Cloudflare Radar

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable context: it mentions the free default model, the BYO key for Anthropic (implying external API calls and user cost), and the return structure, which enhances transparency beyond annotations.

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

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

The description is concise (4 sentences) and front-loaded with the main purpose. Every sentence adds essential information: purpose, default model, API key option, return structure, and use cases. 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 having no output schema, the description fully explains the return structure (per-model {score, confidence, signals, raw_response} + combined view). It also covers model selection, API key necessity, and disambiguation. All aspects required for correct usage are addressed.

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 by explaining the _apiKey parameter ('passed straight through to api.anthropic.com'), the context parameter ('helps disambiguate common names'), and the models parameter (supported strings). This improves understanding beyond the schema alone.

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

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

The description clearly states the tool probes LLMs for knowledge about an entity and scores visibility per model. It specifies the default model and optional Anthropic integration, distinguishing it from sibling tools like 'scan_competitor_ai_presence' by focusing on per-model scoring and specific use cases (AI-marketing audits, pre-launch brand checks).

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

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

The description provides clear use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and guidance on model selection (default vs. Anthropic with API key). However, it does not explicitly exclude scenarios or compare to sibling tools, leaving some ambiguity about when to use this versus 'scan_competitor_ai_presence'.

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

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

No annotations provided, so description carries full burden. It mentions it automatically picks the right source and returns the result, but does not disclose error handling, limitations, or requirements (e.g., authentication, rate limits). Adequate but not comprehensive.

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

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

Single paragraph with clear front-loading of purpose and usage. Examples are provided without verbosity. Every sentence adds value.

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

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

Parameter count low and no output schema, but description provides rich examples and scope. Missing description of result format or potential errors, but overall adequate for a question-answering tool.

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

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

Single parameter 'question' with schema coverage 100%. Description adds value by listing example queries that clarify acceptable input beyond the generic schema description. However, no further detail on format or constraints.

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

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

Description specifies verb (answer), resource (natural-language question), and scope (routing across 300+ sources). Distinguishes from siblings by being a general query tool vs. specific tools like attack_summary.

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

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

Explicitly states when to use: when user asks 'What is X?', 'Look up Y', etc., and when not to manually pick a tool. Provides examples and mentions alternative is using specific Pipeworx tools.

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

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

No annotations provided. Description indicates a read operation (returns breakdown) but does not disclose permissions, data freshness, or side effects. Adequate but not thorough.

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 redundancy. Front-loaded with purpose, then filter capability. Highly efficient.

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

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

Covers main purpose and filter. Lacks explicit mention of output format or default behavior for missing parameters, but schema fills gaps. Not fully complete for a complex tool.

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

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

Schema has 100% description coverage. Description adds value only for the 'location' parameter by framing it as geographic scoping. Other parameters rely on schema alone.

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

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

Explicitly states it returns a percentage breakdown of DDoS attack mix by mitigation product or attack vector over a time window, with optional location filtering. Clearly distinguishes from sibling tools.

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

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

No explicit guidance on when to use this tool vs alternatives. Only mentions filtering by location, but does not discuss scenarios or exclusions.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds behavioral context: the tool fans out to multiple data packs based on bet classification and returns a comparison. It does not contradict annotations and provides useful operational details 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 front-loaded with the main purpose and contains four sentences, each adding meaningful information (input types, processing, output, use cases). While slightly long, it avoids redundancy and earns its length.

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

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

Without an output schema, the description adequately explains the return value ('evidence packet plus a simple market-vs-model comparison') and the fan-out logic. It covers the tool's role as a core demo product, though it could mention format specifics of the evidence packet.

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

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

Schema coverage is 100% with both parameters described. The description adds value by explaining that 'market' can be a slug, URL, or question text, and that 'depth' defaults to 'thorough'. This clarifies intended usage beyond the schema's formal 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 uses a specific verb+resource ('Research a Polymarket bet') and details input formats (slug, URL, question text) and output (evidence packet, market-vs-model comparison). It distinguishes from siblings by focusing on betting-related context integration, making the tool's unique purpose clear.

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

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

The description provides explicit use cases ('should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?') and states it is a demo product. However, it does not explicitly mention when not to use it or compare to alternatives like validate_claim or ask_pipeworx, leaving room for slight ambiguity.

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

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

With no annotations, the description must fully convey behavior, but it only lists returned fields. It does not disclose whether the tool is read-only, data freshness, rate limits, or authentication requirements. This is a significant gap.

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

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

The description is two sentences, front-loaded with the tool's purpose and output structure. No extraneous content, though it could be slightly more structured.

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

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

Given a simple tool with no output schema, the description lists key return fields but lacks detail on ordering, pagination, or whether results are limited to a default. Adequate but not 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 description coverage is 100% and already describes parameters ('limit' with range, 'date_range' with default). The description adds no additional parameter information, so it meets baseline adequacy.

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

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

The description clearly states the tool returns recent BGP route-leak events with specific fields (leaker AS, victim AS, originated prefixes, start/end times). It does not explicitly differentiate from sibling tools, but the purpose is unambiguous.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'attack_summary' or 'internet_quality'. There is no mention of prerequisites or context for invocation.

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

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

No annotations provided, but description explains data sources per type (EDGAR/XBRL for companies, FAERS/approvals/trials for drugs) and mentions returning paired data with citation URIs. Adequately discloses behavior for a query 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?

Description is a single paragraph that covers all key points without fluff. Could be slightly more structured with bullet points, but it remains concise and 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?

Covers main aspects: entity types, data items for each, count limits, and output format. No output schema, but the mention of 'paired data + citation URIs' provides enough context for an agent.

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

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

Schema coverage is 100%, and description adds context beyond schema by specifying the meaning of each type and giving examples for values. This helps agent understand how to fill parameters correctly.

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

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

Description clearly states verb 'compare' and resources 'companies or drugs'. Distinguishes from siblings by noting it replaces 8–15 sequential agent calls, making its scope unique.

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

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

Explicitly lists when to use: queries like 'compare X and Y', 'X vs Y', etc. Does not mention when not to use, but the positive triggers are clear enough for an agent.

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

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

No annotations provided, but the description fully discloses behavior: returns 'top-N most relevant tools with names + descriptions', mentions default/max limit. It is clearly a read-only search operation with no side effects described.

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

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

Single, well-structured paragraph with the most critical information first ('Find tools...'), followed by usage examples and guidance. Every sentence adds value with no redundancy.

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

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

Despite no output schema, the description explains what is returned (top-N, names+descriptions). It covers the tool's role (discovery before using specific tools), which is complete for a tool of this nature.

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

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

Schema coverage is 100% with both parameters described. The description adds value by clarifying that query is a natural language description and explicitly stating default limit (20) and max (50), which are not in the schema.

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

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

The description clearly states 'Find tools by describing the data or task' and lists extensive examples (SEC filings, FDA drugs, etc.), making the purpose unmistakable. It also implicitly differentiates from sibling tools like 'ask_pipeworx' or 'attack_summary' by positioning itself as the meta-discovery tool.

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

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

Explicit usage advice: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available'. While it doesn't explicitly state when NOT to use it, the clear directive to use it first is strong 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?

Without annotations, the description carries full transparency burden. It discloses the tool's read-only nature, the data sources (SEC, USPTO, news, GLEIF), and output format (pipeworx:// URIs). However, it could mention potential rate limits or data freshness, but the provided info is adequate.

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

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

The description is a single, efficient paragraph with no filler. It front-loads the purpose, includes usage examples, and covers all key aspects without redundancy.

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

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

No output schema exists, but the description enumerates the returned data categories (SEC filings, fundamentals, patents, news, LEI) and citation format. This provides sufficient context for an agent to understand the tool's output without needing a schema.

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

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

Schema coverage is 100%, and the description adds critical context beyond the schema: type is limited to 'company', value can be ticker or CIK, and names require prior use of resolve_entity. This enhances parameter understanding significantly.

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: 'Get everything about a company in one call.' It lists example user queries and explicitly contrasts with sibling tool resolve_entity, making the purpose unambiguous and 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 explicit when-to-use guidance (user asks for company profile) and when-not-to-use (names not supported, use resolve_entity first). This offers clear decision support for the agent.

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

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

No annotations provided. Description discloses the destructive nature and purpose, but lacks details on permissions or reversibility. Adequate for a simple operation.

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 direct sentences with no fluff. Front-loaded with action and context.

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

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

Given one parameter and no output schema, the description fully covers purpose, usage, and relationships.

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

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

Schema coverage is 100% with a clear description. The tool description adds minimal extra meaning beyond the schema.

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

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

The description clearly states the action ('delete') and resource ('previously stored memory by key'), and distinguishes from siblings like 'remember' and 'recall'.

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

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

Explicit use cases are given: when context is stale, task is done, or to clear sensitive data. Also pairs with related tools.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. Description adds concrete behavior: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' 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: first states purpose and mechanism, second lists use cases. No fluff, front-loaded with key action.

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

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

For a simple tool with 2 params, no output schema, and rich annotations, the description fully informs about operation, output format, and use cases. 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 descriptions cover 100% of parameters (url and max_links). Description mentions 'key links' but does not add significant meaning beyond schema. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'Generate' and resource 'llms.txt file for any URL', with specific AI crawlers mentioned. It distinguishes from siblings like 'ai_visibility_check' and 'scan_competitor_ai_presence' by focusing on llms.txt generation.

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

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

Provides explicit use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' Lacks explicit when-not-to-use or alternatives, but context is clear.

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

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

With no annotations, the description carries the full burden. It discloses the output type (summary with current value and change) and optional filtering. It does not mention destructive actions, which is consistent with a read-only tool. Slightly more detail on data source could improve transparency.

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

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

Single sentence that front-loads the core purpose and key metrics, with optional filter noted. Efficient and focused.

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

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

Given the tool has only two optional parameters and no output schema, the description fully explains what is returned (summary with metrics and changes). No additional information needed for completeness.

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

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

Schema description coverage is 100% with clear parameter descriptions. The description adds minimal value beyond the schema, only restating the location filter. No additional context for date_range beyond options already listed.

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 provides an Internet Quality Index summary with specific metrics (bandwidth, latency, jitter, packet loss) including current values and changes. It is distinct from sibling tools like attack_summary or bgp_leaks.

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

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

The description implies usage for retrieving internet quality metrics, but does not explicitly state when to use this tool versus alternatives or provide exclusions. No guidance on context or prerequisites.

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

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

With no annotations, the description fully carries the transparency burden. It discloses key behavioral traits: free usage, no quota consumption, rate limit of 5 per identifier per day, and that the team reads digests daily. It does not detail what happens on error or confirmation of submission, but for a feedback tool this is sufficient.

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

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

The description is concise and well-structured: it opens with the core action, then details use cases, guidelines, and constraints. Every sentence adds value, and there is no redundancy. It is efficiently front-loaded with the essential purpose.

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

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

Given the tool's complexity (simple feedback submission with nested parameters) and the absence of an output schema, the description fully covers what the tool does, when to use it, and what happens to the feedback. It is complete enough for an agent to understand 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?

Input schema has 100% description coverage, so the schema already explains all parameters. The description adds no new meaning beyond what the schema provides for 'type', 'context', and 'message'. Thus it meets the baseline for high coverage without extra semantic value.

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

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

The description starts with a clear verb-resource pair: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It precisely defines the tool's purpose (feedback) and distinguishes it from sibling tools like 'ask_pipeworx' (querying) and 'discover_tools' (exploration), which are not feedback-oriented.

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

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

The description explicitly states when to use the tool: for bug, feature, data_gap, or praise. It also tells what not to do: 'don't paste the end-user's prompt.' Further constraints (rate-limited to 5 per identifier per day, free, no quota impact) are provided, offering clear guidance on usage and 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?

Description adds caching behavior (5min-1h), data source (CF analytics-engine), and privacy (no PII). These go well beyond the annotations (readOnly, idempotent, etc.) providing rich behavioral context.

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

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

Four sentences with front-loaded purpose. Every sentence adds value; no redundant or filler content.

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

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

Despite no output schema, description hints at return format (pack, tool, count) and covers caching, use cases, and data origin. Complete for the tool's simplicity.

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?

Single parameter with 100% schema coverage; description explains trade-offs between windows (hot vs steady-state). Adds meaning beyond the enum values.

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

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

Description clearly states it returns top tools, top packs, and total call volume over a time window. Verb 'returns' and specific resource details make purpose unambiguous.

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

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

Explicitly lists three use cases (discovering hot data sources, confirming popular tools, aligning with agent needs). Provides clear context for when to use, though does not exclude alternatives.

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

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

Annotations already indicate read-only and open-world behavior. The description adds valuable details: the tool walks child markets, extracts dates/thresholds, sorts them, and reports violations. This goes beyond annotations and helps the agent understand internal logic.

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 (6 sentences) and front-loaded with the main purpose. It explains the concept efficiently without excessive verbosity, though minor trimming could improve. Still, it's well-structured.

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

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

Despite lacking an output schema, the description explicitly describes the return value as a list of objects with specific fields. It covers the algorithm and edge cases implicitly. For a simple tool with one parameter, it is complete.

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

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

The single parameter 'event' is well-described in the schema (100% coverage). The description does not add extra semantics beyond the schema, but it contextualizes the parameter by linking it to the tool's logic. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities via monotonicity violations in Polymarket events. It explains the underlying logic with concrete examples, distinguishing it from generic tools.

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

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

The description specifies when to use the tool (when events have multiple date/threshold markets) and what constitutes an arbitrage. It lacks explicit alternatives or when-not-to-use guidance, but the intended scenario is well-defined.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds rich context: scanning top markets, grouping by asset, fetching price history once, computing model probability, and ranking by edge. It also notes it's V1 and covers only crypto-price bets, which helps manage expectations.

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

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

The description is a single, well-structured paragraph that covers purpose, method, and use case without unnecessary fluff. It is slightly verbose but every sentence adds value, earning a score above average.

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

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

Despite no output schema, the description explains the return value ('top N ranked by edge magnitude with suggested trade direction') and the overall process. It addresses complexity by detailing the data sources and limitations, making it complete for an agent to use.

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

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

Input schema has 100% description coverage, so baseline is 3. The description reinforces the 'limit' parameter's purpose ('returns top N'). No additional meaning is needed beyond the schema, which is already clear.

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 scans high-volume Polymarket markets, finds where Pipeworx data disagrees with market price, and ranks by edge magnitude. It specifies it's for crypto-price bets using a lognormal model, distinguishing it from the sibling 'polymarket_arbitrage' and clearly defining 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?

The description explicitly frames the tool for the 'what should I bet on today' question, indicating when to use it. However, it does not mention when not to use it or compare it directly with sibling tools, though the context of crypto-price bets implies limitations.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, indicating a safe, read-only operation. The description adds context by explaining the two modes, the interaction of parameters (pre-mapped topics vs explicit overrides), and the structure of the return value (leg-by-leg prices and spreads). This goes beyond annotation signals, providing useful behavioral context.

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

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

The description is a single paragraph of moderate length, but it is well-organized with clear labels ('TWO MODES', 'Returns:') and examples. Every sentence adds value. A slight improvement could be breaking into bullet points or reducing redundancy, but it remains concise and 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?

The description lacks an output schema, so it explicitly describes the return values (leg-by-leg prices, spread in pp). It covers both usage modes and parameter interactions. However, it does not mention error cases (e.g., unmatched topic, missing event) or rate limits, and the return format is somewhat vague (e.g., 'spread in percentage points' without specifying direction). Overall, it is fairly complete for a read-only tool with rich annotations.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds significant meaning: it explains the concept of topic shortcuts vs explicit pairings, describes how parameters interact (explicit overrides topic), and provides concrete examples (e.g., 'KXFED-26OCT'). This extra context is vital for an agent to correctly invoke the tool.

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

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

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket. It specifies the verb (compute spread), resource (prices from two venues), and scope (same resolving question). This distinguishes it from siblings like polymarket_arbitrage and polymarket_edges by focusing on cross-venue arbitrage rather than intra-venue 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 details two modes—topic shortcuts and explicit pairings—and explains when to use each. It provides examples (e.g., 'fed' maps to KXFED-26OCT) and notes that the delta is an arbitrage signal. However, it does not explicitly state when not to use this tool or mention alternatives (e.g., using the sibling polymarket_arbitrage).

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

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

No annotations are provided, but the description fully explains behavioral traits: scoping to identifier, behavior when key is omitted, pairing with remember and forget.

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

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

Three sentences packed with essential information: purpose, usage guidance, scope, and relationship to siblings. 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 and simple parameter, the description is complete. It covers purpose, usage, scope, and related tools. No missing information.

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

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

Schema coverage is 100%, and description adds meaningful context: omitting key lists all saved keys. This goes beyond the schema's description of the 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 the tool retrieves a previously saved value or lists all keys when omitted. It distinguishes itself from sibling tools remember and forget by explaining its role in the memory workflow.

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

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

Explicitly states when to use (retrieve stored context) and mentions alternatives (remember, forget). Provides specific examples like user target ticker, address, research notes.

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

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

No annotations provided, but the description transparently explains the parallel fan-out to three sources and the return structure (structured changes, count, URIs). No contradictions.

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

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

The description is concise with 4-5 sentences, front-loading purpose. Each sentence adds useful info, though could be slightly more compact.

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

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

Given no output schema, the description explains return values effectively. Parameters are fully covered, and the entity type limitation is stated. Completeness is high for a tool of this complexity.

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

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

Schema coverage is 100%, and the description adds value by explaining 'since' formats and defaults, and that 'value' accepts ticker or CIK, going 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 'What's new with a company in the last N days/months?' and provides specific example queries. It distinguishes from siblings like entity_profile by focusing on recent changes.

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

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

The description gives explicit when-to-use examples and explains the fan-out behavior and date formats. However, it does not provide when-not-to-use guidance or 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?

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: data is stored as a key-value pair scoped by the agent's identifier, retention differs for authenticated (persistent) vs anonymous (24 hours) sessions. 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 three sentences, with the purpose front-loaded. Every sentence adds value: purpose, usage guidance, behavioral details, and pairing with siblings. No wasted words.

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

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

Given the tool's simplicity (two parameters, no output schema), the description is complete. It covers purpose, when to use, behavioral details (retention policy), and how to pair with related tools. No gaps remain.

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

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

Schema coverage is 100%, and both parameters have descriptions. The description adds minimal extra meaning beyond examples (e.g., 'findings, addresses, preferences, notes' for value), but it does not significantly augment what the schema already provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later — across this conversation or across sessions.' It uses a specific verb ('save') and resource ('data'), and distinguishes itself from sibling tools like 'recall' and 'forget' by mentioning pairing with them.

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

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

The description explicitly states when to use the tool: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It also provides guidance on pairing with 'recall' and 'forget.' However, it does not explicitly state when not to use it or contrast with alternative tools.

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

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

No annotations are provided, so the description carries full burden. It states it performs a lookup (implying read-only) and returns IDs with citation URIs. However, it does not explicitly guarantee non-destructiveness or address potential side effects, which is acceptable for a lookup tool but leaves some ambiguity.

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, using only four sentences to convey purpose, inputs, outputs, and usage context. It is front-loaded with the core function, then details, and ends with practical advice. Every sentence adds value without redundancy.

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

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

Given the lack of output schema, the description adequately explains what the tool returns (IDs plus citation URIs). It also places the tool in a workflow context (use before other tools). For a lookup tool with two well-described parameters, this is complete.

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

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

The schema already provides descriptions for both parameters (100% coverage). The description adds value by offering concrete examples (e.g., 'Apple' → AAPL) and clarifying that 'value' accepts tickers, CIKs, or names for companies and brand/generic names for drugs, enhancing usability.

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 canonical/official identifiers for companies or drugs, specifying specific ID systems (CIK, ticker, RxCUI, LEI). It distinguishes itself from sibling tools like entity_profile by emphasizing its role in providing inputs for other tools.

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

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

The description explicitly advises using this tool before others that need official identifiers and provides examples of when to use it. While it doesn't list exclusions or when not to use, the context is clear.

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

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

Description details the probing process, ranking, and output (score, confidence, signal density). Annotations (readOnlyHint, idempotentHint) align with description; 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?

Four sentences, front-loaded with main purpose. Every sentence adds value: purpose, method, use case, output. 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 fully covers output (ranked list with score, confidence, signal density). Annotations and schema richness complement the description.

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

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

Schema coverage is 100%, and description adds context (e.g., 'first entry treated as the subject,' examples for context). Adds 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?

The description clearly states 'Compare AI visibility across multiple entities side-by-side,' using a specific verb and resource. It differentiates from sibling tools like 'ai_visibility_check' by indicating it probes multiple entities and ranks them.

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

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

Provides explicit use case ('competitive AI-marketing audits') and an example query. Implicitly contrasts with single-entity probing. However, does not explicitly list when not to use or name alternative tools.

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

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

Annotations already mark it as read-only, idempotent, and non-destructive. The description adds rich behavioral detail: composite nature, partial failure tolerance, timeout for bundlephobia's first measurement, and graceful degradation with sources_failed field.

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

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

The description is a single, well-organized sentence that packs essential information without fluff. It front-loads the core purpose and systematically covers scope, usage, return values, and error handling.

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 (two data sources, partial failures, multiple return fields) and no output schema, the description fully explains the return structure, links, and failure behavior. No gaps for an agent to guess.

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

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

Schema coverage is 100% with clear descriptions for both parameters. The description does not add significant parameter-level meaning beyond what the schema provides, but it does reinforce the composite nature. Baseline 3 is appropriate.

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

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

The description clearly defines it as a composite npm package check combining deps.dev (license, advisories, version history) and bundlephobia (size, dependencies, ESM). It distinguishes from siblings by specifying the NPM ecosystem focus and noting that other ecosystems fall under a different tool.

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

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

Provides explicit usage context: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also clarifies when NOT to use it (PyPI/Maven/Cargo/Go) pointing to deps.dev:version directly, and warns about bundlephobia latency.

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

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

The description discloses that it returns a ranked list with share percentages, but does not mention order, pagination, permissions, or error handling. With no annotations, it is adequate but minimal.

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

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

Three concise sentences: general purpose, metric explanations, return format. No wasted words.

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

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

Given no output schema, the description covers return format. All parameters and their defaults are implicitly covered via schema. For a simple list tool, this is sufficient.

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

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

The schema already covers parameters adequately, but the description adds value by explaining what each metric means (e.g., 'metric=http_requests returns countries by share of HTTP traffic'), which the schema does not provide.

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 returns top locations ranked by a metric, and distinguishes three metrics with their specific meanings (HTTP traffic, DNS queries, attack origin). This specificity sets it apart from siblings like attack_summary or entity_profile.

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

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

The description explains when to use each metric variant, providing clear context. However, it lacks explicit guidance on when not to use this tool or alternatives among siblings.

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

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

With no annotations provided, the description carries full burden. It discloses the return values (verdict types, actual value with citation, percent delta) and limitations (v1 supports company-financial claims via SEC EDGAR+XBRL). It does not mention destructive actions or auth needs, which are not expected for a read-only 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 a single paragraph but front-loaded with purpose, usage, domain, and returns. Every sentence contributes value, though it could be slightly more structured for readability.

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

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

Given the tool's simplicity (1 parameter, no output schema, no annotations), the description thoroughly covers purpose, usage, domain, return types, and efficiency gains. It provides all necessary context for an agent to select and invoke the tool correctly.

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

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

Schema coverage is 100% with a parameter description for 'claim'. The description adds significant meaning by providing example claims and clarifying that the input is natural language, enhancing what the schema provides.

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

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

The description clearly states the verb (fact-check, verify, validate) and the resource (natural-language factual claims against authoritative sources). It distinguishes itself from sibling tools like ask_pipeworx or resolve_entity as the only tool for fact-checking 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 specifies when to use the tool: when an agent needs to check if something a user said is true, with example phrasings. It also notes the domain (company-financial claims) and that it replaces multiple sequential calls, but does not explicitly list cases where it should not be used or provide alternative tools.

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