Urlscan Io

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds value by detailing the return format (per-model {score, confidence, signals, raw_response} + combined view), API key handling, and cost implications. 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 three sentences, front-loaded with the core purpose, and every sentence adds value. No superfluous words.

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

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

Given 4 parameters, no output schema, and annotations covering non-destructive/read-only, the description adequately explains what the tool does, how to use it, and what it returns. It is complete for its complexity.

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

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

Schema description coverage is 100%, but the description adds context by explaining default model behavior, the optional API key purpose, and providing examples for entity and context. This enriches 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 probes LLMs for visibility about a business/brand/product/topic and returns a score. It specifies the verb 'probe' and the resource 'LLMs,' and distinguishes from sibling tools like entity_profile and scan_competitor_ai_presence.

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

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

The description provides explicit use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains the default model and BYO key for Anthropic. It could be more explicit about when not to use, but the context signals list many 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 readOnly, openWorld, idempotent, and non-destructive hints. The description adds context about internal routing, argument filling, citation URIs, and tier/performance characteristics ('works on every tier, one fast call'). No contradiction with annotations; some gaps remain (e.g., error handling when no tool matches) but overall disclosure is strong.

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

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

The description is long but dense and well-structured: opening preference statement, domain list, behavior, trigger phrases, examples, entry-point status, and step-up alternatives. Each sentence contributes distinct value, though some trimming of repetitive examples could tighten it further.

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

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

There is no output schema, but the description explains the return format (structured answer with citation URIs). It covers when to use, what it does, how it differs from alternatives, and provides concrete examples. Given the tool's broad scope, the description is highly complete and self-contained.

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

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

The input schema is fully descriptive (100% coverage), defining 'question' and aliases with a clear explanation. The description adds examples of acceptable questions and reinforces natural-language usage, but does not add new parameter-level semantics beyond the schema. Baseline 3 is appropriate since 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 purpose: it routes queries to the appropriate tool among 5,462 across 1,419 sources, fills arguments, and returns structured answers with citations. It explicitly distinguishes itself from siblings like ask_pipeworx_grounded and deep_research, and positions itself as the default entry point ('START HERE').

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

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

The description provides extensive usage guidance: when to prefer it over web search, trigger phrases ('what is', 'look up', 'get the latest'), specific domains, and step-up alternatives (ask_pipeworx_grounded for hallucination-resistant answers, deep_research for broad/multi-part questions). It also addresses breaking-news use cases.

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

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable context about the experimental nature: 'candidate routing improvements enabled live', 'currently matches ask_pipeworx exactly', and 'falls back to nothing — this IS a full working router'. This goes beyond annotations but does not disclose potential failure modes or rate limits, so a 4 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 well-structured and front-loaded with the core identity ('Beta version of ask_pipeworx'). Each sentence adds meaningful context (current state, usage, comparison, fallback). It is slightly long but each part earns its place, so a 4 is warranted.

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

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

For a complex beta router with no output schema, the description covers what the tool is, its current state, how to use it, and how it relates to the stable version. It references ask_pipeworx for the response shape, which is sufficient given the sibling context. It could have elaborated on response details, but the reference to 'same response shape' adds enough.

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

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

The input schema has 100% coverage with detailed descriptions for the 'question' parameter and its aliases. The description only says 'same arguments' without adding further parameter-level detail, so it relies on the schema. This meets the baseline 3 for high schema coverage.

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

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

The description clearly states it is a beta version of ask_pipeworx, an identical universal router with the same tools, arguments, and response shape. It distinguishes itself from the stable sibling by highlighting experimental routing improvements, so its purpose is specific and differentiates it from ask_pipeworx.

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

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

The description explicitly says to 'Use it exactly like ask_pipeworx when you want the newest routing' and notes that results are compared against the stable router. This gives clear when-to-use guidance and references the stable alternative, satisfying the usage dimension fully.

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 this as read-only, open-world, idempotent, and non-destructive. The description adds crucial behavioral details: it extracts answers strictly from tool results, includes a refusal mechanism with specific reasons, and returns a structured evidence object. This goes beyond the annotations and fully discloses edge-case behaviors.

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?

Though longer than a one-liner, every sentence earns its place: purpose, mechanism, success/refusal output shapes, use cases, and cost trade-off. The description is front-loaded with the core purpose and structured clearly, making it easy to scan.

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

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

The description covers all essential aspects: what it does, how it routes, output format on success, refusal reasons on failure, and when to use it. Since there is no output schema, the description fully compensates by listing return fields. It is complete for a tool of this complexity.

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

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

Schema coverage is 100%: all six parameters are aliases for 'question' and each has a description. The description does not add parameter-specific semantics beyond the schema, which is already self-explanatory. This meets the baseline for high coverage but does not exceed it.

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

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

The description clearly states the tool's function: a hallucination-resistant answer mode that extracts answers from tool results with evidence. It distinguishes itself from sibling ask_pipeworx by emphasizing grounding and explicit refusal behavior, making its unique value obvious.

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

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

Provides explicit guidance on when to use this tool: for high-stakes reads where answers will be quoted, cited, or acted on. It also names the alternative (ask_pipeworx) and advises preferring it for casual lookups, including the cost difference of an extra LLM call.

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

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

The description goes far beyond annotations by detailing internal behavior like fan-out examples, resolver contract, parent event extraction, news fallback, safety short-circuits, and resolution-rule risk. It is fully transparent and does not contradict any annotations.

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

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

The description is very lengthy (multiple paragraphs) and contains extensive detail. While each piece adds value, it lacks conciseness. A more structured or summarized format could improve readability without losing information.

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

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

The description covers all essential aspects: input format, process, output shapes, edge cases (low confidence, closed markets, wide spreads), cancellation rules, and news fallback. Despite no output schema, the description fully compensates by detailing the response structure.

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

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

With 100% schema description coverage, the description still adds significant value by explaining the nature of 'market' (slug, URL, question text), the effect of 'depth' (quick vs thorough), and the implications of 'include_raw' (response size vs detail). This enriches the parameter understanding.

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

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

The description clearly states the tool researches Polymarket bets by pulling Pipeworx data. It specifies the verb ('research'), resource ('Polymarket bet'), and provides concrete usage examples. It distinguishes from sibling tools like ask_pipeworx and polymarket_arbitrage by focusing on comprehensive bet research with evidence packets.

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 use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and provides examples. It does not explicitly exclude cases or mention alternatives, but the context is clear enough for agents to decide when to invoke it.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds significant behavioral context: for 'company' it pulls latest 10-K financials and handles off-calendar fiscal years; for 'drug' it pulls specific counts. It also mentions results are sorted by primary metric and returns 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 one well-structured paragraph that front-loads usage examples, then explains behavior for each type, then notes sorting and output. Every sentence serves a purpose with 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 tool has two distinct entity types, two parameters, and no output schema, the description covers what data is returned (paired financials or counts) and how results are sorted. It could optionally detail the exact output format, but the provided level is sufficient for an agent to understand the tool's functionality.

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 goes beyond the schema by explaining what data each 'type' value retrieves and how 'values' should be formatted (tickers/CIKs vs. drug names) with examples. This adds meaningful semantics above the schema's enum and min/max annotations.

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

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

The description explicitly states it performs side-by-side comparisons of 2–5 companies or drugs, with specific data sources for each type (SEC filings for companies, FAERS/FDA/trials for drugs). It clearly distinguishes from sequential single-entity lookups by noting it 'Replaces 8–15 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 provides clear when-to-use guidance with example phrases (e.g., 'which is bigger') and explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It does not explicitly state when not to use, but the context is sufficient for most cases.

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

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

Annotations already declare readOnly/openWorld/idempotent; description goes far beyond by disclosing account/payment requirements, parallel routing, findings packet format, gaps[] for unanswered facets, never-invented evidence, contradictions[] for standard/thorough, citation_uri fetchability, semantic excerpting, and latency. No contradiction with annotations.

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

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

Long but appropriately dense for a complex tool. Front-loaded with account requirement and the core differentiator ('this is NOT open-web search'). Section flows logically: prerequisites → function → use cases → alternatives → depth semantics → return format → performance. Some redundancy with schema depth descriptions, but no wasted sentences.

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

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

Given no output schema, the description thoroughly explains return values (findings packet with verbatim evidence, confidence, source, fetched_at, citations, gaps[], contradictions[], hop field), timing, prerequisites, and edge cases (empty gaps for non-structured topics). It is self-sufficient for an agent to invoke correctly.

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

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

Schema already provides 100% coverage for both parameters, but the description adds critical missing context: 'depth:"thorough" needs a paid plan' and a richer explanation of how depth affects behavioral outcomes (gap recovery, contradiction scan, lead-chasing). This goes beyond the schema's enum 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 what the tool does: 'Grounded multi-source research across Pipeworx's 1419 STRUCTURED data sources... in ONE call' and explains the decomposition/routing mechanism. It distinguishes itself from siblings like ask_pipeworx by noting it is NOT open-web search and is for broad/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?

Provides explicit when-to-use versus alternatives: 'For a single lookup use ask_pipeworx', 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx', and 'If you are not signed in, use ask_pipeworx instead — it works on every tier.' Also gives example research questions and depth-level guidance.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds useful context about output format: returns top-N tools with names, descriptions, full input schemas, and curated examples, ready to call directly.

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

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

Concise, front-loaded with purpose, then lists examples, then provides usage guidance. Every sentence adds value with 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?

Despite no output schema, the description fully explains what the tool returns (top-N relevant tools with details) and how to use it (as an initial discovery step). It covers key behavioral aspects like 'ready to call directly' and 'no second schema lookup needed.'

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

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

Schema coverage is 100% with descriptions for all parameters. The description provides examples of valid query values (e.g., 'analyze housing market trends') and explains the main parameter and its aliases, offering value beyond the schema.

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

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

The description clearly states the tool finds tools by describing data or task, lists specific domains, and explains it returns relevant tools with full schemas. It distinguishes itself from sibling tools that perform different functions.

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

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

Explicitly instructs 'Call this FIRST when you have many tools available and want to see the option set (not just one answer),' providing clear when-to-use guidance and contrasting with tools that return single answers.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds transparency by stating no API key needed and specifying return fields (URL, verdict, screenshot link, scan date), which is useful beyond annotations.

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

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

Single sentence, front-loaded with action and resource, no wasted words. Highly concise and structured.

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

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

Given the tool's simplicity (1 parameter, output schema present), the description covers the purpose, prerequisites, and return value highlights. It is complete for an agent to use correctly.

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

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

Schema description coverage is 0%. The description adds 'specific domain' but no format or constraints beyond the parameter name. It provides minimal added semantics.

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

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

The description clearly states the verb 'search', the resource 'urlscan.io past scan results', and the target 'specific domain'. It distinguishes itself from sibling tools by focusing on domain-specific lookup.

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 mentions 'no API key required' which is a helpful prerequisite, but it does not provide when to use this tool versus alternatives or any exclusions. It lacks explicit usage guidance.

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

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. Description adds that patents 'soft-fails until reactivated', news uses GDELT→GNews fallback, and fundamentals sorted by period_end DESC. 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 a single dense paragraph; front-loaded with query examples, but could benefit from bullet points or structured sections for readability. Still concise given the information density.

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 (multiple sources, soft-fail, fallback, no output schema), the description covers all key aspects: data sources, return fields, and limitations. Sibling tools provided for context.

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

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

Schema coverage is 100%. Description adds that 'type' currently only supports 'company' (person/place coming soon), and 'value' accepts ticker or zero-padded CIK but not names, supplementing 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 provides a 'full cross-source profile of a US public company in ONE parallel call', with example queries and a detailed list of returned data (CIK, recent filings, fundamentals, patents, news, LEI). This distinguishes it from siblings like 'resolve_entity' for names.

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

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

Explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' for holistic views, and warns that names are not supported, directing to 'resolve_entity' first. Provides clear when-to-use and when-not-to-use guidance.

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

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

Annotations already indicate destructiveHint=true and readOnlyHint=false. The description adds context about clearing sensitive data and stale context, but does not disclose other behavioral details like irreversibility or permission requirements. 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 extremely concise: one sentence for purpose followed by one sentence for usage guidance. No redundant information, and the key action is front-loaded.

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

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

Given the tool's simplicity (one required parameter, no output schema, no nested objects), the description fully covers purpose, usage, and behavioral context. No gaps remain.

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

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

Schema coverage is 100% and the schema already describes the 'key' parameter. The description does not add semantic value beyond stating 'by key', so it meets the baseline for high coverage without additional insight.

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', identifying the action (delete) and resource (memory by key). This clearly distinguishes from sibling tools like 'remember' and 'recall'.

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

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

The description provides specific conditions for use: 'when context is stale, the task is done, or you want to clear sensitive data'. It also advises pairing with 'remember' and 'recall', offering clear guidance on when to use this tool over alternatives.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint as true and destructiveHint as false. The description adds key behavioral details: it fetches an external URL, extracts content, and outputs a standard format text blob. This fully informs the agent about side effects (network call, non-destructive, idempotent) and output nature.

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: the first delivers the core purpose and benefit, the second details the process, and the third lists use cases. Every sentence earns its place with 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?

The tool is simple with two parameters and no output schema. The description covers purpose, process, and output format. It could mention error handling or prerequisites (e.g., internet access), but given the tool's straightforward nature and rich annotations, it is sufficiently 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% with both parameters documented. The description adds marginal value beyond the schema (e.g., default 25 for max_links). Since baseline is 3 when coverage is high, this score reflects adequate but not exceptional added meaning.

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

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

The description uses specific verbs ('generate', 'fetches', 'extracts', 'emits') and clearly defines the resource (llms.txt for a URL). It explicitly distinguishes from siblings by describing a unique purpose (AI crawler indexing) and listing specific use cases, which helps an agent differentiate it from other audit tools.

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

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

The description provides three concrete use cases ('getting a client's site indexed...', 'drafting llms.txt for your own project', 'auditing... competitor'), offering clear context. It does not explicitly state when not to use or name alternatives, but the sibling list includes related tools, and the context is sufficient for an agent to infer 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds useful behavioral context like 'no API key required' and specifies the return type (scan records for pages hosted at or contacting that IP). 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 a single, front-loaded sentence with no wasted words. 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, the output schema exists, and the parameter is straightforward, the description provides all necessary context: what it does, how it works (no API key), and what it returns.

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 0% description coverage for the 'ip' parameter. The description only mentions 'specific IP address' without adding format details (e.g., IPv4/IPv6). For a simple string parameter, more context would help, but the parameter name and type are self-explanatory.

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

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

The description clearly states the tool searches urlscan.io past scan results for a specific IP address, with a specific verb and resource. It distinguishes from siblings like 'domain' by focusing on IP addresses.

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

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

The description provides clear context for when to use the tool (to search for an IP address) but does not explicitly mention when not to use it or alternative tools. It gives enough guidance for typical 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 declare read-only and non-destructive behavior. Description adds caller scope and return field details, fully disclosing behavior.

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

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

Two sentences, no wasted words, front-loaded with the core action.

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

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

For a simple list tool with no output schema, it covers purpose, return fields, and usage. Could optionally mention ordering or pagination but not necessary.

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 description does not need to add parameter details. It does not introduce new information beyond the schema for 'include_inactive'.

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

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

Clearly states it lists the caller's active subscriptions and enumerates return fields. Distinguishes from siblings like subscribe/unsubscribe by specifying review purpose.

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

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

Explicitly advises using this tool to review monitored items before adding more or to find an ID to cancel. Lacks explicit 'when not to use' but provides clear context.

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

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

The description adds valuable behavioral context beyond the all-false annotations: it discloses rate limiting ('5 per identifier per day'), confirms it is free and doesn't count against quota, and explains what happens to feedback ('team reads digests daily and signal directly affects roadmap'). It also warns not to paste the end-user's prompt, which is a non-obvious behavior.

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

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

Despite being longer than average, every sentence contributes essential information. The text flows logically from purpose ('Tell the Pipeworx team...') to usage cases ('Use when...') to exclusions ('ONLY for tools served...') to practical instructions ('Describe the issue...') and constraints ('Rate-limited...'). No redundancy or filler.

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

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

For a simple feedback submission tool with no output schema, the description fully covers all necessary context: what to report, when to use it, what not to report, how to phrase the issue, rate limits, quota impact, and post-submission processing. The nested context object is adequately explained in the schema, and the description reinforces its purpose.

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 provides complete descriptions for all parameters (100% coverage), including enum meanings. The description adds extra semantic guidance beyond the schema, such as 'Describe the issue in terms of Pipeworx tools/packs' and 'don't paste the end-user's prompt,' which clarifies how to use the message parameter effectively. This is a modest but meaningful addition over 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 function: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It also distinguishes this tool from siblings by explicitly scoping it to 'tools served by this Pipeworx connection' and instructing users to file issues with other MCP servers elsewhere.

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

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

Explicit when-to-use guidance is provided: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' It also gives a clear when-not-to-use case: 'if the tool came from a different MCP server... file it with that server instead.'

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 and idempotent. Description adds caching details (5min-1h), data source (CF analytics-engine), and privacy (no PII), complementing annotations well.

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

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

Concise three-sentence description with bulleted use cases; every sentence adds value, front-loaded with main purpose.

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

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

With one parameter, comprehensive annotations, and no output schema needed, the description fully covers what the tool returns, its caching, and context for using it.

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's enum values are fully explained with purpose (shorter windows for hot items, longer for steady-state), and default is mentioned, exceeding 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 returns trending tools, packs, and call volume on Pipeworx, distinguishing it from siblings like ask_pipeworx or search by focusing on aggregate usage data.

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

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

Explicitly lists three use cases (discovering hot data sources, confirming canonical choice, aligning use cases), providing clear guidance on when to use this tool.

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

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

The description extensively discloses behavior beyond annotations, including the scanning algorithm (monotonicity violations), partition checks (sum of YES prices ≈1), semantic anchor (Jaccard similarity ≥0.30), partition filter (placeholder slug rejection), and fill check (live CLOB depth analysis). It warns about limitations like deviating partition signals not being tradable if the book lacks depth.

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

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

The description is comprehensive but verbose (over 300 words). It is well-structured with clear sections for each mode, filters, and response format, but could be more concise. The front-loading of the purpose is 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 tool's complexity (two optional parameters, multiple modes, edge cases), the description fully covers all necessary context: input formats, internal logic, response structure (opportunities array, partition_check details), fill check, and limitations (placeholder filtering, similarity threshold). No output schema is present, so the description compensates by detailing return fields.

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?

Despite 100% schema description coverage, the description adds significant meaning: it explains that `event` accepts slugs or full Polymarket URLs and triggers single-event mode, while `topic` accepts seed questions and triggers cross-event scanning. It details the behavior of each parameter mode, including child market traversal and comparator execution.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It defines three distinct modes (trending_scan, event, topic) with specific use cases, making the purpose unambiguous and distinguishing it from sibling tools like polymarket_edges.

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

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

The description provides explicit guidance on when to use each mode: 'Call with NO args for a trending_scan... pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan.' It recommends event mode for specific markets and topic for cross-event scanning. It also advises against trading when realizable_edge_pp ≤ 0 and directs custom sizing to polymarket_fill_risk.

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

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

The description discloses behavioral traits beyond annotations, such as caching (1 hour at KV level), response structure including diagnostics, and caveats (e.g., Fed bets excluded, 24h move warning). Annotations already indicate read-only and idempotent behavior, and the description does not contradict them.

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

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

The description is lengthy but well-structured, front-loaded with the purpose, then segment details, knobs, and response structure. Every sentence adds value, and there is no repetition 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 tool's complexity (9 parameters, no output schema), the description is highly complete. It explains the response top-level fields and diagnostics, making it sufficient for an agent to understand inputs, outputs, and behaviors.

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

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

The input schema has 100% coverage, and the description adds significant meaning to each parameter. For example, it explains 'min_liquidity' and 'max_spread_pp' as tradeable-edge filters, and clarifies why 'min_partition_leg_kelly' exists due to partition arbs always returning kelly_fraction_half=0 at the parent level.

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 specifies the resource (Polymarket markets) and action (scan and return opportunities), and differentiates from sibling tools like polymarket_arbitrage by focusing on edge detection using Pipeworx data.

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

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

The description explicitly states it is built for 'what should I bet on today' scenarios and provides detailed knobs and filters for tradeable edges. While it gives clear context, it does not explicitly mention when not to use this tool or compare it to alternatives like polymarket_arbitrage.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds rich behavioral detail: returns time-series, trend classification, decay rate, expired opportunities, snapshot dates. Explains limitations (TTL, data gaps, decay from closes not intraday). 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 long but well-structured: first sentence states purpose, then explains response fields, then limits. Every sentence adds value, though slightly verbose. Front-loaded with key query.

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 fully explains the response structure (tracked, expired, snapshot_dates) and important constraints (TTL, data gaps). Covers all necessary context for correct invocation.

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

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

Schema covers both parameters with descriptions. Description adds default and max for 'days', and mentions window family options which are already in schema. Adds marginal value beyond schema so baseline 3 is appropriate.

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

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

Clearly states the tool provides edge persistence and decay telemetry from daily snapshots, distinguishing it from the sibling tool polymarket_edges which likely gives current edges. 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?

Explicitly answers the question of when to use this tool: to assess if an edge is new or old/decaying. Provides guidance that a fresh wide edge and an old wide edge are different trades, effectively excluding scenarios where only current edge is needed.

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

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

Annotations already mark readOnlyHint=true. Description reinforces this by describing a read-only check ('walks the ladder', 'returns verdict'). No contradiction; adds context about partial fills and forced directional risk.

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 (headings for modes, bullet-like return fields). Every sentence adds value; however, slightly verbose for a tool definition.

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

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

For a 4-parameter tool with no output schema, the description comprehensively lists return fields for both modes, explains risk (thin books, unhedged position), and provides usage thresholds. 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 has 100% coverage with parameter descriptions. Description adds even more: explains side default per mode, size_usd as 'settlement notional S' for baskets, and range clamp. Goes 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: realizable-vs-theoretical edge check against live CLOB order-book depth. It distinguishes between single-market and basket modes with specific actions and return fields.

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: before acting on polymarket_arbitrage SELL/BUY-EVERY-LEG signals or polymarket_edges trades above ~$500. Explains why partial basket fills convert arb to unhedged direction, the dominant loss mode.

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

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

The description goes far beyond annotations (readOnly, idempotent, etc.) by detailing safety fields (compatibility_warning, temporal_alignment, skipped counters), explaining when spreads are meaningless, and covering edge cases. No contradictions with annotations. This is exemplary 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 long and includes significant technical detail (e.g., skipped_cross_type counters) that could be condensed. It is front-loaded but could be more concise. A 3 is appropriate for being adequate but not streamlined.

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, safety fields, and parameter modes. It covers all necessary information for an agent to use the tool correctly, including warnings about temporal alignment and match compatibility. Complete for the tool's complexity.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the two modes, that 'topic' auto-maps to events, and that explicit params override. This extra context justifies a 4, though the schema already covers parameter definitions.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question, with two modes and a detailed response structure. However, it does not explicitly distinguish from sibling tools like polymarket_arbitrage, which could cause agent confusion. A 4 is appropriate as the purpose is clear but missing sibling differentiation.

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

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

The description provides explicit guidance on when to use each mode (topic shortcuts vs. explicit tickers) and warns that most pre-mapped topics may return compatibility warnings, indicating when not to rely on the tool. While it does not compare to sibling tools, the usage context is well-defined. Score 4 reflects strong guidance but no explicit alternatives.

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

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

Annotations already declare readOnlyHint and idempotentHint. Description adds scoping detail (anonymous IP, BYO key hash, account ID) and explains the dual behavior (retrieve vs list). 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?

Three sentences with no redundancy. Each sentence adds value: purpose, usage context, scoping, and companion tools. Well front-loaded.

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

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

Given the tool's simplicity and full annotation coverage, the description covers all needed aspects: operation modes, scoping, pairing with other tools.

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

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

Schema coverage is 100% for the single optional parameter. Description adds meaning by explaining the effect of omitting key (list all) and providing example values like 'user_research_topic'.

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

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

The description clearly states the tool retrieves saved values or lists keys, with examples (ticker, address, research notes). It distinguishes from sibling tools remember and forget explicitly.

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 says when to use (look up stored context) and mentions companion tools (remember, forget), providing clear context. Could be more explicit about when not to use but is adequate.

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 openWorldHint. The description adds useful behavioral details: returns specific fields (source, citation_uri, raw payload), explains the effect of mark_read on subsequent calls, and notes compatibility with polling. This enriches the understanding 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 relatively concise and front-loaded with the main purpose. Each sentence contributes unique information, and there is no redundant or filler content. It could be slightly more terse, 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 no output schema, the description adequately explains return values (source, citation_uri, raw event payload). It covers filtering options and behavioral flags. The tool's complexity is moderate, and the description provides sufficient context for an AI agent to use it 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 100% coverage with descriptions for all 5 parameters. The description adds context by providing an example for type ('sec_8k'), clarifying that since is an ISO timestamp, and explaining the behavior of mark_read. This adds meaning beyond the schema alone.

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

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

The description clearly states the tool pulls fired events from a subscription feed, which matches the name 'recent_alerts'. It specifies the resource (subscription feed) and verb (pull), but does not explicitly differentiate from sibling tools like 'list_subscriptions' or 'subscribe', so a slight deduction.

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 concrete usage guidance: filtering by type and since, using mark_read to manage read state, and mentions that polling works fine. It also notes an alternative access method. However, it does not explicitly state when to use this tool over siblings or when not to use it.

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), description details multi-source fan-out, GDELT→GNews fallback, PatentsView soft-fail, and return structure (changes[], total_changes, 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?

Description is slightly long but front-loaded with examples and covers many aspects efficiently. Minor room for improved structure without losing information.

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

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

Given no output schema, the description covers use cases, parameter behavior, source interactions, and edge cases (PatentsView soft-fail, fallback) comprehensively.

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 meaningful context: explains since format (ISO/relative) with recommendation, restricts type to 'company', and describes value as ticker or CIK.

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 defines the tool as a 'change feed for a company in the last N days/weeks/months' with example queries. It distinguishes from sibling 'entity_profile' by specifying when to use each.

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 example queries to guide usage, explicitly states when to use entity_profile instead (for static profiles), and notes fallback sources and limitations like PatentsView API sunset.

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

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

Adds significant value beyond annotations: explains key-value storage, user scoping, and persistence differences between authenticated (persistent) and anonymous (24 hr) sessions. 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?

Single efficient paragraph with no wasted words. Purpose is front-loaded, and structure flows logically from action to examples to storage details.

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

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

Despite no output schema, the description explains storage behavior, scoping, persistence, and references companion tools for full workflow. Sufficient for an agent to understand and use correctly.

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

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

Schema coverage is 100% with descriptions for both parameters. Description reinforces with usage examples (e.g., 'subject_property') and clarifies value flexibility, adding practical context 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 saves data for reuse across sessions, gives concrete examples of what to store, and explicitly names complementary tools (recall, forget) for differentiation.

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

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

Provides specific guidance on when to use (discoveries worth carrying forward) with examples, but lacks explicit when-not-to-use conditions. Mentions pairing with recall and forget for later operations.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering safety. The description adds behavioral context beyond annotations by stating 'Each call cascades through several lookup endpoints internally' and that company resolution is 'auto-disambiguated,' which informs the agent about potential complexity and built-in handling. It also discloses the citation URIs returned, enriching the behavioral profile without contradicting annotations.

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

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

The description is longer than average but well-structured: it starts with illustrative query examples, then states the purpose with a directive, and finally breaks down supported types with return details. Every sentence adds value, though the initial example list is a bit redundant with the later explanations. It could be slightly tighter, but the front-loaded purpose and clear formatting earn a strong 4.

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

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

With no output schema, the description properly explains return values for each type, including citation URIs. It also situates the tool in the larger workflow ('other tools require as input', 'replaces 2-3 manual lookups'). It does not cover error cases or behavior when no match is found, but for a straightforward resolver with two parameters, the description is largely complete.

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

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

The input schema has 100% coverage with descriptions for both parameters, giving a baseline of 3. The description goes beyond the schema by detailing what each type returns (e.g., 'ticker + 10-digit CIK + company_name' for company, 'RxCUI + ingredient + brand' for drug) and providing example inputs like 'ozempic' and 'metformin'. This adds practical meaning to the type and value parameters, justifying a score above baseline.

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

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

The description clearly states the tool's function: 'resolve a user-spoken NAME to the canonical/official identifier other tools require as input.' It also lists specific supported types (company, drug) and what identifiers they return (ticker, CIK, RxCUI). The directive 'Use FIRST whenever you have a name but need an ID' differentiates it from sibling tools like entity_profile or search, making its purpose unmistakable.

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

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

The description gives explicit 'when to use': 'Use FIRST whenever you have a name but need an ID', and notes that it replaces 2-3 manual lookups, establishing a strong recommendation for name-to-ID conversion. However, it does not explicitly mention 'when not to use' or name specific alternative siblings, so it lacks the full exclusion criteria needed for a 5.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. The description adds 'keyless' which may imply no API key needed, but is not elaborated. With annotations covering safety, the description provides minimal additional behavioral context.

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

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

The description is extremely concise at four words, front-loading the purpose. Every word is necessary, with 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?

For a simple tool with one parameter and an output schema (not shown), the description is minimal. It doesn't describe what the 'full result' includes or any edge cases. 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?

Schema coverage is 0%, so description must add meaning. The description clarifies that the uuid parameter is a 'scan uuid', which is not evident from the schema alone. This adequately explains the parameter's purpose.

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 a full result identified by scan uuid. 'Keyless' hints at authentication, but is somewhat ambiguous. It distinguishes from sibling tools, none of which are named 'result'.

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

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

No guidance on when to use this tool versus alternatives. No mention of prerequisites, exclusions, or context for using this over other scan-related tools.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the tool is safe and non-destructive. The description adds value by explaining the internal behavior: it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. It also mentions the return format (score, confidence, signal density). No contradiction with annotations.

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

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

The description is two sentences, front-loaded with the main action and then providing use case and return format. Every sentence adds value, and there is no redundancy or fluff. Perfectly concise for the information conveyed.

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 tool has no output schema, so the description appropriately covers the return format (ranked list with score, confidence, signal density per entity). It also explains the internal mechanism (probing each entity, ranking). Given the moderate complexity (4 params, all documented in schema), this description is complete and sufficient for an agent to understand what the tool does and what it returns.

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

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

The schema covers 100% of parameters with descriptions in the JSON schema itself. The description adds marginal value beyond the schema—it clarifies the first entity is treated as the subject, which is already in the schema's description of 'entities'. No additional parameter semantics are provided beyond what the schema offers.

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 compares AI visibility across multiple entities, using ai_visibility_check to probe each entity and ranks results. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (more general). The verb 'Compare' and resource 'AI visibility across multiple entities' are 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 explicitly mentions 'Useful for competitive AI-marketing audits' and provides a concrete example question. It implies usage for comparing multiple entities, and the sibling ai_visibility_check is the alternative for single-entity checks. However, it does not explicitly state when not to use or provide exclusions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds substantial behavioral context: fans out to deps.dev and bundlephobia, returns specific fields, partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed will list timeouts.

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 with a clear front-loaded purpose. It uses a list-like structure for return fields. While efficient, it could be slightly more structured (e.g., bullet points) for easier scanning.

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 carries full burden. It lists all summary fields, mentions per-advisory details, links, alternative versions, and explains failure behavior. This provides a complete picture of what the tool returns and how it behaves.

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 extra value beyond schema: clarifies that scoped packages (e.g., @types/node) are accepted and that version defaults to latest when omitted.

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 is a composite check for npm packages covering license, advisories, size, and tree-shake support. It distinguishes itself from sibling tools by being specific to dependency scanning; no other sibling tool covers this domain.

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 usage context is given: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' It also notes the NPM-only scope in v1 and directs to deps.dev for other ecosystems, providing alternative guidance.

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

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

Annotations already cover readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds the term 'keyless,' which is ambiguous and not explained. It fails to detail behavioral traits such as pagination, result ordering, or limitations.

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

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

The description is extremely short (6 words), but this brevity sacrifices clarity. While there is no superfluous text, the description is under-specified and fails to earn its place by providing necessary context.

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

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

Given 3 parameters, an output schema, and many sibling tools, the description is grossly incomplete. It does not explain the meaning of 'past scans' or 'keyless,' nor how the parameters influence results. The output schema exists but the description adds no context about return values.

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

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

Schema description coverage is 0%, meaning the input schema provides only types and examples, but no property descriptions. The description adds no parameter information, leaving the agent to guess the purpose of 'size,' 'query,' and 'search_after'.

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 'Search past scans (keyless)' is vague. It identifies the resource as 'past scans' but lacks a specific verb (searching is implied by the name). It does not differentiate from sibling tools like 'search_within' or 'scan_competitor_ai_presence'.

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

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

No guidance provided on when to use this tool versus alternatives. The description does not mention any context, prerequisites, or exclusions, leaving the agent without direction.

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

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

The description discloses key behavioral traits beyond annotations: truncation at 200K chars, embedding model (BGE-base-en), cosine similarity, 500-char windows, character offsets, and a truncation flag. There is no contradiction with the read-only, idempotent, non-destructive 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 the core action and packs in essential details (limits, model, offset, pairing). Every sentence is informative and no words are wasted.

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

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

Given the tool's three parameters, complete schema descriptions, and rich annotations, the description fully covers behavior, limits, use cases, and integration with a sibling tool. It is self-contained for an agent to invoke correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by providing natural-language query examples and explaining the pairing with 'ask_pipeworx_grounded', which enhances understanding beyond the schema's parameter descriptions.

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

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

The description specifies 'semantic search INSIDE a fetched record', clearly distinguishing it from general search tools like 'search'. It provides concrete examples (SEC 10-K, article) and names a sibling tool ('ask_pipeworx_grounded'), making the purpose unambiguous.

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

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

The description explicitly states when to use the tool ('when the record is too big to cram into the prompt') and names an alternative tool for grounding. It does not exhaustively list exclusions or when not to use it, but provides strong contextual 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?

Description says 'Submit a new URL' which implies creating a new scan record, contradicting the annotation readOnlyHint: true indicating a read-only operation. This is a serious inconsistency.

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 very concise with one sentence. However, it sacrifices necessary detail for brevity.

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

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

Given 7 parameters and low schema coverage, the description fails to provide essential context about authentication usage (e.g., how to provide the key) and parameter meanings. Output schema is present but not referenced.

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 only 29% and the description adds no additional information about parameters beyond the schema. Several parameters like 'tags', 'referer', 'useragent', 'customagent' are left unexplained.

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

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

Description clearly states the action 'Submit a new URL for scanning', which is specific and uses a verb-resource pair. However, it does not differentiate from sibling tools that may also involve scanning, such as 'scan_competitor_ai_presence' or 'scan_dependency'.

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

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

Description mentions 'requires key' but provides no guidance on when to use this tool versus alternatives like 'search' or other scanning tools. Lack of context for usage decisions.

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

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

The description adds behavioral context beyond annotations: requires OAuth account, phone verification for SMS, rate limit on SMS (10/day), and that the feed is always on. It does not contradict annotations; idempotentHint=true might be slightly at odds with 'Returns the new subscription id' implying creation, but that is a nuanced point. Overall, good 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 detailed but well-organized. It front-loads the purpose and return value, then systematically covers requirements, supported types with examples, and delivery channels. All sentences earn their place. Could be slightly more concise in the delivery section but overall good.

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

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

The description covers the tool's purpose, prerequisites, type parameters, delivery options, and return value (subscription id). Given the complexity of the input (nested objects, multiple types) and no output schema, it provides enough context for an agent to use correctly. It could mention the response format more fully, but the explicit mention of subscription id is adequate.

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 substantial meaning: it explains each subscription type with examples (e.g., sec_8k items codes, polymarket_edge topics), and details delivery channels with constraints (SMS cap, phone verification, webhook secret). This goes well beyond the schema's minimal 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 creates a proactive monitoring subscription, specifies the resource (live-data event stream), and lists supported types and delivery channels. It distinguishes from sibling tools like 'unsubscribe' and 'list_subscriptions' that perform related but different operations.

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

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

The description includes usage context (requires OAuth account) and explains the behavior for different subscription types. It does not explicitly state when to use this tool versus alternatives, but the use case is clear from the verb 'create' and the context of monitoring. Siblings are mentioned but not contrasted.

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

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

The description adds significant context beyond annotations: it explains that output includes category-bucketed examples with exact tool+argument shapes drawn from a live catalog, and that calling with no arguments returns a full spread. This complements the readOnlyHint and idempotentHint annotations perfectly.

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 tightly packed with valuable information. It front-loads with query examples and uses dashes to structure content. Minor redundancy could be trimmed, 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 the tool's simplicity (one optional parameter, no output schema), the description fully covers return format, topic filtering, usage guidance, and relationship to meta-tools. No gaps remain for an agent to effectively use the 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?

The parameter schema has 100% coverage, but the description enriches it by listing concrete possible values for the optional topic parameter (finance, pharma, etc.) and specifying that omission yields a cross-category spread. This provides practical guidance beyond the schema description.

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

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

The description clearly states the tool returns category-bucketed example questions with tool+argument shapes. It distinguishes itself from siblings by explicitly positioning as the onboarding entry point for learning what Pipeworx can do and how to call meta-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 unfamiliar with Pipeworx's capabilities or to learn meta-tool invocation. It provides clear context but does not explicitly state when not to use it or list alternatives for similar tasks.

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 important behavior beyond annotations: row is deactivated (not deleted), historical events remain via recent_alerts. Annotations indicate non-destructive idempotent write; description adds precision.

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 action and constraint, succinctly covering all needed 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?

Fully describes the tool's effect (deactivation, not deletion), ownership rule, and link to related tools. No output schema needed; sufficient for a single-param 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% for the single 'id' parameter. Description adds context: id is a uuid returned by subscribe, bridging the two tools.

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') with method ('by id'). Distinguishes from sibling 'subscribe' and 'list_subscriptions'.

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

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

Explicitly states ownership enforcement ('you can only cancel your own subscriptions'), guiding correct usage. Does not list alternatives but context implies when to use.

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

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

Annotations already declare readOnly, openWorld, idempotent hints. Description adds value by detailing the dual-pipeline behavior (SEC/XBRL for financial, grounded for others), the return format (verdict, value, citation, reasoning), and that it replaces multiple sequential calls. 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?

Single paragraph, dense with information, front-loaded with purpose phrases. Could be slightly more structured (e.g., bullet points for return fields) but every sentence contributes meaningful 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?

Given only 2 parameters, no output schema, and rich annotations, the description fully covers what the tool does, its internal logic, parameter effects, and expected outputs. No gaps for an agent to use it 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%. Description adds meaning beyond schema: explains tolerance_pct default behavior and override use cases, and clarifies that claim type determines pipeline. This context helps agents use parameters correctly.

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

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

The description uses specific verbs like 'validate', 'verify', 'confirm or refute' and clearly identifies the resource as natural-language claims. It distinguishes itself from sibling tools by explaining it replaces multiple sequential calls for fact-checking.

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

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

Explicitly states when to use: 'whenever the agent needs to check whether something a user said is factually correct.' Differentiates between company-financial claims and other claims with specific pipeline descriptions. Lacks explicit when-not-to-use but context is sufficient.

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