Wikimedia Rest

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, so the agent knows it's safe and idempotent. The description adds value by mentioning cost implications (Anthropic requires BYO key) and return format, but doesn't cover rate limits or other behavioral details.

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

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

The description is two sentences, front-loads the action, and contains zero wasted words. Every sentence earns its place.

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

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

Despite having no output schema, the description explains the return structure (per-model {score, confidence, signals, raw_response} + combined view). All parameters are described, and usage context is provided. The tool is adequately documented for an LLM-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 description coverage is 100%, so baseline is 3. The description adds practical usage tips: explains model values ('workers-ai', 'anthropic'), apiKey necessity, and context disambiguation. This goes 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 probes LLMs for knowledge about a business/brand/product/topic and scores visibility per model. It specifies the default model and optional Anthropic probing. While it doesn't explicitly differentiate from sibling tools, the purpose is specific and actionable.

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

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

The description provides clear usage scenarios: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It doesn't include explicit when-not-to-use or alternatives, but the context is helpful and sufficient for most agents.

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

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

Annotations already indicate read-only, idempotent, open-world. Description adds that it routes queries to many tools and returns structured citations, providing 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 detailed but somewhat repetitive (e.g., 'question' appears multiple times). Could be trimmed slightly while retaining all key points.

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 routing tool with many sources, the description covers what, when, and outcome. No output schema, but it mentions structured answer with citation URIs.

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

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

Schema coverage is 100% with descriptions. Description adds examples of the kind of questions to ask, which enriches the parameter meaning beyond the schema.

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

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

The description clearly states it handles factual questions about structured data and lists specific domains (SEC filings, FDA, etc.). It distinguishes from web search but not from sibling tools like ask_pipeworx_grounded.

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

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

Explicitly says to prefer over web search and provides examples of when to use. However, it does not mention when not to use alternatives like ask_pipeworx_grounded.

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 significant behavioral context beyond annotations: it mentions the extra LLM call cost, the structured refusal reasons, and the exact output format with fields like evidence and refusal_reason. 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 with two meaningful sentences. It front-loads the purpose and includes critical details about usage and behavior without redundancy. Every sentence adds value.

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

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

Given the tool's complexity (multiple modes, explicit refusals, no output schema), the description fully covers response format, refusal reasons, use cases, and relation to sibling. It provides all necessary context for an agent to use the tool correctly.

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

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

Schema description coverage is 100%: all parameters are documented as aliases for 'question'. The description does not add additional meaning beyond the schema, but since the schema is fully covered, a baseline of 3 is appropriate.

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

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

The description clearly states the tool's purpose: a hallucination-resistant answer mode for high-stakes reads. It explains that it routes like ask_pipeworx but extracts answers only from tool results, and includes explicit refusals. It distinguishes from the sibling ask_pipeworx, which is for casual lookups, by noting the extra cost and use case.

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

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

The description explicitly tells when to use: for answers that will be quoted, cited, or acted on, and when the agent must not invent facts (finance, legal, medical, statements). It also advises to prefer ask_pipeworx for casual lookups, providing clear when-not and an alternative.

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

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

Annotations indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description goes far beyond by detailing fan-out behavior, classifiers, response shapes (market, analysis, evidence), resolver contract with match confidence, parent event extraction, news fallback mechanisms, safety features (low-confidence short-circuits, closed market handling), spread warnings, and resolution-rule risk. This provides exceptional 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 quite long but front-loaded with core purpose. Each sentence adds value, covering classifiers, fan-out examples, response shapes, resolver contract, parent events, news fields, safety, spread, and resolution rules. While comprehensive, the length may overwhelm an AI agent scanning for quick details. A slightly more structured format could improve conciseness.

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

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

Given the complexity of the tool and the absence of an output schema, the description is remarkably complete. It covers all key aspects: classifiers, fan-out examples per category, response fields, resolver contract with alternatives, parent event extraction, news fallback handling, safety features (low-confidence, closed markets, wide spreads), and resolution-rule risk. No gaps identified.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds valuable usage context: market accepts slug/URL/question text, depth defaults to 'thorough', and include_raw summarizes vs full payloads. The description also includes examples in the schema property. It adds meaning beyond the schema without redundancy.

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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the verb ('Research'), resource ('Polymarket bet'), and scope ('one call'). It distinguishes from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on data research.

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

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

The description explicitly provides use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z"'. It also explains when the tool short-circuits (low confidence, closed markets), which implies when not to use it. However, it does not explicitly name alternative tools, though the sibling list makes differentiation clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, so safety profile is covered. The description adds valuable behavioral details: what data each type pulls (SEC EDGAR for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and return format (paired data + citation URIs). 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 a single paragraph that front-loads with query examples. Every sentence contributes meaningful information. While slightly lengthy, it is efficient and well-structured for the content.

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

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

Given the tool's complexity (two simple parameters, no nested objects, no output schema), the description is comprehensive. It covers purpose, usage, data sources, result format, and even replaces 8-15 sequential lookups. No gaps remain for an agent to select and invoke correctly.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds context for each enum value (what 'company' vs 'drug' retrieves) and examples for values, but the schema already clearly explains parameters. No additional syntactic or format details beyond schema.

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

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

The description clearly states the tool's purpose: side-by-side comparison of 2-5 companies or drugs. It uses specific verbs like 'compare' and provides example queries. It distinguishes from sibling tools like entity_profile by emphasizing parallel single-call comparison vs sequential 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?

The description explicitly lists query patterns (e.g., 'compare X and Y', 'which is bigger') and instructs to 'ALWAYS PREFER over sequential single-pack lookups' when comparing entities. This provides clear when-to-use guidance.

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

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

Beyond annotations (readOnly, openWorld, idempotent), the description details the decomposition process, parallel routing, return of verbatim evidence with citations, and explicit gaps for unanswered facets. Also states expected timing (15-60s) and that it never invents facts.

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

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

The description is front-loaded with purpose and key differentiators, then details. While slightly long, every sentence adds value. Minor redundancy ('decomposes... routes...') could be tightened, but overall efficient.

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

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

Despite no output schema, the description fully explains the return format: structured findings packet with verbatim evidence, confidence, source, fetched_at, citation, and gaps. Also covers time estimate and prerequisites, leaving no 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?

Schema coverage is 100%, but the description adds meaning: it explains the depth enum values (quick=3, standard=5, thorough=8) and links thorough to paid plans. It also clarifies question parameter accepts broad/multi-part queries, complementing 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 'Grounded multi-source research in ONE call' by decomposing questions into sub-questions and routing them in parallel. It distinguishes itself from sibling tools like ask_pipeworx by targeting broad or multi-part questions.

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

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

Explicitly states when to use ('Use for broad or multi-part questions') and when not ('use ask_pipeworx for single lookups'). Also mentions prerequisites (Pipeworx account) and paid plan requirement for 'thorough' depth, giving clear 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=true, idempotentHint=true, and destructiveHint=false. The description adds value by disclosing that results are 'ready to call directly, no second schema lookup needed' and mentions the 'top-N' limit. No contradictions with annotations.

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

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

The description is informative but slightly verbose. It front-loads the key action and purpose, but could be trimmed without losing clarity. Still, it earns its length by providing multiple examples and usage guidance.

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

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

Despite lacking an output schema, the description fully explains what is returned (tool names, descriptions, full input schemas with curated examples) and covers a broad domain of use cases. It is complete for a discovery tool with rich annotations.

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

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

Schema coverage is 100%, so the parameters are well-documented. The description mentions that 'query' accepts aliases (task, q, description, search), but this information is already in the schema. No additional semantic meaning beyond what the schema 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 the tool's purpose: 'Find tools by describing the data or task.' It provides a comprehensive list of example use cases (SEC filings, financials, etc.) and distinguishes from sibling tools by positioning itself as a discovery tool rather than a specific data 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?

Explicit guidance: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This tells the agent when to use it and implies when not to (if a specific tool is already known). Examples of queries are given.

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

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

Annotations already declare readOnly, non-destructive, idempotent; description adds rich behavioral detail: parallel call across sources, patent soft-fail, GDELT→GNews fallback, specific return fields. 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?

Long but information-dense; every sentence earns its place. Front-loaded with examples. Minor verbosity but acceptable for complex tool.

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

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

Despite no output schema, description details all return types, sources, edge cases (patent sunset, fallback), and limitations, making it complete for an AI agent.

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

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

Schema coverage is 100% and description reinforces examples ('AAPL', '0000320193') and restates that names are not supported. Adds value beyond schema by providing usage context.

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

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

The description clearly states the tool creates a full cross-source profile of a US public company. It distinguishes from siblings by explicitly preferring this over chaining single-pack lookups and directing name resolution to 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?

Provides explicit when-to-use guidance: 'ALWAYS PREFER over chaining...' and when-not: 'names not supported (use resolve_entity first)'. This leaves no ambiguity.

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

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

Annotations already declare read-only, non-destructive, idempotent behavior. Description adds details on content types but no new behavioral traits beyond what annotations cover. 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, front-loaded with main purpose. Efficient but could add brief param guidance without clutter.

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

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

Output schema exists, annotations are rich, but parameter semantics are weak. For a read-only retrieval tool, description is adequate but not thorough.

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 0% description coverage. Description only implies date parameters ('specific date') but does not explain 'lang' or 'project' parameters, leaving ambiguity.

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 retrieves Wikipedia's 'today's featured' content (featured article, most-read, picture of the day, on-this-day events) for a specific date. It distinguishes from sibling 'onthisday' which is likely limited to events.

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: 'what's on Wikipedia today', 'featured article on 2026-05-21', 'seed daily-roundup content'. Does not explicitly mention alternatives or when not to use, but context is clear.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true, indicating deletion and idempotency. The description adds minimal behavioral information beyond 'delete' and 'clear sensitive data.' No contradictions are present.

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

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

The description is two sentences, front-loaded with the core action, and every sentence adds value. No unnecessary words, making it highly concise.

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

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

Given the tool's simplicity (one parameter, no output schema), the description fully covers what the tool does, when to use it, and its relationship to siblings. No gaps are 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?

The schema has 100% coverage with one parameter 'key' described as 'Memory key to delete.' The description does not add additional meaning or examples beyond the schema, 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 explicitly states 'Delete a previously stored memory by key,' clearly identifying the verb (delete) and resource (memory by key). It distinguishes from sibling tools 'remember' and 'recall' by focusing on deletion, which is the opposite operation.

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

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

The description provides explicit when-to-use scenarios: 'when context is stale, the task is done, or you want to clear sensitive data.' It also mentions pairing with 'remember' and 'recall,' guiding the agent on complementary tools.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating a safe, non-destructive operation. The description adds specifics: it fetches the page, extracts title/description/key links, and outputs standard markdown. This provides behavioral context beyond the annotations, such as the extraction and formatting process.

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, consisting of three well-structured sentences. It front-loads the purpose and provides additional context without unnecessary verbosity. Every sentence adds value.

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

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

Given the low complexity (2 parameters, no output schema), the description fully covers what the tool returns: a single text blob ready for deployment. It is complete for an AI agent to understand inputs and outputs.

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

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

Schema coverage is 100%, so the input schema already describes the two parameters (url and max_links) with clear descriptions. The description does not add additional parameter semantics beyond stating the extraction process. Baseline 3 is appropriate as the schema carries the burden.

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

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

The description clearly states the tool's function: generating a production-ready llms.txt file for a URL. It specifies the verb 'generate', the resource 'llms.txt file', and the target 'any URL'. It distinguishes itself from sibling tools like 'page_summary' and 'ai_visibility_check' by focusing on a specific output format.

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

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

The description provides clear use cases: getting a client's site indexed, drafting for own project, auditing competitors. It implies when to use but does not explicitly exclude scenarios or mention alternative tools. The context is sufficient for an AI agent to decide when to invoke this tool.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds return fields and caller-specific behavior, but lacks details on pagination or ordering. With annotations, a moderate score is appropriate.

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

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

The description is three sentences, front-loaded with action and return info, with no superfluous content. Highly efficient.

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

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

Given the tool's simplicity (1 optional param, no output schema, strong annotations), the description is complete: it states purpose, returns, and usage guidance.

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

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

The schema already fully describes the single parameter (include_inactive) with 100% coverage. The description does not add additional meaning beyond the schema, justifying a baseline score of 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?

The description clearly states it lists the caller's active subscriptions and specifies return fields (id, type, params, etc.). It distinguishes from siblings like subscribe/unsubscribe by providing usage context.

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

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

The description gives explicit use cases: 'review what you're monitoring before adding more' and 'find an id to cancel'. This implies when to use, but does not explicitly exclude other contexts or contrast with siblings.

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

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

Annotations already mark the tool as readOnly, openWorld, idempotent, and non-destructive. The description complements this by specifying the output categories (births, deaths, etc.) and the historical scope. It adds context beyond annotations without contradiction.

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

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

The description is concise—two sentences—covering purpose, example queries, parameter guidance, and output options. No wasted words, and 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?

Given the presence of an output schema and annotations, the description is adequate for a date-based historical lookup tool. It explains what the tool returns and how to filter. Minor missing details (e.g., language/project behavior) prevent a perfect score.

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

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

Schema description coverage is only 20% (only 'type' has a description). The description adds critical meaning for the 'type' parameter by listing valid values, but it does not explain 'lang', 'project', or provide details for 'day'/'month' beyond examples. More explanation for uncovered parameters would be beneficial.

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: retrieving historical events (births, deaths, events, holidays, selected) for any month+day. It uses specific verbs and resources (e.g., 'what happened on this day') and differentiates from sibling tools by focusing on historical date-based lookups, which no other sibling explicitly offers.

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

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

The description provides clear usage guidance with example queries and parameter values, such as 'type="all"' or filtering to 'births'/'deaths'. However, it does not explicitly state when not to use this tool or mention alternatives among siblings, which prevents a top score.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds that it returns rendered HTML but does not disclose potential size limits or rate-limiting behavior. Minimal added value.

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 with front-loaded purpose and immediate differentiation from sibling. Every word adds value.

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

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

Given the tool's simplicity and existing output schema, the description is largely adequate. It could briefly mention output size or pagination, but it is sufficient 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 the description does not explain the 'lang' or 'project' parameters, leaving their meaning unclear. The examples in the input schema help marginally, but the description should compensate.

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

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

The description clearly states the tool provides 'Full Wikipedia article HTML (Parsoid output)' and explicitly contrasts it with page_summary, making its purpose distinct and specific.

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

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

It tells when to use ('when page_summary's extract isn't enough') and implies its scope (complete article, infoboxes, tables). However, it does not explicitly exclude other scenarios or 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?

The annotations already indicate this is a safe, read-only operation. The description adds context about the type of media included but does not disclose additional behavioral traits beyond what annotations provide.

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

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

Two sentences with no fluff; front-loaded with the key purpose. Every sentence earns its place.

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

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

While an output schema exists, the description lacks guidance on optional parameters and does not explain the expected output format or behavior when parameters are omitted. Incomplete for a tool with no schema descriptions.

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 0% description coverage, and the description provides no explanation of the parameters (lang, title, project) beyond an example using title. This is a significant gap.

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

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

The description clearly states that the tool returns images, videos, and other media embedded in Wikipedia articles, and provides example queries. However, it does not explicitly differentiate from similar sibling tools like page_html.

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

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

The description gives useful example use cases but does not specify when not to use this tool or provide alternatives among sibling tools.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds value by specifying the exact metadata fields returned (categories, language links, etc.), which goes beyond annotations. No contradictions detected.

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

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

The description is two sentences long, with the first sentence immediately stating the output and the second providing usage examples. Every sentence is informative and there is 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?

Given the presence of an output schema, the description does not need to detail return values. However, it lacks information about parameter defaults, error conditions, or prerequisites (e.g., valid title). Among many sibling tools, this description is adequate but could be more complete to help an agent choose correctly.

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

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

The input schema has 3 parameters (lang, title, project) with 0% schema description coverage. The description does not explain the meaning of lang or project, nor does it mention their optionality or default behavior. Only title is implied as required through examples, leaving the agent without guidance on the other parameters.

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

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

The description clearly states 'Wikipedia article metadata' and lists specific fields (categories, language links, content_urls, page_id, ns). It gives example use cases such as 'what category is X in' and 'what languages is this article in', which effectively distinguishes this tool from sibling tools like page_summary or page_html.

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 specific use cases (categories, languages, IDs) and hints at usage for obtaining canonical IDs needed by other tools. However, it does not explicitly state when NOT to use this tool or mention alternatives, leaving some ambiguity for the agent.

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. Description adds that the endpoint is binary and the caller fetches the PDF directly, providing helpful 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, front-loaded sentence that efficiently states purpose. However, it could include a brief note on required parameters without losing conciseness.

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?

Adequately describes the return value (URL) given the output schema exists, but missing parameter context (especially the required title) reduces completeness for a tool with 0% schema coverage.

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

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

Schema description coverage is 0%, and the description does not explain any parameters (e.g., 'title' is required). The description fails to compensate for the lack of parameter documentation in the schema.

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

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

Description clearly states the tool returns the PDF download URL for a Wikipedia article, specifying it's a binary endpoint. This distinguishes it from sibling tools like page_html or page_metadata.

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?

Implied usage (when needing a Wikipedia article's PDF), but no explicit guidance on when not to use or comparison to alternatives among many page_* siblings.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the description adds context about returning references/citations. No contradictions. Could mention that not all articles have references, but adequate.

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 succinct sentences with key information front-loaded. No unnecessary words; 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?

Output schema exists, so return values are documented. Description covers purpose and usage but does not mention potential edge cases (e.g., articles with no references). Good overall.

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 0% with no descriptions on parameters. The description does not explain what 'lang', 'title', or 'project' mean, leaving the agent to infer from examples. Should at least state that 'lang' specifies the Wikipedia language edition and 'title' is the article title.

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

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

Description clearly states the tool returns citations/references/sources from Wikipedia articles, providing specific example queries. It distinguishes itself from siblings like page_summary or page_metadata by focusing on references.

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 like 'what sources back the claim that…' and 'harvest authoritative external links'. Does not mention when not to use, but the examples give clear context for appropriate usage.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. Description adds valuable context: returns up to ~12 related pages with summaries. No contradictions.

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

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

Three concise sentences: core functionality, typical use cases. No wasted words. Front-loaded with the key phrase.

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 main purpose and use cases. Without parameter details, agent might guess lang/project usage. Output schema exists but not shown, so return format is partly covered by 'with summaries'. Adequate for a simple 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 has 3 parameters (lang, title, project) with 0% description coverage. Description only implies title as the main parameter ('any topic'), but does not explain lang or project. Minimal value added beyond schema.

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

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

Description clearly states the tool returns related articles from Wikipedia's related-articles graph, using phrases like 'What's related to X' and 'see also'. It distinguishes from sibling tools like search_within or entity_profile by focusing on topic exploration.

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 provides use cases: 'what should I read next', topic exploration, finding adjacent concepts. Lacks explicit alternative tools or when-not-to-use, but clear enough for typical agent 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 already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. Description adds context about the nature of the output (recent history) and ties to vandalism checking, which is beyond annotations.

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

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

Two sentences with no fluff: first sentence states what the tool does, second lists use cases. Front-loaded and efficient.

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

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

Given annotations and output schema, descriptions covers core purpose. However, it omits details like how many revisions are returned, the meaning of 'recent', and the role of lang/project parameters. Slightly incomplete for a parameter-heavy tool.

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

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

Schema description coverage is 0%, so description should compensate. It mentions 'Wikipedia article' implying title but does not explain lang or project parameters. The example in schema is not in description, leaving param semantics largely undocumented.

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

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

Description clearly states it returns 'Recent edit history of a Wikipedia article' and provides three specific use cases (when last edited, who edited, check for disputes). This distinguishes it from sibling tools like page_metadata or page_summary.

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 appropriate uses such as checking last edit time or recent disputes. However, it does not mention when to avoid this tool or provide alternatives, which would be helpful given many sibling tools.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by specifying the returned fields (title, description, thumbnail, lead-paragraph extract) and the default project and language. It does not contradict annotations and provides additional behavioral context beyond what annotations offer.

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

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

The description is concise and well-structured. It front-loads common query patterns, states the tool's purpose, gives usage guidance, and ends with an example. Every sentence adds value with no waste.

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

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

Given the tool has only three parameters (all documented), an output schema (so return values are explained elsewhere), and annotations covering safety and idempotency, the description is complete. It adequately covers all necessary information for an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100% (all three parameters have descriptions). The description adds examples (e.g., page_summary({ title: 'Albert Einstein' })) and clarifies default values for 'lang' and 'project'. This adds practical meaning beyond the schema's technical 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 fetches a Wikipedia article extract (title, description, thumbnail, lead-paragraph extract) for any topic, person, place, event, or concept. It uses specific verbs and resources ('fetches the Wikipedia article extract') and distinguishes from siblings by focusing on a 'quick encyclopedic reference' rather than full page content or metadata.

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

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

The description explicitly states when to use: 'whenever an agent needs a quick encyclopedic reference.' It provides example query patterns and notes defaults (en.wikipedia.org) and options (project/lang). However, it does not explicitly mention when not to use or compare to siblings like page_html or page_metadata, leaving some room for 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?

The description discloses rate-limiting (5 per identifier per day), that it's free and doesn't count against tool-call quota. These behavioral traits go beyond the annotations (which don't contradict) and provide essential context for the agent.

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

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

The description is a single well-structured paragraph that front-loads the core purpose, then provides usage guidance and behavioral notes. 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?

The description covers all necessary aspects: purpose, use cases, parameter guidance, rate limits, and quota impact. For a feedback tool, no output schema is needed, and the description is sufficiently complete 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?

The input schema already has 100% description coverage. The description adds value by explaining enum values (bug, feature, etc.) in detail, stating message length constraints (1-2 sentences, 2000 chars max), and guiding on optional context fields (pack, tool, vertical). This enhances understanding beyond the schema.

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

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

The description clearly states the tool's purpose: submitting feedback about Pipeworx tools (bugs, missing features, praise). It distinguishes from sibling tools like ask_pipeworx or discover_tools by focusing on feedback rather than queries or discovery.

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

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

The description explicitly specifies when to use the tool (wrong data, missing tool, praise) and what to avoid (pasting end-user prompts). It provides instructions on describing issues in terms of tools/packs and mentions the team's reading habits.

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

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

Annotations already declare read-only and idempotent behavior. The description adds useful context: self-aggregating, no PII, caching behavior, and derivation from analytics engine, enhancing transparency beyond annotations.

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

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

Description is concise and well-structured: first sentence captures core functionality, then bullet-like use cases, and finally source/caching details. 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?

For a simple tool with one optional parameter and no output schema, the description fully covers purpose, use cases, parameter, and behavior. Return types are mentioned.

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 the implications of each window length (short vs. long windows), beyond the enum values.

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

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

The description clearly states the tool returns trending tools, packs, and call volume over a window, using specific verbs and resources. It distinguishes itself from siblings by focusing on aggregate trending data rather than individual tool details.

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

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

Three explicit use cases are provided, guiding when to use the tool (discovery, confirmation, alignment). However, it does not mention when not to use it or directly compare to sibling tools like 'discover_tools'.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as appropriate. The description adds substantial behavioral context: the dual-mode requirement, partition check logic, semantic anchor for cross-event pairs, partition filter, and fill check. No contradictions with annotations.

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

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

The description is lengthy and contains many details. While the complexity justifies the length, it could be more concise. The structure is logical (requirements, modes, details, response). Some sentences are dense and might be split for clarity. Still, it is functional.

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

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

Given no output schema, the description thoroughly explains the response structure (opportunities[] with fields, partition_check, fill check) and algorithm constraints (Jaccard similarity, placeholder filter). It also references a sibling tool for further customization. No gaps are apparent.

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

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

Schema coverage is 100% with descriptions for both parameters. The description further enriches semantics by explaining when to use each (e.g., 'use this if you know the specific Polymarket event' for event, and 'use this if you want to scan related events across the platform' for topic), along with examples and the requirement that one must be 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?

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes two modes (event and topic) with specific use cases. However, it does not explicitly differentiate from sibling tools like polymarket_edges or polymarket_edge_tracker, though the distinct functionality is implied.

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: 'REQUIRES one of event OR topic — call with no args fails.' It explains when to use each mode and why cross-event mode is beneficial. It also directs users to the sibling tool polymarket_fill_risk for custom sizing. It does not mention when not to use this tool or alternative arbitrage tools, but the guidance given is clear and actionable.

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

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

The description provides extensive behavioral details beyond annotations. It explains the three model families (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, CONCENTRATED_LONGSHOT) with their methodologies, edge calculation (net of slippage), Kelly fraction caps, liquidity/spread filters, 24h-move warnings, and diagnostics. Caching behavior is also disclosed. Annotations already declare readOnlyHint=true, so 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 lengthy but well-structured: purpose, model segments, output fields, knobs, response structure, and caching. It is front-loaded with the main idea. While detailed, each section justifies its presence given the tool's complexity. Minor verbosity in technical details (e.g., per-sport α values) is acceptable for precision.

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 9 parameters and no output schema, the description covers the response structure (by_segment, fed_candidates, _diagnostics) and opportunity fields (edge_pp_net, kelly_fraction, etc.). It explains caching and diagnostics for empty segments. Slight omission of exact output format (e.g., list structure) but sufficient for an agent to understand.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds practical guidance beyond schema, e.g., for 'max_spread_pp': 'Set to 2 to require tight books — anything wider eats most plausible edges.' It also explains the interplay of 'min_partition_leg_kelly' with partition arbs. This extra context elevates the score.

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

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

The description clearly states the tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It uses specific verbs ('scan', 'return') and identifies the resource ('Polymarket markets'). It also distinguishes itself by saying it's built for 'what should I bet on today' and avoids paging through hundreds of markets. This differentiates it from sibling tools like 'polymarket_arbitrage' and 'bet_research'.

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

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

The description implies usage for discovering betting opportunities without explicitly stating when not to use or naming alternatives. It says 'Built for what should I bet on today — agents discover opportunities without paging hundreds of markets,' which provides context but no exclusions. While clear, it lacks explicit guidance on when to prefer this over other tools.

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

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

Annotations already declare read-only, idempotent, non-destructive. Description adds useful behavioral details: 60-day TTL, daily close data, meaning of gaps. 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?

Front-loaded with key question, then response structure, then limits. Some redundancy (e.g., the 'fresh vs old' example could be condensed) but overall efficient.

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

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

No output schema, so description fully explains response fields (tracked, expired, snapshot_dates) with details. Parameters explained. Annotations cover safety. Completeness is high for this complexity level.

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 restates parameters with defaults and valid range in the 'Args:' line, adding some context but not significantly beyond schema. 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?

Description uses specific verb 'answers' and resource 'edge persistence and decay telemetry'. Clearly distinguishes from sibling 'polymarket_edges' by focusing on historical persistence rather than current snapshots.

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

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

Explicitly states the question it answers, guiding when to use. Does not explicitly list alternatives or when not to use, but context from sibling list and the example of fresh vs old edges imply usage scenarios.

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

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

The description details behavioral traits beyond annotations: it 'walks the ladder', returns specific fields like top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict. For basket mode, it explains per-leg fill detail, thin_legs[], and forced directional risk. Annotations already indicate readOnly, idempotent, non-destructive, which are consistent.

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

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

The description is well-structured with clear sections, front-loaded with the essential requirement (REQUIRES one of market or event). It uses bullet-like format for modes. While comprehensive, it is slightly verbose but every sentence adds value. Minor trim could improve conciseness.

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 with two modes and 4 parameters, the description is complete. It explains all output fields in detail, despite no output schema. It covers risks (thin books, partial fills, forced directional risk) and provides usage context. The definition fully equips an agent to select and invoke the tool correctly.

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

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

Schema coverage is 100% with descriptions for all 4 parameters. The description adds significant value by explaining how size_usd is interpreted differently in basket mode (settlement notional, shares per leg) and the auto default for side in basket mode. It also clarifies default values and clamping range.

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 a 'realizable-vs-theoretical edge check against live CLOB order-book depth', specifying two distinct modes (single-market and basket). It uses specific verbs ('check', 'returns') and distinguishes itself 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 is provided: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also explains when not to use it by noting that theoretical overround on thin books is not capturable and warns about partial fills converting arb to unhedged positions.

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 fully discloses behavioral traits beyond annotations. It explains that pre-mapped topics often return compatibility warnings, details safety fields (compatibility_warning, temporal_alignment, skipped counters), and clarifies that real spreads are rare. Annotations already indicate readOnly and idempotent, which are consistent.

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

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

The description is long but well-structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS). Every sentence serves a purpose. Front-loading with the core function helps. Minor room for tighter phrasing, but overall efficient.

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

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

Given no output schema, the description compensates by detailing response fields (leg-by-leg prices, spread array, compatibility_warning, temporal_alignment, skipped counters). It covers edge cases (non-equivalent bet shapes, temporal misalignment) and explains counters. Fully adequate for a tool with 3 parameters.

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 the distinction between 'topic' and explicit event tickers, providing the full list of topic shortcuts, and explaining that explicit overrides override the topic-mapped side. This goes beyond the schema's property descriptions.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes itself from siblings (e.g., polymarket_arbitrage, polymarket_edges) by focusing on cross-venue comparison, with explicit mention of two modes (topic shortcuts and explicit event tickers).

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

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

The description explains two usage modes and provides warnings about when spreads are meaningful (e.g., compatibility_warning, temporal alignment). However, it does not explicitly contrast with sibling tools or state when not to use it. Context is clear but exclusions are implicit.

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

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

Annotations already cover readOnly, openWorld, idempotent, and non-destructive nature. Description adds return shape compatibility with page_summary, which is useful but not essential. 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, no fluff. First sentence defines purpose, second adds usage context and return format reference. Efficient and well-structured.

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

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

Given low complexity, rich annotations, and reference to page_summary for output, description covers most needs. Only gap is parameter documentation, which is minor for a simple 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 0% and description omits meaning of 'lang' and 'project' parameters. Only an example given, leaving agent guessing. Description should at least indicate that lang is language code and project is Wikipedia project type.

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

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

Clearly states the tool picks a random Wikipedia article summary, provides example uses ('surprise me'), and implicitly distinguishes from page_summary by highlighting randomness. The verb 'pick' and resource 'random Wikipedia article summary' are specific.

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

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

Explicitly mentions when to use ('give me a random Wikipedia article', 'surprise me', seeding exploration) and notes the return format matches page_summary, aiding alternative selection. Lacks explicit when-not-to-use, but 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?

Description adds value beyond annotations (scoping, listing all keys, pairing context), and 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?

Three front-loaded sentences, each essential, no fluff.

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

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

Covers purpose, usage, scoping, and pairing well; lacks return format description but acceptable for a simple retrieval tool.

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

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

Schema covers parameter; description adds context that key is saved via remember and omitting lists keys, enriching meaning.

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

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

Description clearly states it retrieves a saved value via remember or lists keys, and 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?

Explicitly tells when to use (look up context stored earlier) and pairs with remember/forget, but does not include when-not-to-use scenarios.

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

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

Annotations indicate read-only, idempotent, non-destructive. Description adds 'mark_read:true flags events as read so next call shows newer ones', disclosing a side effect beyond the annotations. Also confirms polls work fine.

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

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

Four sentences, each valuable. First sentence states purpose, second lists return fields, third explains parameters, fourth provides alternative access. No fluff, 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?

Despite no output schema, description tells what fields are returned (source, citation_uri, raw payload). It covers filter options, mark_read behavior, and polling suitability. Provides alternative endpoint for programmatic access.

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

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

Schema covers 100% of parameters. Description adds meaning beyond schema by explaining the intent of filter parameters (type, since) and the effect of mark_read. Does not redundantly restate schema.

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

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

The description clearly states the verb 'Pull fired events from your subscription feed' and specifies what is returned (source, citation_uri, payload). It distinguishes from siblings by focusing on recent alerts with filtering options.

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

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

Mentions polling works fine and gives an alternative REST endpoint for scripts/dashboards. Provides usage context but does not explicitly compare to sibling tools like list_subscriptions or recent_changes.

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

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

Annotations already indicate read-only and idempotent behavior. The description adds useful behavioral traits: fanning out to multiple sources, fallback from GDELT to GNews on rate limits, and USPTO soft-fail due to API sunset. No contradiction with annotations.

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

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

The description is a single dense paragraph but efficiently conveys all necessary information. It is front-loaded with example queries and includes no fluff. However, structuring it into bullet points could improve readability.

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

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

Given the tool's complexity (multiple data sources, fallback logic, date parsing), the description covers all essential aspects: sources, fallback behavior, parameter details, return structure (changes[], total_changes, URIs), and alternative tool usage. Annotations and full schema coverage complement this well.

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

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

Schema already provides descriptions for all parameters (100% coverage). The description adds value by clarifying the 'since' parameter formats and acceptable values, and explaining that 'value' can be ticker or CIK. It also gives context through examples.

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 'change feed for a company in the last N days/weeks/months in ONE parallel call' with specific example queries. It distinguishes from sibling 'entity_profile' by specifying use cases.

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 when-to-use scenarios ('What's new with X' etc.) and directs to 'use entity_profile instead when you want the static profile'. It also includes guidance on 'since' parameter format and typical values.

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 idempotentHint=true and not destructive. The description adds behavioral context: 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours.' and 'Stored as a key-value pair scoped by your identifier.' 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 (80 words, 4 sentences) and front-loads the purpose. Every 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?

The description covers purpose, usage, behavioral details, and companion tools. It lacks details on idempotency behavior (e.g., overwriting existing keys) but is adequate for a simple store tool with good annotations.

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

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

Schema coverage is 100% with clear descriptions for both parameters. The description provides additional context about scoping and gives examples of keys, but does not add significant new semantics beyond the schema.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later — across this conversation or across sessions.' It uses a specific verb ('save') and resource ('data to reuse'), and distinguishes itself from siblings like '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?

The description provides explicit usage guidelines: 'Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again.' It also directs to companion tools: 'Pair with recall to retrieve later, forget to delete.'

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 explaining cascading internal lookups, auto-disambiguation, and the return format (including 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 well-structured with examples upfront, but could be slightly more concise. It efficiently covers purpose, usage, and return details in one paragraph.

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

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

For a 2-parameter tool with no output schema, the description is highly complete. It explains the supported entity types, input formats, and what each returns (including citation URIs). No gaps.

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

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

Schema coverage is 100% with descriptions for both 'type' and 'value'. The description adds meaning by listing specific input examples (ticker, CIK, name) and explaining how disambiguation works, going 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 IDs, with specific examples ('ticker for', 'CIK for', 'RxCUI for'). It distinguishes from siblings by positioning it as the first step when needing an ID, which is unique among the listed tools.

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

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

The description explicitly says 'Use FIRST whenever you have a name but need an ID', providing clear when-to-use guidance. It also mentions that one call replaces 2-3 manual lookups. However, it does not specify when not to use it or list alternatives like entity_profile for richer data.

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

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

Annotations already indicate readOnly, idempotent, openWorld. Description adds that it probes each entity, ranks by score, and returns score, confidence, signal density per entity. No contradictions.

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

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

Three concise sentences: purpose, mechanism, use case. No redundant words. Information is front-loaded and earns its place.

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

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

Despite no output schema, the description explains return format (ranked list with score, confidence, signal density). Covers parameters, behavior, and output adequately. No gaps given complexity.

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

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

Schema has 100% coverage. Description adds that first entity is treated as subject and rest as competitors, which is not in schema. Other parameters (models, apiKey, context) are sufficiently described in 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 compares AI visibility across multiple entities side-by-side, using ai_visibility_check, and ranks results. It distinguishes from sibling ai_visibility_check by focusing on multi-entity comparison.

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

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

Provides an explicit use case (competitive AI-marketing audits) and example question. Implies that for single entity checks, use ai_visibility_check. However, no explicit when-not-to-use or other 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?

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint=false), the description adds critical behavioral details: it fans out to two external sources, bundlephobia's first measurement can take 5-30s, partial failures degrade gracefully with sources_failed listing timeouts. This gives the agent a realistic understanding of latency and error handling.

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

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

The description is a single paragraph but well-organized with purpose first, then alternates, then ecosystem scope, then partial failure handling. It is reasonably concise for the complexity, though it could benefit from bullet points for the returned fields. All sentences are earned.

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

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

Despite no output schema, the description thoroughly explains return values (summary block with all fields, per-advisory detail, links, alternative versions). It covers degeneracy (partial failures, sources_failed). Given the complexity of the tool, this is highly complete.

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

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

Schema covers both parameters with descriptions (package name with scoped packages, version defaults to latest). The description does not add extra meaning beyond the schema. With 100% schema coverage, baseline is 3. No further elaboration is needed.

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

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

The description clearly states it's a composite check for evaluating an npm package, combining deps.dev (license, advisories, version history) and bundlephobia (bundle size, dependency count, ESM/tree-shake support). It explicitly mentions the ecosystem limitation (NPM only v1) and distinguishes from sibling tools by specifying that other ecosystems fall under a different tool (deps.dev:version). The verb 'scan' and resource 'dependency' are well-defined.

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

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

The description provides clear usage scenarios: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also mentions partial failures and graceful degradation. However, it does not explicitly state when NOT to use (e.g., for non-npm packages beyond the mention of ecosystem versioning). The sibling tools list includes many unrelated tools, but the description acknowledges alternatives for other ecosystems.

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 many behaviors beyond annotations: embedding model (BGE-base-en), chunking strategy (500-char overlapping windows), character cap (200K chars with truncation flag), and return fields (passages with offsets and similarity scores). Annotations already cover read-only and idempotent; description adds functional details.

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

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

Every sentence adds value. Front-loaded with tool action and use case. No redundancy. Technical details compactly provide essential behavioral info.

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

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

Despite lacking output schema, description explains return format (top-N passages with offsets and scores). Covers all needed aspects: purpose, usage, parameter semantics, behavior, limitations (cap), and integration with sibling 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%, yet description adds value: for 'limit' it specifies range (1-20) and default (5) not in schema; for 'query' provides natural-language examples; for 'text' confirms max chars. Significantly enriches 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 the tool performs 'semantic search INSIDE a fetched record' with a specific verb and resource. It distinguishes from siblings like ask_pipeworx_grounded by describing the pairing 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 states when to use: 'Use when the record is too big to cram into the prompt.' Provides alternative guidance by mentioning pairing with ask_pipeworx_grounded. Covers context-saving benefit and return characteristics.

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?

Disclosures beyond annotations: auth requirements, type-specific details, delivery caps and webhook behavior. Annotations indicate idempotentHint=true, but description does not address idempotency; however, it adds significant 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?

Front-loaded with purpose and then detailed; each section adds value. Slightly lengthy but justified by complexity.

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

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

Given the complexity (3 params, nested objects, no output schema), the description covers all crucial aspects: types, delivery, requirements, limitations. Minor gap on idempotency, but overall very complete.

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

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

Schema coverage is 100%, and description adds rich examples and constraints for each parameter (e.g., item codes, topic, webhook signing). Greatly 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?

Clearly states 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' This is a specific verb+resource, and it distinguishes from sibling tools like list_subscriptions and unsubscribe.

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

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

Provides substantial guidance: requires OAuth account, lists supported types and delivery channels, and mentions prerequisites like phone verification. Does not explicitly exclude alternatives, but context is clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds behavioral context beyond annotations by detailing the return format (category-bucketed examples with tool+argument shapes) and the effect of the optional topic parameter. 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 packs essential information: purpose, usage examples, parameter details, and return format. It is front-loaded with trigger phrases and structured with examples. A slightly more streamlined presentation could improve conciseness, but it remains effective.

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

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

Given the absence of an output schema, the description fully explains what the tool returns: category-bucketed example questions with tool+argument shapes. It covers usage context (first-time onboarding), parameter options, and the tool's role among siblings. With strong annotations, the description is complete and leaves no significant gaps.

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

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

The schema has one optional parameter 'topic' with a description listing possible values. The description adds value by explicitly listing the focus areas (e.g., 'finance', 'pharma') and explaining that omitting the parameter yields a cross-category spread. This clarifies usage 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 identifies the tool as an onboarding entry point that returns category-bucketed example questions with tool+argument shapes. It uses specific verbs like 'returns' and lists example topics, making the purpose unmistakable and distinguishing it from sibling tools like 'discover_tools'.

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

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

The description explicitly advises using this tool FIRST when uncertain about Pipeworx's capabilities and to learn how to call meta-tools. It explains that calling with no arguments gives a full spread, and passing a topic focuses the results. It does not explicitly state when not to use it, but the guidance is clear and contextual.

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 soft-delete behavior (deactivated, not deleted) and connection to recent_alerts. Annotations provide readOnlyHint and destructiveHint, but description adds important context beyond them.

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

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

Three sentences, front-loaded with the main action, 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?

Covers purpose, ownership constraint, behavioral impact, and relation to historical events. Complete for a simple tool with one parameter 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 coverage is 100% with clear description for id parameter. Description adds minor context ('returned by subscribe'), but 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?

Clear verb 'Cancel' and resource 'subscription by id'. Distinguishes from sibling tools like subscribe and list_subscriptions.

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

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

Explicitly states ownership enforcement, guiding when to use. Lacks explicit alternatives, but context is clear.

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

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

Annotations indicate readOnlyHint and idempotentHint, and the description adds behavioral details about return format (verdict, structured form, citation, delta) and data sources (SEC EDGAR + XBRL), enhancing 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 concise, front-loaded with user query patterns, and every sentence adds value—no redundancy or fluff.

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

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

Given the single parameter and no output schema, the description fully covers what the tool does, when to use it, its scope, and what it returns, making it contextually complete.

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

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

The single parameter 'claim' is well-described in the schema with examples, and the description adds context but no additional semantic depth beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: natural-language claim verification against authoritative sources, with specific examples and scope (company-financial claims for US companies). It distinguishes itself from siblings by its specific function.

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 clear guidance to use when checking factual accuracy, but does not explicitly mention when not to use or compare to alternatives. The scope limitation is noted.

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