Urlhaus

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

Annotations already declare safe read-only, idempotent behavior. The description adds important details: default model, cost implications (free vs. BYO), and return structure (per-model score, confidence, etc.), which go beyond annotations.

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

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

The description is two sentences long, front-loads the main action, and every sentence adds value—no wasted words. Structured with clear information hierarchy.

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

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

For a tool with 4 parameters and no output schema, the description covers purpose, usage, parameters, and return format adequately. It could mention limits (e.g., rate limits) but is sufficient for correct invocation.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds extra meaning: explains default model behavior, usage of '_apiKey' as pass-through, and return structure details, providing clear 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 specifies a clear verb ('probe'), resource ('LLMs'), and scope ('what they know about a business/brand/product/topic'), and distinguishes itself from sibling tools like 'scan_competitor_ai_presence' by focusing on specific entity visibility scoring.

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 states when to use (AI-marketing audits, brand checks) and provides conditional guidance (e.g., needing '_apiKey' for Anthropic). While it does not explicitly exclude alternatives, the context is clear enough for an agent to decide.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false, establishing the tool as safe to call. The description adds meaningful context about its routing behavior, argument-filling, and citation URIs, which helps the agent trust the response format. However, it does not go into potential errors, latency, or edge cases, so minor gap remains.

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 a strong preference directive, explains the mechanism, gives usage examples, and ends with escalation guidance. Every sentence contributes, though some repetition (e.g., listing many data categories and then examples) could be trimmed without loss. Acceptable for a complex routing tool.

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

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

Given the tool's complexity and lack of an output schema, the description covers what it returns (structured answer with citations), the breadth of data domains, the single-call nature, and when to choose alternatives. It also notes its default status across all tiers. The agent has sufficient context to select and invoke the tool correctly for most factual questions.

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

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

Schema coverage is 100%, with the 'question' parameter and several aliases fully described. The description adds value by characterizing the kind of input expected (natural language questions, with concrete examples like 'current US unemployment rate' and 'Apple's latest 10-K'), effectively enriching parameter semantics beyond the schema's generic 'your question or request.'

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

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

The description states the tool's core function: routes questions to one of 5,462 tools across 1,419 verified sources and returns structured answers with stable pipeworx:// citation URIs. It clearly distinguishes from siblings by naming ask_pipeworx_grounded and deep_research as alternatives, and the verb 'PREFER' sets expectations around when it should be selected.

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: 'PREFER OVER WEB SEARCH' for a semantically broad list of data types, 'START HERE for most questions,' and specifies when to use alternatives ('for a hallucination-resistant single answer... use ask_pipeworx_grounded; for a broad/multi-part question... use deep_research'). It also lists example queries, making selection unambiguous.

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

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

Beyond the annotations (readOnly, idempotent, etc.), the description discloses the current state (no active candidate, matches ask_pipeworx exactly), the possibility of live routing improvements during tests, and the fact that it is a fully working router with no fallback. This provides crucial behavioral context for an experimental tool.

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

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

The description is somewhat verbose with four sentences, but each sentence carries meaningful information: definition, current status, usage guidance, and non-fallback assurance. It's reasonably structured with a mix of clauses, but could be tightened slightly.

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 references 'same response shape' which is useful only if the reader knows ask_pipeworx. It thoroughly explains the beta context, current equivalence, and experimental nature, making it largely complete for a tool that is a variant of a well-known sibling.

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 parameters, so the description doesn't need to add parameter details. The phrase 'same arguments' is a helpful pointer but adds no new semantics beyond the schema's explicit alias documentation.

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

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

The description clearly identifies this as a beta universal router with the same 5,462 tools, arguments, and response shape as ask_pipeworx, distinguishing it from the stable sibling by its experimental routing improvements. It specifies the tool's function and its relationship to 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?

Explicitly says to use it 'exactly like ask_pipeworx when you want the newest routing' and references comparison against the stable router, which implies when to choose this over alternatives. It doesn't explicitly state when NOT to use it (e.g., for production reliability), but the beta/experimental framing makes this implicit.

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

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

Discloses refusal reasons (not_in_source, no_tool_match, etc.), return shape (answer, evidence, confidence, source, fetched_at), and the extra LLM call cost. This goes well beyond annotations, enriching the agent's understanding of failure modes and output contract.

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

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

Dense with useful information, front-loaded with the core purpose, then usage and behavior. No redundancy; every sentence earns its place despite length.

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

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

Fully explains return values, refusal modes, routing behavior, and cost trade-off. With no output schema, the description carries the burden and does so completely. Contextually complete for an agent to decide and invoke correctly.

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

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

Schema covers all parameters with 100% description coverage, including aliases, so baseline is 3. The description adds no additional parameter semantics, but none are needed because the schema is 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?

Clearly states it's a hallucination-resistant answer mode for high-stakes reads, with specific mechanics (routing, extraction from tool result only) and explicit distinction from sibling ask_pipeworx. Verb+resource is precise and differentiating.

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 (quoted/cited/acted-on answers, high-stakes domains) and when to prefer the alternative (casual lookups with ask_pipeworx due to extra LLM call). Names the sibling and gives a cost-based rule.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive. The description adds extensive behavioral context: resolver contract, parent event extraction, news fallback, safety short-circuits, wide-spread tradeability, and cancellation rules. 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 verbose but well-structured with bolded section headers and clear examples. It is front-loaded with purpose and usage. Every sentence adds value given the tool's complexity, though it could be slightly more concise.

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

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

The description is exceptionally complete for a tool with 3 parameters and no output schema. It covers resolver confidence, parent events, news fields, safety, spread warnings, cancellation rules, and more. All necessary context is provided.

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

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

Schema coverage is 100%, and the description adds value beyond schema by explaining default behavior (depth default thorough, include_raw default false) and providing context for the market parameter (slug, URL, or question text). The description examples further clarify usage.

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

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

The description states a specific verb+resource: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It clearly distinguishes from sibling tools like polymarket_edges and polymarket_arbitrage by focusing on data research for a given bet.

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

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

The description provides clear usage scenarios: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also includes guidance on when to inspect results (check market_match_confidence) and handles edge cases like closed markets, but does not explicitly state when not 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 indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds context: for companies it pulls from SEC EDGAR/XBRL with correct off-calendar fiscal years, for drugs from FAERS and FDA, and results are sorted by primary metric. It also mentions citation URIs.

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

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

The description is a single dense paragraph, but it is well-organized with examples first, then behavior, then benefits. It is concise yet comprehensive, though a slight structure with bullet points could improve readability.

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

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

Given the complexity of comparing entities with multiple data sources and no output schema, the description clearly states the return format (paired data + citation URIs), sorting behavior, and data sources. It covers all necessary aspects 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 coverage is 100% with two parameters. The description adds examples of acceptable values for 'values' (tickers/CIKs for company, drug names for drug) and explains what data each type returns. This goes 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 explicitly states that the tool performs side-by-side comparison of 2–5 companies or drugs in a single parallel call. It includes concrete usage examples like 'Compare X and Y' and distinguishes between entity types with specific data sources.

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 advises to ALWAYS PREFER this tool over sequential single-pack lookups when comparing entities, claiming it replaces 8–15 sequential lookups. It does not explicitly name alternative tools, but the guidance is clear and actionable.

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

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

Beyond the readOnlyHint/openWorldHint annotations, the description adds substantial behavioral context: account/sign-in requirements, paid depth tier, parallel routing across 5,462 tools, return packet format (verbatim evidence + confidence + source + fetched_at + citation), gaps[] and contradictions[] arrays, hop field, fetchable citation_uri guarantee, semantic excerpting behavior, and expected latency (15-60s up to 90s). No contradiction with annotations.

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

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

The description is a dense, single large paragraph that packs in a lot of important information but is not concise or well-structured. The main purpose statement appears after the account-requirement warning, so it is not ideally front-loaded. While every sentence has value, formatting with bullets or headers would improve scannability.

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 carries the full burden of explaining return values and does so comprehensively: findings packet contents, gaps[], contradictions[], hop field, citation_uri fetchability, timeouts, and use-case boundaries. It also covers account prerequisites and alternates, making the tool selection and invocation guidance 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% so baseline is 3. The description adds meaning to the depth parameter by detailing the paid plan requirement and explaining the gap-recovery/iterative-hop behavior for standard and thorough. It also clarifies that the question parameter is intended for broad/multi-part questions ('decomposition is the point'), which is 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 a specific verb+resource: 'Grounded multi-source research across Pipeworx's 1419 STRUCTURED data sources ... in ONE call – this is NOT open-web search.' It explicitly distinguishes itself from sibling tool ask_pipeworx, thereby clarifying its unique scope.

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

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

The description provides explicit when-to-use and when-not-to-use guidance: 'Best for broad/multi-part questions over structured data... For a single lookup use ask_pipeworx... For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx.' It also notes the account requirement and points to ask_pipeworx as the alternative when not signed in.

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

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

Annotations indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds beyond by detailing the return format (top-N tools with names, descriptions, schemas) and that results are ready to call, no second schema lookup.

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

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

The description is concise (4-5 sentences), front-loaded with core action, and each sentence adds value. No redundancy.

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

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

Given no output schema, the description adequately explains the return format and usage context. However, it does not mention edge cases like empty results or performance, which would push it to 5.

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

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

Schema coverage is 100%, and the description does not add significant semantic value beyond what the schema provides. The schema already documents all parameters and aliases. Description focuses on purpose, not parameters.

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

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

The description clearly states the tool finds/discover tools by describing data or task, and specifies it returns top-N relevant tools with full schemas, ready to call. This distinguishes it from siblings like compare_entities or deep_research.

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

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

Explicitly instructs to call this FIRST when many tools are available, and provides examples of when to use (e.g., SEC filings, FDA drugs). Also implies not for direct answers but for tool discovery.

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

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

Adds context beyond annotations: describes fan-out across multiple sources, mentions patents API sunset and soft-fail, and lists returned fields. 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?

Front-loaded with example queries and key instruction. While slightly lengthy, every sentence adds value with minimal redundancy.

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

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

Strong coverage given annotations and schema: safety profile from annotations, param details from schema, and behavioral details from description. No output schema needed for clarity.

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

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

Schema coverage is 100%, so baseline 3. Description adds practical context (e.g., zero-padded CIK) but largely repeats schema. Adds no critical depth beyond what 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?

Description uses specific verb-resource ('full cross-source profile') and lists example queries. Distinguishes from sibling tools like resolve_entity by specifying that names are not supported.

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 to prefer over chaining individual lookups and provides clear exclusion condition: 'names not supported (use resolve_entity first)'.

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: 'clear sensitive data' and the pairing guidance. Annotations already show destructiveHint=true, so no contradiction.

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

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

Two concise sentences: first states purpose, second provides usage guidelines. No unnecessary words.

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

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

For a simple tool with one parameter and no output schema, the description fully covers purpose, usage, and behavior. Contextual completeness is high.

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

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

Schema coverage is 100% with a clear parameter description. The tool description adds no additional meaning beyond the schema, meeting the baseline.

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

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

The description clearly states the action ('Delete') and resource ('previously stored memory by key'), and distinguishes it from sibling tools 'remember' and 'recall' by mentioning pairing.

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

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

Explicitly states when to use: 'When context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' Also mentions pairing with 'remember' and 'recall', implying 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?

Describes the fetching, extraction, and output format ('single text blob ready to drop at site-root/llms.txt'), adding context beyond annotations. No annotation contradiction; readOnlyHint and idempotentHint are consistent. Does not detail error handling or rate limits, but acceptable for a read-only tool.

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

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

Four sentences, each serving a purpose: function, process, output format, and use cases. Front-loaded with the main action. No redundant words.

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

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

For a simple two-parameter tool with no output schema and safe annotations, the description covers purpose, process, output, and use cases adequately. Lacks mention of error handling or prerequisites but overall complete.

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

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

Schema coverage is 100% with descriptions for both parameters. Description adds example for url ('https://example.com') and constraint for max_links ('default 25, max 50'), but this is marginal extra value beyond the schema. Baseline of 3 is appropriate.

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

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

Description clearly states 'Generate a production-ready llms.txt file for any URL' and explains the purpose for AI crawler indexing. It differentiates from sibling tools like ai_visibility_check and scan_competitor_ai_presence by focusing specifically on generating the llms.txt file.

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: getting a client's site indexed, drafting for own project, or auditing competitor view. However, it does not mention when not to use this tool or suggest alternatives, which would strengthen guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and idempotency. The description adds that it returns recently submitted malware URLs, which aligns with annotations but does not disclose additional behavioral traits such as rate limits, result ordering, or potential staleness.

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: the first states the action clearly, the second provides a use case. There is no redundant or unnecessary wording, and it is efficiently front-loaded.

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

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

For a simple tool with one optional parameter, an output schema, and no nested objects, the description covers the core functionality and a typical use case. It could mention the output format explicitly, but the output schema fills that gap.

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

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

Input schema has 1 parameter (limit) with a description covering its type, default, and max. Schema description coverage is 100%, so the tool description adds no parameter information beyond what is already in the schema. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool retrieves recently submitted malware URLs from URLhaus ('Get a list of recently submitted malware URLs from URLhaus'). It names the specific verb and resource, though it does not explicitly differentiate from sibling tools like lookup_url or recent_alerts that may overlap in functionality.

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

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

The description implies usage for monitoring latest threats ('Useful for monitoring the latest threats.'), but provides no explicit guidance on when to use this tool versus alternatives, nor any conditions or exclusions.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds context by listing specific return fields and usage scenarios, no contradictions.

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

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

Two sentences with no wasted words: first states purpose and return fields, second gives usage guidance. Front-loaded and efficient.

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

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

Given 1 optional parameter and no output schema, description adequately covers return fields and usage context. Could optionally mention parameter behavior, but not required.

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 1 optional parameter (include_inactive) with description, and schema coverage is 100%. Description does not mention the parameter, so baseline score of 3 is appropriate.

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

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

Description clearly states the tool lists active subscriptions and enumerates return fields (id, type, params, etc.), distinguishing it from siblings like subscribe and unsubscribe.

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

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

Explicitly states when to use it: 'review what you're monitoring before adding more or to find an id to cancel.' Does not explicitly mention alternatives, but sibling context implies separate tools for other actions.

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

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds context about the data source (URLhaus) and explicitly states it returns all known malicious URLs. 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?

Two sentences, front-loaded with purpose, no extraneous information. Every sentence adds value.

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

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

Simple one-parameter lookup tool with output schema explaining return values. Description covers all necessary context: data source, input, output.

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

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

Schema coverage is 100% with clear description and example. Description adds no further semantics beyond what the schema already 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?

Description clearly states verb 'look up', resource 'hostname or IP address in URLhaus database', and output 'malicious URLs'. Distinguishes from sibling lookup_payload and lookup_url by specifying host 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?

Context implies usage when you have a host/IP and want associated malware URLs, but no explicit when to use vs alternatives or when not to use. Sibling tools exist for different lookup types, but the description does not guide selection.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the description isn't needed for safety. It adds context about the data source (URLhaus) and output fields but doesn't disclose any additional behavioral traits (e.g., rate limits, authentication). This is adequate but not exceptional.

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 that efficiently conveys purpose and results. It is concise without being overly terse, though it could be slightly more structured (e.g., separate result list).

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 an output schema present (even if not shown here), the description sufficiently covers inputs and outputs. It mentions the database and key return fields. It lacks details like number of results or authentication, but for a lookup tool with good annotations, this is nearly complete.

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

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

Schema coverage is 100% and each parameter has a clear description (hash format and length). The tool description does not add extra meaning beyond that, so it meets the baseline but doesn't exceed.

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 action: look up a malware payload by MD5 or SHA256 hash. It specifies the database (URLhaus) and lists the returned fields, distinguishing it from sibling tools like lookup_host and lookup_url.

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

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

The description implies when to use the tool (when you have a hash and need payload info). It doesn't explicitly state exclusions or alternatives, but the sibling context makes differentiation clear. Adding a note that at least one hash is required would improve 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 declare the tool as read-only, idempotent, and non-destructive. The description adds that it queries an external database (URLhaus) and returns specific fields. However, it does not disclose potential rate limits, authentication requirements, or behavior on non-malicious URLs. The description provides some additional context but is not exhaustive.

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

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

The description is concise and well-structured: two sentences, the first stating the action and purpose, the second listing return fields. No unnecessary details, and each sentence serves a clear function.

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

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

Given the tool's simplicity (one parameter, annotations indicating safe operation, and an output schema), the description covers the essential aspects. It explains the purpose, the data source, and return values. It could mention limitations or typical use cases, but overall it is adequate for an AI agent to understand and invoke the tool correctly.

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

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

The input schema has 100% coverage for its single parameter 'url', with a clear description and examples. The description does not add extra meaning beyond confirming the parameter is a URL and mentioning the database context. Since the schema already covers the parameter thoroughly, the description adds minimal value here.

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: to look up a URL in the URLhaus database to check for malware. It specifies the database (URLhaus) and the return fields (threat category, status, blacklist status, tags). This distinguishes it from sibling tools like lookup_host or lookup_payload, which target different entities.

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

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

The description does not provide guidance on when to use this tool versus alternatives like lookup_host or lookup_payload. It only states what it does, leaving the agent to infer usage context. No explicit exclusions or comparisons are given, which is a gap given the sibling tools.

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

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

No safety annotations are provided, so the description carries the full disclosure burden. It discloses that feedback is rate-limited to 5 per identifier per day, costs nothing and doesn't count against quota, and is reviewed daily with roadmap impact. It also instructs users not to paste end-user prompts, adding operational context beyond the schema. However, it doesn't mention what the response/outcome is after submission, a minor gap.

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

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

The description is relatively long but each sentence serves a purpose: it covers purpose, when, when-not, how to describe, and operational notes (rate limit, cost). It is front-loaded with the core purpose and immediately gives usage scenarios. While a bit dense, nothing feels redundant.

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 tool with no output schema and full parameter schema, the description covers the essential context: what it does, when to use, scope, and operational constraints. It doesn't explain post-submission behavior, but that's not critical for using the tool. Overall, it provides enough context to invoke 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 schema covers all three parameters with rich descriptions, so the baseline is 3. The description adds value by mapping the 'type' enum values to concrete scenarios and advising on message content ('Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt'), which clarifies the message parameter. It doesn't add much about 'context' but the schema already explains 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 opens with 'Tell the Pipeworx team something is broken, missing, or needs to exist,' a specific verb+resource statement. It further differentiates from siblings by clarifying it is for feedback on Pipeworx tools only, not for using them, and enumerates the exact use cases (bug, feature/data_gap, praise). This clearly separates it from tools like ask_pipeworx or remember.

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: '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 provides a when-not: if the tool is from a different MCP server, file with that server instead, and offers a heuristic for identifying Pipeworx tools. This is exemplary usage guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. Description adds value by specifying data source (CF analytics-engine), no PII, and caching behavior (5min-1h). No contradictions.

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

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

Three front-loaded sentences: purpose, use cases, technical details. No wasted words.

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

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

Despite no output schema, description adequately covers return structure (top tools, packs, total volume) and parameter semantics. All needed context is present 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 has 100% coverage with enum and description. Description adds extra semantic meaning: short vs long windows for hot vs steady-state demand, enhancing the schema's 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?

Description clearly states it returns top tools, packs, and call volume over recent windows. Verb-resource pair is explicit, and it distinguishes from sibling tools like lookup/recall by focusing on aggregated trending 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?

Three explicit use cases are provided (discovering hot data, confirming canonical tools, aligning use cases). No explicit when-not-to-use, but the tool's niche is clear from context.

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

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

Annotations declare readOnlyHint true, idempotentHint true, destructiveHint false. Description adds substantial behavioral context: checks (monotonicity, partition, fill), thresholds (3pp, 20% placeholder, 0.30 Jaccard), response structure, and trade guidance. 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 comprehensive and front-loaded but quite verbose. Every sentence adds value, but could be more concise. Still, it is well-structured and informative.

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

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

Given the tool's complexity (multiple modes, algorithmic details, response fields) and absence of an output schema, the description fully explains return values, internal checks, and even references a related tool for custom sizing. 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?

Schema coverage is 100% with descriptions for both parameters. Description significantly enriches meaning: explains modes, recommended usage, example slugs, acceptance of full URLs, and the operations performed for each parameter.

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

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

The description clearly states that the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) and contrasts with sibling tools like polymarket_edges and polymarket_fill_risk.

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 each parameter: no args for trending scan, event for specific market, topic for cross-event scanning. Also advises when not to trade (realizable edge ≤ 0) and suggests polymarket_fill_risk for custom sizing.

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. Description adds behavioral context: caching at KV level for 1h keyed on all knobs, Fed bets excluded from ranking, and diagnostics provided. 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?

Front-loaded with purpose, then structured sections for model families, edge details, and response format. Dense but every sentence earns its place. Slightly verbose but justified given complexity.

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

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

Despite no output schema, description details the full response structure (by_segment, fed_candidates, diagnostics) and explains why some segments might be empty. Completely covers what the agent needs to understand return values and parameters.

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

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

Schema coverage is 100%; every parameter is described. Description adds significant extra meaning beyond schema types and brief descriptions, e.g., slippage_pp explains typical bid/ask depth, min_liquidity explains walking the book, min_partition_leg_kelly clarifies its application. Schema alone is insufficient without this context.

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

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

Description clearly states it scans Polymarket markets and returns opportunities where Pipeworx data disagrees with market price. Specific verb ('scan') and resource ('Polymarket markets') with clear value proposition. Distinguishes from sibling tools like polymarket_arbitrage and polymarket_edge_tracker by focusing on Pipeworx disagreement and discovery.

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

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

Explicitly built for 'what should I bet on today' and provides detailed guidance on tradeable-edge knobs and filters. While it doesn't directly compare with siblings, it gives clear context on when to use the tool and how to configure parameters.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds significant behavioral detail: daily closes vs intraday, 60-day snapshot TTL, snapshot gaps due to cache-miss, and full response structure. 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 well-structured with a clear introduction, response field details, and limits. It is front-loaded with the core question. Slightly verbose but every sentence adds value; could be trimmed slightly 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 thoroughly explains the response structure (tracked, expired, snapshot_dates) and each field's meaning, including trend types and decay calculation. It also covers limitations. Completely adequate for a tool with two optional parameters.

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

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

Schema coverage is 100% with parameter descriptions. The description adds context: days default 14 and max 30, window default '1wk' and refers to snapshot family, and explains that decay numbers come from daily closes. This adds meaning beyond schema basics.

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 provides edge persistence and decay telemetry from daily snapshots, answering a specific temporal question about edge duration. It distinguishes itself from sibling tools like polymarket_edges by focusing on time-series analysis rather than current 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 explicitly explains when to use this tool (comparing fresh vs old wide edges) and provides context for interpreting results. However, it does not explicitly list alternatives or when not to use it, though the purpose inherently differentiates it from siblings.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context: it walks the order book ladder, returns specific fields (top_of_book, vwap_fill_price, slippage, verdict), and warns about thin legs and partial fills. This goes beyond annotations without contradicting 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 dense and provides thorough information, but it is verbose with some redundancy (e.g., repeated mentions of partial fills). It could be more concise while retaining key points.

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

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

Despite the lack of an output schema, the description enumerates all return fields for both modes, covers edge cases (thin_legs, forced_directional_risk), and explains the implication of partial fills. It is comprehensive for the tool's complexity.

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

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

Schema coverage is 100%, but the description adds significant value by explaining how size_usd is interpreted differently in single-market vs basket mode, the default logic for side, and clarifying that market and event are mutually exclusive. This enhances the bare schema descriptions.

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

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

The description clearly states it performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It explicitly defines two modes (single-market and basket/partition) and differentiates from siblings like polymarket_arbitrage and polymarket_edges by emphasizing its role as a pre-trade risk check.

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

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

The description explicitly says 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It provides clear when-to-use guidance and explains why (theoretical overround not capturable, partial fills create directional risk).

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

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

The description adds extensive behavioral context beyond annotations: explains response structure, safety fields (compatibility_warning), temporal alignment, and skipped types. Annotations already indicate readOnly, openWorld, idempotent; description complements 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?

The description is lengthy but well-structured with headings. Every sentence adds value, though slight verbosity prevents a perfect score. The structure aids readability.

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

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

Despite no output schema, the description fully explains the response, edge cases (compatibility, temporal alignment), and usage. For a complex tool, this is highly complete.

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

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

Schema coverage is 100%, but the description enriches parameter semantics by listing the topic enum values and giving explicit ticker examples. It explains that topic auto-fetches matching events, adding value beyond schema.

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

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

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket for the same question. It distinguishes two modes (topic shortcuts and explicit tickers), which differentiates it from siblings like polymarket_arbitrage.

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

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

The description provides explicit guidance on when to use topic mode vs explicit mode and warns that most pre-mapped topics return compatibility warnings. It does not explicitly mention when not to use it, but the alternatives are clear.

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

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

Annotations already indicate readOnly and idempotent. The description adds context about scoping (anonymous IP, BYO key hash, account ID) and behavior when key is omitted, adding value beyond annotations without contradiction.

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

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

Two sentences, front-loaded with purpose, no wasted words. Efficient and well-structured.

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

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

While missing output format details, the description is complete for a simple retrieval tool. Could mention return type but adequate overall.

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

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

Schema coverage is 100% and description adds meaning by explaining that omitting the key lists all keys. This provides clear guidance beyond the schema's basic description.

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

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

The description clearly states the verb (retrieve/list) and resource (previously saved memory), distinguishes between retrieving a specific key and listing all keys, and differentiates from sibling tools like remember and forget.

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

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

The description explicitly tells when to use the tool (to look up previously stored context), provides examples, explains scoping, and mentions pairing with remember and forget for a complete workflow.

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

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

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral context by explaining the mark_read parameter's side effect (flagging events as read) and the feed's persistence. It does not contradict annotations.

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

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

The description is concise (four sentences) and front-loaded with the primary purpose. Every sentence adds useful information without 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 no output schema, the description adequately explains the return fields (source, citation_uri, raw payload). It covers all 5 parameters (none required) and provides usage guidance (polling, alternative HTTP endpoint). The description is 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?

The input schema covers 100% of parameters with descriptions. The description adds significant value by providing examples (e.g., 'sec_8k' for type), clarifying the ISO timestamp format for since, and explaining the effect of mark_read on subsequent calls. This goes beyond the schema.

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

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

The description clearly states the tool's function: 'Pull fired events from your subscription feed.' It specifies what is returned (source, citation_uri, raw event payload) and how to filter. However, it does not explicitly differentiate from siblings like list_subscriptions or subscribe, though the purpose is distinct.

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 context on usage, noting that polling works fine and that the same feed is available via an HTTP endpoint for scripts/dashboards. It implies when to use the tool versus the alternative but does not explicitly state when not to use it or compare with sibling tools.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true. Description adds valuable context: fans out to multiple sources, explains fallback and soft-fail mechanisms, and describes return structure. 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?

Front-loaded with example queries. Every sentence serves a distinct purpose: purpose, sources, parameter formats, return structure, alternative. No wasted words, well-structured for quick understanding.

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

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

Despite no output schema, description fully explains return structure (changes[] grouped by source, total_changes count, citation URIs). Covers source behaviors, parameter formats, and edge cases like USPTO soft-fail. 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 100%. Description adds extra meaning beyond schema: explains 'since' format with relative shorthand examples and recommends '30d' or '1m', clarifies 'value' can be ticker or CIK, and reiterates 'type' limitation.

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 'change feed for a company in the last N days/weeks/months' from multiple sources, with specific verbs like 'fans out'. It distinguishes itself from sibling 'entity_profile' and lists example queries that match user intents.

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

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

Explicitly provides when-to-use scenarios with example queries. Gives a direct alternative: 'Use entity_profile instead when you want the static profile'. Also explains source behavior (GDELT→GNews fallback, USPTO soft-fail) guiding appropriate 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?

Adds context beyond annotations: key-value scoping by identifier, persistence for authenticated users, 24-hour retention for anonymous. Annotations already cover idempotency and non-destructiveness.

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

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

Four sentences, front-loaded with purpose, every sentence adds value. No redundancy.

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

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

Complete for simple tool with two string params and no output schema. Explains storage scope, persistence, and tool pairing.

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 descriptions for key and value. Description provides example key formats but adds minimal extra meaning 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?

Clear verb 'save' and resource 'data', explicitly states purpose to reuse later. Differentiates from siblings 'recall' and 'forget'.

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

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

Explicitly says when to use ('when you discover something worth carrying forward') and pairs with recall/forget for retrieval/deletion.

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

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

Annotations declare readOnly/idempotent/destructive=false, so safety is covered. The description adds unique behavioral details: internal cascading lookups, auto-disambiguation, and citation URI return values. This gives the agent insight into performance and output beyond the annotations.

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

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

The description is structured with a clear opening, a usage directive, and a labeled 'SUPPORTED TYPES' section. While not minimal, every sentence contributes context—examples, return values, and internal behavior are all relevant. It's front-loaded with 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 only 2 parameters and no output schema, the description covers essential return fields (ticker, CIK, company_name for companies; RxCUI, ingredient, brand for drugs) and includes citation URI formats. It also explains the multi-endpoint cascade, making it self-sufficient for an agent to understand what happens and what to expect.

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

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

The input schema already covers parameter names, types, and values with 100% coverage. The description enriches this with concrete examples (AAPL, 0000320193, ozempic, metformin) and explains what each type returns, making parameter semantics clearer. This goes beyond the schema 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 uses specific action verbs ('resolve', 'look up', 'find') tied to a clear resource: mapping user-spoken names to canonical IDs (ticker, CIK, RxCUI). It explicitly distinguishes itself from siblings by stating these IDs are what 'other tools require as input', making it the entry point for name-to-ID resolution.

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

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

The description provides an explicit usage directive: 'Use FIRST whenever you have a name but need an ID.' It also enumerates supported types with input formats, but does not explicitly state when not to use it or mention alternatives by name. The context is clear enough to guide the agent.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false. The description adds valuable context: it discloses that it probes each entity with ai_visibility_check, ranks by score, and returns specific fields (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 long, front-loaded with the core purpose ('Compare AI visibility across multiple entities side-by-side'), and contains no filler. Every sentence adds value.

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

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

Despite no output schema, the description specifies return fields (score, confidence, signal density). Parameters are fully documented via schema. The tool's complexity (ranking multiple entities) is addressed, though additional details about sorting or assumptions could improve completeness.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds marginal value by noting that the first entity is treated as the 'subject' for narrative and mentioning the Anthropic API key requirement. However, parameter descriptions in the schema already provide clear 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 tool compares AI visibility across multiple entities side-by-side, ranks by score, and surfaces most/least recognized. It uses specific verbs and resources ('compare', 'probes', 'ranks') and distinguishes from the sibling tool 'ai_visibility_check' (single-entity) by focusing on multi-entity comparison.

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

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

The description explicitly provides a use case ('competitive AI-marketing audits') and an example question. It implies usage for comparing entities but does not explicitly specify when to avoid using it or mention alternatives like 'ai_visibility_check' for single-entity queries.

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

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

Adds significant behavioral context beyond annotations: fans out across multiple services, partial failures degrade gracefully with 'sources_failed' list, bundlephobia first measurement can take 5-30s. No contradiction with annotations (readOnlyHint, idempotentHint, etc.).

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

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

Description is moderately long but front-loaded with purpose and key details. Each sentence serves a purpose, though some minor redundancy could be trimmed. No wasted words.

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

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

Despite no output schema, the description lists all return fields (summary block, per-advisory, links, alternative versions) and covers edge cases like partial failures and timing. This provides complete context for an agent to understand the tool's behavior and output.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by clarifying that scoped packages (e.g. '@types/node') are accepted and that version defaults to latest published. This extra context raises the score above baseline.

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

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

The description clearly states the tool's purpose: a composite check for adding an npm package, covering license, advisories, version history, and bundle size metrics. It specifies 'NPM ecosystem only in v1', distinguishing from other ecosystems handled by deps.dev directly. No sibling tool overlaps.

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

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

Explicitly states when to use: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also provides alternatives for other ecosystems (PyPI, etc.), giving clear guidance on when not 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?

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds valuable behavioral details: uses BGE-base-en embeddings + cosine similarity over 500-char overlapping windows, input cap of 200K chars with truncation/flagging, and that every passage carries a character offset for verification.

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

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

The description is concise (4-5 sentences) and frontloaded with the core action. Every sentence adds value: purpose, usage context, pairing suggestion, technical mechanism, and limitation. 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?

Given the tool's complexity (semantic search, truncation, offsets) and absence of an output schema, the description is remarkably complete. It explains input, output format (passages with offsets and similarity scores), limitations (200K chars), embedding method, and use case.

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 parameters are well-documented. The description adds meaning beyond the schema by explaining the high-level usage pattern (pass fetched text + query), giving example queries, and clarifying the limit's effect (max passages). This surpasses the baseline of 3.

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

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

The description clearly states the tool's purpose: semantic search inside a fetched record (e.g., SEC 10-K, article) using natural-language queries. It distinguishes from siblings by explicitly mentioning pairing with ask_pipeworx_grounded and highlighting the offset feature for verification.

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 when a record is too large for the prompt, saving context by returning only relevant passages with offsets. It pairs well with ask_pipeworx_grounded. While it lacks explicit 'when not to use' guidance, the context is clear enough.

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

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

The description adds significant behavioral details beyond annotations: requires OAuth account, delivery constraints (phone verification, 10/day SMS cap, webhook auto-disable after 10 failures), and per-type parameter examples. 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 well-structured with clear sections and front-loaded purpose. It is slightly long but efficiently packs information without redundancy; every sentence adds value.

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

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

Given the tool's complexity (multiple types, nested objects, multiple delivery channels), the description is comprehensive. It covers required parameters, type-specific filters, delivery constraints, and return value (subscription ID). Lacks note on idempotency (annotations indicate idempotent but description does not mention), but otherwise 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 already has 100% description coverage for all parameters. The description enriches semantics by providing concrete examples for each subscription type and detailed delivery channel constraints, adding value beyond the schema.

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

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

The description clearly states the tool creates a proactive monitoring subscription to a live-data event stream and returns a subscription ID. It lists supported types and delivery channels, distinguishing it from sibling tools like list_subscriptions and unsubscribe.

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

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

The description provides clear context on when to use the tool (to set up proactive monitoring) and requirements (Pipeworx OAuth account). It gives examples for each subscription type but does not explicitly contrast with sibling tools or state when not 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 provide readOnlyHint, idempotentHint, destructiveHint. Description adds context that it returns category-bucketed examples and is non-destructive. Does not contradict annotations.

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

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

The description is thorough but slightly verbose with multiple alternative phrasings. It front-loads the purpose and is well-structured, though some repetition could be trimmed.

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

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

For a simple, read-only onboarding tool with one optional parameter and no output schema, the description covers the use case, return format (categories), and usage context fully. 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 has 100% coverage with a description of the topic parameter. Description adds nuance by stating 'Omit for a cross-category spread,' which is not in the schema. This provides decision guidance beyond enum listing.

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 the onboarding entry point for a new agent, returning category-bucketed example questions with exact tool calls. It distinguishes from siblings like ask_pipeworx and discover_tools by specifying its role.

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 to use this tool FIRST when the agent does not know what Pipeworx can do, and explains when to omit or include the topic parameter. No guidance on when not to use is needed given its introductory purpose.

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 ownership constraint, deactivation behavior, and link to historical data. Adds value beyond annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true). 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?

Two sentences, no wasted words. Front-loads purpose, then adds constraints and behavior efficiently.

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

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

Covers all necessary information for a simple cancel operation: id, ownership, effect on data, and relation to other tools (recent_alerts). No output schema needed.

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

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

Schema has 100% coverage and describes id as 'Subscription id (uuid) returned by subscribe.' Description adds context that the id comes from subscribe, which is helpful but not essential. Slightly improves over baseline 3.

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

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

Clearly states the action (Cancel) and resource (subscription by id). Distinguishes from siblings like subscribe and list_subscriptions.

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

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

Explicitly states ownership enforcement (only own subscriptions) and behavioral outcome (deactivation, not deletion, preserving history via recent_alerts). Provides when-to-use and when-not-to-use context.

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

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

Annotations already provide readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds significant behavioral context: it details the two processing pathways (SEC EDGAR for financial, grounded pipeline for other), explains the verdict set (confirmed, approximately_correct, refuted, etc.), and mentions verbatim evidence with citations. 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 moderately long but well-structured, starting with example queries, then stating the purpose, detailing pathways, and specifying output. Every sentence adds value; however, it could be slightly more concise without losing clarity.

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

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

Despite no output schema, the description fully explains the return values (verdict, actual value with citation, reasoning). It covers the two processing paths, the type of claims (financial vs. other), and the role of tolerance. For a tool with two parameters and moderate complexity, this description is comprehensive.

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%, providing baseline 3. The description adds value beyond the schema: it explains that 'tolerance_pct' overrides the implied tolerance and suggests use cases (e.g., hallucination detection with 1-2). It also gives concrete examples for the 'claim' parameter, clarifying its natural-language nature.

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 is highly specific: it states the tool verifies natural-language factual claims against authoritative sources, with examples like 'fact check' and 'verify the claim that…'. It distinguishes itself from sibling tools by noting that it replaces 4-6 sequential calls and covers both financial and other claims.

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 clearly states when to use this tool: 'whenever the agent needs to check whether something a user said is factually correct.' It differentiates between company-financial claims and other claims, implying the appropriate context. However, it does not explicitly mention when not to use it or list alternatives among siblings.

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