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 query examples and is informative throughout, but slightly verbose. Every sentence serves a purpose, though minor trimming could improve conciseness without losing clarity.

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

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

Despite no output schema, the description thoroughly covers input types, data sources, sorting behavior, and output format (paired data + citations). For a tool handling two distinct entity types with different data, this is complete enough for correct 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?

With 100% schema coverage, the description still adds value: it explains that 'company' type pulls revenue, net income, cash, long-term debt; 'drug' type pulls FAERS counts, FDA approvals, trial counts. For the values parameter, it specifies format (tickers/CIKs for company, names for drug) and constraints (2-5 items).

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 performs side-by-side comparison of 2-5 entities (companies or drugs) in one parallel call, with example queries like 'Compare X and Y' and 'X vs Y'. It clearly distinguishes itself from sequential single-entity lookups, 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 provides explicit guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It explains when to use each type (company vs drug) and highlights efficiency gains ('Replaces 8–15 sequential lookups'), leaving no ambiguity about appropriate 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?

Annotations already provide safety hints (readOnly, idempotent). Description adds context: 'never invented', explicit gaps[], account/plan needs, timing 15-60s. 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?

Concise but packed with useful info. Front-loaded with purpose. Slightly long but each 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?

Completely covers functionality, prerequisites, output format (findings packet), limitations (gaps), and timing. No output schema but description explains return values adequately for agent use.

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

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

Schema covers both params (100% coverage). Description adds depth defaults and plan requirements, and clarifies question is natural language. Adds value beyond schema.

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

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

Description clearly states 'Grounded multi-source research in ONE call' and explains decomposition and parallel routing. Distinguishes from sibling ask_pipeworx for single lookups.

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

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

Explicitly says 'Use for broad or multi-part questions' with examples, and contrasts with ask_pipeworx for single lookups. Also mentions account and plan requirements.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that it returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' This extra detail about the return format and immediacy of results goes 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 moderately sized and front-loaded with the core purpose. Every sentence contributes useful information, though the list of example domains ('SEC filings, financials...') is somewhat lengthy but still relevant.

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 presence of annotations, the description covers what the tool does, when to use it, what it returns (names, descriptions, schemas), and behavior (results ready to call). It lacks details about pagination or the exact ranking, but these are not critical for a search/discovery tool with no output schema.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining that 'query' accepts aliases like 'task', 'q', 'description', 'search' and provides concrete examples. It also clarifies the 'limit' default and max (20, max 50), which is not in 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 'Find tools by describing the data or task.' It specifies the verb-resource relationship (discover tools) and distinguishes from siblings by emphasizing that this tool is for browsing/searching available tools, not for retrieving specific data. Examples like SEC filings, financials, and FDA drugs 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 explicitly advises to 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear guidance on when to use the tool. It implies that for direct answers, other tools should be used, though it does not explicitly list 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, openWorldHint, idempotentHint, and non-destructive. The description adds significant context: it performs a parallel call across multiple sources, lists all return fields, mentions the patents API sunset (soft-fail), and notes the use of GDELT→GNews fallback. No contradiction.

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

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

The description is somewhat long but is well-structured with a front-loaded query snippet, bullet points of returned data, and clear notes on limitations. Every sentence serves a purpose given the tool's complexity, 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?

For a complex tool with no output schema, the description meticulously documents all return fields (cik, filings, fundamentals, patents, news, LEI) with details on data sources and ordering. Also covers edge cases (patents soft-fail, name resolution prerequisite).

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 good descriptions. The description adds value beyond schema by explaining that type is only 'company' currently, value must be ticker or zero-padded CIK, and that names are not supported. This clarifies limitations and alternatives.

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 purpose: 'full cross-source profile of a US public company'. It provides numerous example user queries and details what data is aggregated, clearly distinguishing it from related tools like resolve_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?

Explicit guidance: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' when a holistic view is needed. Also states 'names not supported (use resolve_entity first if you only have a name)', giving clear when-to-use and when-not-to-use instructions.

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

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

Annotations already indicate destructive (destructiveHint=true) and idempotent (idempotentHint=true) behavior. Description adds context about clearing sensitive data and stale memories, but does not fully disclose idempotency or error handling for missing keys. With annotations, the additional value is moderate.

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 the action. Every sentence adds value: first states core function, second provides usage scenarios and sibling references. 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 single-parameter destructive tool with annotations covering key behaviors, the description sufficiently explains when and why to use it. No output schema needed; return value is implied. Fully meets needs.

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

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

Schema coverage is 100% with parameter 'key' described as 'Memory key to delete'. Description repeats 'by key' but adds no new semantic detail beyond the schema. Baseline 3 is appropriate as 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 'Delete a previously stored memory by key', specifying the action and resource. It distinguishes from sibling tools 'remember' and 'recall' by mentioning pairing with them, making 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?

Provides explicit usage conditions: 'Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' Also references complementary tools (remember, recall), guiding context-aware 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?

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral details: fetches the page, extracts title/description/key links, emits standard markdown, and outputs a text blob ready for deployment. 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 plus a bullet list of use cases. Every sentence adds value: first defines action, second explains process and output. No redundant or filler content.

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

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

For a simple tool with 2 parameters, no output schema, and full annotation coverage, the description explains what it does, how it works, output format, and use cases. Could mention that the URL must be accessible, but overall complete.

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

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

Schema coverage is 100% (both parameters described). Description mentions 'Fetches the page' and 'extracts title/description/key links' which aligns with parameters but adds no new parameter-specific details beyond schema. Baseline of 3 is appropriate.

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

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

Description specifies the tool's exact action: generate a production-ready llms.txt file for any URL. It names the target format (llms.txt) and mentions the purpose (indexing by AI crawlers). This clearly distinguishes it 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?

The description lists concrete use cases (getting a client's site indexed, drafting for own project, auditing competitors) and implies when to use (when you need an llms.txt). It does not explicitly state when not to use or name alternatives, but the use cases provide sufficient guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds the default behavior (active only) and return field names, providing useful context beyond annotations. No contradictions.

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

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

Two sentences: the first states purpose and return fields, the second gives usage guidance. No wasted words. Front-loaded with essential information.

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

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

For a simple list tool with no output schema, the description covers return fields, default behavior, and usage context. All relevant information is present.

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

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

Schema coverage is 100% for the single parameter 'include_inactive' with clear description. The schema already explains it, so the description adds no additional parameter meaning. 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 'List the caller's active subscriptions' with a specific verb and resource. It also lists return fields which adds precision. It distinguishes from sibling tools like 'subscribe' and 'unsubscribe' by focusing on 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?

Provides explicit use cases: 'review what you're monitoring before adding more or to find an id to cancel.' It lacks when-not-to-use or alternatives, but the context is clear for a simple list 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?

Discloses rate limit (5 per day), that it's free and doesn't count against quota, and that the team reads digests daily. No contradiction with annotations (readOnlyHint=false, etc.).

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

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

Concise yet comprehensive: covers purpose, usage, behavioral notes, and parameter guidance in a single paragraph without fluff.

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

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

Given simple tool with no output schema, description fully covers all needed aspects: purpose, usage, behavioral traits, and parameter semantics. No gaps.

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

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

Schema coverage is 100%, baseline 3. Description adds meaning by explaining enum values (bug, feature, etc.) and providing context on how to use the context object and message field.

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

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

The description clearly states the tool is for sending feedback (broken, missing, needs to exist) to the Pipeworx team. It differentiates from siblings by being a feedback mechanism, not a data or action tool.

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

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

Explicitly lists when to use (bug, feature, data_gap, praise) and provides guidance on what to describe (in terms of tools/packs, not end-user prompts). Also mentions rate limits.

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, idempotent, and non-destructive. Description adds valuable context: self-aggregating, derived from CF analytics-engine, no PII, caching behavior (5min-1h). No contradictions.

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

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

Two concise paragraphs with clear headings and bullet points. Every sentence adds value, though the 'Useful for' section could be slightly more compact. Overall efficient.

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

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

Given no output schema, the description explains return types (top tools, top packs, total call volume) and includes caching and data source details. Fully adequate for an agent to understand behavior without needing an output schema.

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

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

Schema coverage is 100% and includes a description for the 'window' parameter. The description mentions windows but adds only marginal context beyond the schema (e.g., shorter vs longer windows for hot vs steady-state). Baseline of 3 is appropriate.

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

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

The description uses specific verbs ('Returns') and resources ('top tools, top packs, total call volume') and clearly distinguishes itself from sibling tools by focusing on aggregated trending data rather than individual queries or 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?

Three explicit use cases are listed (discovery, confirmation, alignment). However, no explicit when-not-to-use or alternative tools are mentioned, though the use cases imply contexts where this tool is appropriate.

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

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

Annotations are consistent (readOnly, idempotent, not destructive). The description adds significant behavioral context: requires one parameter, explains algorithms (monotonicity, partition sum, semantic anchor, partition filter, fill check with book depth), and describes response format including when not to trade. 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 relatively long but each sentence adds value. It starts with a bold requirement, then proceeds with clear explanations. It is well-structured but could be slightly more concise; however, the complexity justifies the length.

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

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

Without an output schema, the description fully explains return values (opportunities, partition_check, fill check details). It covers edge cases (no args fails, low similarity, placeholder filters). Given the tool's complexity, the description is 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?

Both parameters have schema descriptions with 100% coverage. The description adds substantial meaning: provides example slugs for event and seed questions for topic, and explains the implications of each mode (e.g., cross-event catches patterns single-event misses). This enriches 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 finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) with specific examples, and differentiates from sibling tools like polymarket_edges by focusing on arbitrage detection.

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 event mode for a specific market and topic mode for cross-event scanning, with examples. It states calling with no args fails. However, it does not explicitly mention when not to use this tool versus alternatives like polymarket_fill_risk, but the context is clear 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 extensively covers behavioral details beyond annotations: model families, caching policy (1h at KV level), edge computation, slippage assumptions, and response structure. Annotations already indicate read-only, idempotent, non-destructive, and open-world, and the description adds significant 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 long and very detailed, covering multiple paragraphs and technical specifics. While well-organized with sections, it could be more concise without losing critical information.

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

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

Despite no output schema, the description thoroughly explains the response structure: top-level by_segment, fed fields, diagnostics. It covers what each opportunity includes, making it complete for an agent.

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

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

Parameter schema coverage is 100% with individual descriptions, so baseline is 3. The description adds extra context for some parameters (e.g., slippage explanation about Polymarket fees) that enhances understanding beyond the schema.

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

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

The description clearly states it scans top Polymarket markets and returns opportunities where Pipeworx data disagrees with market price, with a specific use case 'what should I bet on today'. It distinguishes itself from siblings by focusing on edges versus arbitrage or spreads.

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 a clear context for use ('what should I bet on today') but does not explicitly mention when not to use it or compare directly with siblings like polymarket_arbitrage. Usage guidance is implied rather than explicit.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable context: uses daily snapshots, 60-day TTL, decay computed from daily closes not intraday, and gaps due to no scans. 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?

The description is thorough and well-structured, with clear sections for response fields and limits. It is front-loaded with the main purpose and avoids unnecessary detail, though slightly long.

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?

Without an output schema, the description fully explains return values (tracked, expired, snapshot_dates) and limitations. It covers all necessary details for a complex time-series 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?

Input schema has 100% coverage with descriptions for both parameters. The description restates defaults and limits, but the schema already provides clamp and default values. Minimal additional value beyond schema.

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

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

The description explicitly states the tool's purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers a specific question and distinguishes itself from the sibling tool polymarket_edges by focusing on historical analysis.

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

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

The description provides clear context for when to use: 'Answers how long has this edge existed and is it shrinking?' with a concrete example. However, it lacks explicit instructions on when not to use or alternatives, though the context is sufficient.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, etc. Description adds major behavioral context: REQUIRES market or event, walks ladder, returns verdict (clean|degraded|cannot_fill), warns about forced_directional_risk. No contradiction with annotations.

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

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

Long but well-structured with bold headers for modes and warnings. Every sentence earns its place given complexity. Could be slightly trimmed, but front-loaded and clear.

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, yet description covers all return fields (verdict, capture_ratio, thin_legs, forced_directional_risk, etc.) and explains basket mechanics. Complete for a complex tool.

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

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

Schema description coverage is 100%. Description adds value beyond schema: explains interaction between side and mode (auto for basket), size_usd interpretation (max spend vs target proceeds), and clamp range. This elevates over baseline 3.

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

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

Clear verb+resource: 'Realizable-vs-theoretical edge check against live CLOB order-book depth'. Explicitly distinguishes single-market from basket modes, names return fields (top_of_book, vwap_fill_price, etc.), and differs from sibling tools like 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?

Explicit guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Explains why (theoretical overround not capturable, partial fills cause directional risk).

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

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

Annotations indicate read-only, open-world, and idempotent behavior. The description adds extensive behavioral details: compatibility_warning scenarios, temporal alignment, skipped cross types, and that real spreads are rare. No contradictions.

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

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

The description is well-structured with bold labels and clear sections, but it is somewhat verbose with detailed safety explanations. Could be slightly more concise without losing key information.

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

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

Given the tool's complexity (cross-venue spread with multiple safety fields and no output schema), the description is comprehensive. It explains response format, safety fields, temporal alignment, and compatibility warnings, leaving no major gaps.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds significant meaning by explaining the two modes, providing examples, and warning about pre-mapped topics, which goes beyond what the schema alone provides.

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

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

The description clearly states it calculates the cross-venue spread between Polymarket and Kalshi for the same question, and the name explicitly conveys this. It distinguishes from siblings like polymarket_arbitrage by focusing on cross-venue rather than same-venue arbitrage.

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

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

The description explicitly details two usage modes (topic shortcuts and explicit tickers) and warns that pre-mapped topics often return compatibility warnings, advising against assuming tradeability. It lacks explicit comparison to other sibling tools like polymarket_arbitrage, 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?

Annotations already declare readOnly, idempotent, non-destructive. Description adds scoping detail (anonymous IP, BYO key hash, account ID) which is useful context beyond annotations.

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

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

Three efficient sentences, front-loaded with core function, no extraneous 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?

Covers retrieval, listing, scoping, and pairing. Missing return format but acceptable given simplicity and no output schema.

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

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

Schema covers both param use and omission to list keys. Description restates this without adding new syntax or format details. Baseline 3 due to 100% 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?

Clearly states it retrieves a saved value or lists all keys, with specific verb and resource. Distinguishes from siblings '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?

Provides explicit context: use to look up stored context without re-deriving. Mentions pairing with remember/forget. Does not explicitly state when not to use, but guidance is clear.

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

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

The description discloses key behaviors beyond annotations: the mark_read effect, polling suitability, and an alternative HTTP access method. It aligns with annotations (readOnly, idempotent, openWorld) and adds context about state changes when mark_read is used. 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 concise and front-loaded with the main purpose. It includes examples and key behavioral notes in a compact format. However, it could be better structured with bullet points or clearer separation of features.

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 provides a reasonable summary of return fields. It covers filtering, mark_read, and alternatives (HTTP endpoint). It lacks explicit mention of ordering (most recent implied) and pagination behavior, but given openWorldHint, it's acceptable. Overall, the description is contextually complete for typical usage.

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

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

Schema coverage is complete, and the description enriches parameter understanding: it specifies that type can be e.g., 'sec_8k', that since is an ISO timestamp, and explains the effect of mark_read. This adds practical context beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: pulling fired events from the subscription feed. It specifies the returned fields and filtering options. However, it does not explicitly differentiate from sibling tools like list_subscriptions or subscribe, which are related to subscription management. The purpose is clear but could be more precise in distinguishing 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?

The description provides some usage context (polls work fine, alternative HTTP endpoint) but does not explicitly state when to use this tool versus related siblings. For example, there is no guidance on when to use recent_alerts vs list_subscriptions or subscribe. This leaves room for confusion.

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

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

Annotations already declare readOnlyHint and idempotentHint. The description adds significant behavioral context: sources (SEC EDGAR, GDELT/GNews fallback, USPTO soft-fail), input formats for `since`, return structure (changes[] grouped by source, citation URIs), and failure behaviors. 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 dense but efficiently structured: starts with usage examples, then details sources, parameter formats, and return structure, ending with an alternative tool reference. A small improvement could be splitting into bullet points for readability, but it's quite concise for the information covered.

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

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

Given 3 parameters, no output schema, and moderate complexity, the description covers all essential aspects: what the tool does, when to use it, how to use parameters (including formats and defaults), what sources are queried, failure modes, and return format. It also distinguishes from its sibling tool, making it self-contained.

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

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

Schema coverage is 100%, but the description adds valuable semantics: explains `since` format (ISO or relative shorthand) and suggests typical values like '30d'. For `value`, clarifies ticker or CIK. For `type`, notes only 'company' is supported. This goes beyond bare schema descriptions.

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

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

The description clearly states the tool's purpose: a change feed for a company in the last N days/weeks/months, fanning out to multiple sources. It uses specific verbs ('What's new with X', 'latest on Y') and distinguishes from the sibling tool entity_profile, making it easy for an agent to select the right tool.

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

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

Provides explicit when-to-use guidance with example queries (e.g., 'what's new with X', 'latest on Y') and explicitly directs the agent to entity_profile for static profile needs. This helps the agent avoid misuse.

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

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

Discloses key-value structure, scoping by identifier, persistence differences (authenticated vs anonymous sessions). Adds context beyond annotations (which only hint at non-destructive, idempotent writes).

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?

Every sentence adds value: purpose, use case, technical detail, sibling references. No redundancy or filler.

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

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

Covers usage, scoping, persistence, and lifecycle. Does not describe return value, but this is acceptable for a storage tool paired with recall.

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

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

Schema covers 100% of parameters with descriptions; description enhances with formatting examples (e.g., 'subject_property') and clarifies key as arbitrary string, value as any text.

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 'Save data' and resource 'data the agent will need to reuse later', with explicit scope (conversation/sessions) and examples. Distinguishes from siblings recall and forget.

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

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

Explicitly tells when to use ('discover something worth carrying forward') with concrete examples. Also pairs with recall/forget for lifecycle management.

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 safety hints. The description adds value by explaining internal cascading behavior and output structure for each type, exceeding annotation coverage 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 detailed but every sentence adds value. It is front-loaded with examples and purpose. Could be slightly tighter but remains well-organized 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?

With no output schema, the description fully compensates by detailing return values for each type. It also covers internal behavior and replacement of multiple lookups, making the tool self-contained.

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

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

Schema coverage is 100% with good descriptions. The description further enriches meaning by providing concrete examples for each parameter (e.g., ticker, CIK, drug names), adding value beyond the schema.

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

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

The description clearly states the tool resolves names to canonical identifiers, with examples like 'ticker for...' and explicitly says it is for when you need an ID from a name. It distinguishes from siblings by being the go-to resolution tool.

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

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

The description explicitly advises 'Use FIRST whenever you have a name but need an ID,' providing clear when-to-use guidance. It also details supported entity types and outputs, leaving no ambiguity about applicability.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive behavior. The description adds significant detail: it probes each entity with ai_visibility_check, ranks by score, surfaces most/least recognized, and returns a ranked list with score, confidence, signal density. No contradictions.

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

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

The description is two sentences, front-loading the main action and then elaborating efficiently. No wasted words.

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

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

Despite no output schema, the description explains the return format (ranked list with score, confidence, signal density) and the internal mechanism (probes per entity). Covers all key aspects given 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 covers 100% of parameters. The description adds value by clarifying that the first entity is treated as 'subject' and explaining the models parameter (workers-ai vs anthropic with _apiKey).

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: 'Compare AI visibility across multiple entities side-by-side.' It specifies the verb (compare/scan), resource (AI presence/visibility), and distinguishes it from sibling ai_visibility_check which likely checks single 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 usage context: 'Useful for competitive AI-marketing audits' and gives an example question. However, it does not explicitly state when not to use this tool or alternatives, leaving some 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?

Beyond annotations (readOnly, idempotent, not destructive), description adds behavioral context: partial failures, bundlephobia delay (5-30s), graceful degradation with sources_failed list. No contradiction with annotations.

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

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

Description is detailed but front-loaded with key purpose. Some extra detail may be trimmed, but overall structure is clear and information-dense.

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?

Without output schema, description adequately explains return structure (summary block, advisories, links, alternatives). Covers core functionality and edge cases.

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%. Description adds value beyond schema: scoped packages accepted, version defaults to latest. Provides additional meaning to input 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 states a specific composite check for npm packages, covering license, advisories, version history, and bundle size. It distinguishes from siblings by specifying NPM ecosystem and mentioning alternatives for other ecosystems.

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

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

Explicitly says use when agent asks about safety/popularity/size/cost of adding an npm package. Mentions limitations (NPM only in v1, other ecosystems via deps.dev:version directly). Provides clear context for tool selection.

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

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

Discloses technical details: embeddings model (BGE-base-en), similarity metric (cosine), window size (500-char overlapping), character cap (200K chars with truncation flag). Annotations already indicate safe/idempotent read, so description adds significant behavioral context beyond annotations.

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

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

Description is a single well-structured paragraph that front-loads the main purpose. Could be slightly more concise, but every sentence adds value without redundancy.

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

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

Despite having no output schema, description explains return values (passages with offsets and similarity scores), technical constraints, and use-case pairing. All necessary information for an agent to use the tool correctly is present.

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

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

Schema description coverage is 100%, providing baseline of 3. Description enhances with concrete examples for 'text' (SEC filing, article) and 'query' (natural-language questions), and clarifies that 'limit' controls number of passages returned.

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 'Semantic search INSIDE a fetched record' using specific verb and resource, and distinguishes from sibling 'ask_pipeworx_grounded' by explaining the pairing and workflow.

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 when to use: 'Use when the record is too big to cram into the prompt' and explains how it saves context and provides offsets for verification. Lacks explicit 'when not to use' but implies 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?

Adds significant behavioral context beyond annotations: requires Pipeworx OAuth account, anonymous+BYO cannot persist, delivery channel limits (SMS 10/day), webhook auto-disable, signature information. 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?

Packs essential info into a well-organized description: purpose first, then requirements, types, delivery channels. No unnecessary sentences.

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

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

Covers all aspects: account requirement, types, delivery options, return value, limits. Minor gap: doesn't mention idempotent behavior (e.g., duplicate returns same subscription). Overall very complete for the 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%, but description enriches with concrete examples for each type's params (e.g., items codes, topic values) and delivery details (verification, caps, webhook secret).

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

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

Clearly states 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' Distinguishes from siblings like unsubscribe 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?

Provides context on when to use (proactive monitoring, persistent) but lacks explicit when-not-to-use or alternatives. However, the description is clear on supported types and delivery channels.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds that it returns example questions with exact tool+argument shape from a live catalog, and that it can be called with no arguments for full spread. No contradictions.

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

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

The description is slightly lengthy but front-loaded with common trigger questions and each part adds value. It could be slightly more concise, but it remains clear 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?

With no output schema, the description fully explains that it returns category-bucketed example questions with exact tool+argument shapes. It covers usage, parameter options, and the tool's role as an onboarding resource. Complete for the tool's complexity.

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

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

Schema coverage is 100% (single optional string parameter). The description adds meaning by listing example topic values (finance, pharma, etc.) and clarifying behavior when omitted (full spread) vs passed (focus). This goes beyond the schema's 'Optional focus area' 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 states it is the onboarding entry point for an agent that wants to know what Pipeworx can do, returning category-bucketed example questions with tool arguments. It explicitly distinguishes this from other tools by saying 'Use this FIRST when you do not yet know what Pipeworx can do' and lists sibling tools like 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?

It clearly says when to use: 'Use this FIRST when you do not yet know what Pipeworx can do.' It also says to call with no arguments for full spread or pass a topic to focus. However, it doesn't explicitly state when NOT to use it or compare to sibling tools beyond the first-use directive.

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 context beyond annotations by explaining that the row is deactivated (not deleted) and its historical events remain available via recent_alerts. This is consistent with the destructiveHint=false annotation.

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 concise sentences that front-load the purpose and then provide key behavioral details. 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 tool with one parameter and no output schema, the description covers all necessary context: action, ownership, and behavioral nuance. Annotations further support safety.

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

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

The input schema has 100% description coverage, so the schema already documents the 'id' parameter. The description does not add further parameter details, which is acceptable given the schema 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 states the action 'Cancel a subscription by id' and specifies the resource (subscription). It distinguishes itself from sibling tools like subscribe and list_subscriptions.

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

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

The description includes ownership enforcement ('you can only cancel your own subscriptions'), providing a clear usage constraint. It also explains the effect (deactivation vs deletion), but does not explicitly mention when not to use the tool.

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

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

Annotations already provide readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds no additional behavioral context, 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 extremely concise (two words), but this brevity sacrifices clarity. It earns its place but fails to inform adequately.

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 an output schema, the description does not mention return values or response structure. The input parameter is unexplained, making the tool's usage unclear.

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% and the description does not explain the 'user_id' parameter format, source, or constraints beyond the schema's minimal example.

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 'User profile.' is vague and does not differentiate from sibling tools like 'entity_profile' or 'user_videos'. It lacks a specific verb and resource 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?

No guidance on when to use this tool versus alternatives. Sibling tools that may also retrieve user data are not addressed.

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 no behavioral context beyond these annotations, contributing nothing to 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?

Extremely brief but not concise in a helpful way; it is a tautological placeholder that wastes the description slot. Every sentence should add value, but this one does not.

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 an output schema and 5 parameters, the description provides no context for usage. The tool requires user_id and supports pagination/sorting, but none of this is mentioned. Completely inadequate.

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 and 5 parameters, the description does not explain any parameter. The example in the schema is insufficient, and the description adds no meaning beyond property 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 'User videos.' restates the tool name without adding any specific verb, resource, or scope. It fails to distinguish this tool from sibling tools like 'video', 'videos_search', or 'user', which likely overlap.

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

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

No guidance on when to use this tool versus alternatives such as 'video' or 'videos_search'. The description provides no context for selection.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. The description adds value by detailing return values (verdict types, structured form, citation, delta), the data source (SEC EDGAR + XBRL), and efficiency gain. No contradictions.

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

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

The description is well-structured, starting with example queries, then purpose, scope, returns, and benefit. It is slightly lengthy but each sentence adds value 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?

Despite no output schema, the description fully explains the return structure (verdict types, actual value, citation, delta) and the data source, making it complete for an agent to understand behavior and 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?

Schema coverage is 100% with a clear description of the 'claim' parameter. The tool description reiterates natural-language claim examples but does not add new semantic details 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 performs natural-language claim verification against authoritative sources, with specific examples. It distinguishes itself from siblings by focusing on fact-checking financial claims and replacing multiple sequential 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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also limits scope to company-financial claims (v1), implying when not to use it, but does not name alternative tools.

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

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

Description adds no behavioral context beyond what annotations already provide (read-only, idempotent, non-destructive). It doesn't describe return format, error conditions, or any 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?

Extremely concise, but at the expense of useful content. The single sentence is clear but inadequate for a meaningful tool definition.

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 lacks important details such as what the tool returns (despite an output schema existing), how to use the ID, and how it relates to other video-related tools.

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 only parameter 'id' is not described in the input schema (0% coverage) and the description offers no explanation of its format, source, or required specifics, forcing the agent to guess.

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 'Single video.' is a tautology that barely clarifies the tool's function. It doesn't specify a verb or resource, and fails to distinguish from sibling tools like 'videos_search' or 'category_videos'.

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 guidelines provided about when to use this tool versus alternatives. No context on prerequisites or exclusions, leaving the agent without direction.

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, openWorldHint, and non-destructive nature. Description adds no additional behavioral context, failing to add value beyond structured data.

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 (2 words) but under-specified. Does not earn its place as it lacks necessary detail to be 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 6 parameters, 1 required, no param descriptions, and siblings requiring differentiation, the description is completely inadequate. Fails to guide effective use.

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 0% parameter description coverage. Description does not mention any parameter or provide meaning beyond parameter names and examples. No compensation for low 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 is extremely terse ('Search videos'). It names verb and resource but lacks specificity to distinguish from sibling tools like 'search_within', 'channel_videos', 'user_videos'. Barely above tautology.

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

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

No guidance on when to use this tool vs alternatives (e.g., 'search_within', 'channel_videos'). No exclusions or context provided.

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