Apollo Intelligence Network

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 mentions pricing ('$0.12/query') which hints at a paid service with potential rate limits or costs, but doesn't disclose other behavioral traits like what the tool returns, error conditions, or performance characteristics. The description is minimal and lacks operational details.

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

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

The description is very short (one sentence) and front-loaded with the bundle components, but it wastes space on pricing details ('$0.18 value for $0.12', '$0.12/query via x402') that don't help an AI agent understand how to use the tool. It's concise but not effectively structured for tool selection.

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 (a bundle combining multiple data types), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the bundle returns, how the data is structured, or any limitations. The pricing info is insufficient to compensate for these gaps, making it inadequate for an AI agent to use effectively.

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 tool has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so a baseline score of 4 is appropriate as it doesn't have to compensate for any gaps.

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

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

The description states it's a bundle of 'Trends + Pain Points + Agent Intel' and mentions pricing, but it's vague about what the tool actually does (e.g., what data it returns, what 'bundle' means operationally). It doesn't clearly distinguish from siblings like 'agentic_trends', 'pain_points', or 'agent_intel', which appear to be individual components of this bundle. The description focuses on pricing rather than 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?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or comparisons to sibling tools like 'agentic_trends' or 'pain_points'. The pricing information might imply a cost-benefit consideration, but it's not framed as explicit usage guidance.

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

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

No annotations are provided, so the description carries the full burden. It mentions a cost ('$0.05/query'), which is useful behavioral context about pricing. However, it lacks other critical details such as rate limits, authentication needs, data freshness, or what happens on invocation (e.g., returns a list, triggers an action). For a tool with no annotations, this is insufficient.

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

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

The description is concise with two short phrases, front-loading the purpose ('Agentic economy signals') and including operational details. There's minimal waste, though the structure could be slightly improved by clarifying the action verb. Every sentence adds value, making it efficient.

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

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

Given the complexity (a data-fetching tool with no output schema and no annotations), the description is incomplete. It doesn't explain what the return values are (e.g., format, structure, examples) or provide enough behavioral context for safe use. With no output schema, the description should compensate by detailing expected outputs, but it fails to do so.

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 tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for this scenario is 4, as the description doesn't need to compensate for any parameter documentation gaps. It appropriately avoids discussing parameters.

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

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

The description states the tool provides 'agentic economy signals' covering 'funding, launches, partnerships, market signals', which gives a general purpose. However, it's vague about what specific action the tool performs (e.g., search, retrieve, analyze) and doesn't clearly distinguish from siblings like 'agentic_insights_bundle' or 'agent_intel'. The mention of '$0.05/query' is operational but doesn't clarify the core function.

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

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

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention any context, prerequisites, or exclusions for usage. With many sibling tools (e.g., 'crypto_prices', 'economic_indicators'), there's no indication of how this tool fits into the broader ecosystem or when it's the appropriate choice.

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 a cost ('$0.05/query'), which is useful behavioral context, but lacks other critical details such as rate limits, authentication needs, data freshness, or what happens on invocation (e.g., returns a list, generates a report). For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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

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

The description is concise and front-loaded, stating the purpose and cost in a single sentence. There's no wasted text, but it could be slightly more structured by explicitly mentioning the action (e.g., 'Retrieves curated...') to improve clarity without adding bulk.

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 annotations and no output schema, the description is incomplete for a tool that likely returns complex economic data. It mentions cost but omits details on output format, data scope, or error handling. For a tool in a server with many data-focused siblings, this leaves the agent under-informed about 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 has 100% description coverage, with the 'category' parameter well-documented in the schema itself. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 for high schema coverage without compensating 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 states the tool provides 'curated agent economy opportunities' with examples like 'investment signals, partnerships, launches', which gives a general purpose. However, it's vague about what specific action the tool performs (e.g., search, list, analyze) and doesn't clearly distinguish it from sibling tools like 'opportunity_bundle' or 'agentic_insights_bundle', which might offer similar economic insights.

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 is provided on when to use this tool versus alternatives. The description mentions a cost ('$0.05/query via x402'), which implies a usage consideration, but it doesn't specify contexts, prerequisites, or comparisons to sibling tools like 'opportunity_bundle' or 'crypto_trending' that might overlap in domain.

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

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

The description adds valuable context beyond annotations: it specifies the tool is 'FREE' and 'No payment required' (addressing cost concerns), and describes the output format ('recommended tools by category with example calls'). Annotations already cover read-only, open-world, idempotent, and non-destructive traits, so the description appropriately supplements rather than contradicts 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 extremely concise (two short sentences) and front-loaded with the core purpose. Every word earns its place: the first sentence explains what it does, the second adds critical behavioral context (free, output format). No wasted words or 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?

For a simple single-parameter tool with comprehensive annotations (read-only, open-world, idempotent, non-destructive) and no output schema, the description is complete: it explains the purpose, usage context, behavioral traits (free, output format), and distinguishes it from siblings. No significant gaps exist given the tool's low complexity.

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

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

Schema description coverage is 100%, so the schema fully documents the single 'task' parameter. The description does not add any parameter-specific details beyond what's in the schema, but it implies the parameter's purpose by mentioning 'your task'. Baseline 3 is appropriate given the high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('Discover which Apollo tools match your task') and resources ('Apollo tools'), and explicitly distinguishes it from sibling tools by explaining it returns recommendations rather than performing the tasks themselves. The mention of 'No payment required' further clarifies its unique free nature.

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

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

The description provides explicit guidance on when to use this tool ('Discover which Apollo tools match your task') and implicitly when not to use it (for actual task execution, as it only returns recommendations). It distinguishes this as a meta-tool for tool discovery versus the many sibling tools that perform specific tasks.

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

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

No annotations are provided, so the description carries the full burden. It discloses cost ('$0.05/query via x402'), which is useful behavioral context beyond the input schema. However, it lacks details on rate limits, authentication needs, error handling, or what the response looks like (since no output schema). This is a moderate disclosure given the mutation risk is low (it's 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?

The description is a single, efficient sentence that front-loads the core purpose and includes cost information. There is zero waste; every word earns its place, making it highly concise and well-structured.

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

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

Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is somewhat complete: it covers purpose and cost but lacks details on output format, error cases, or usage scenarios. It's adequate as a minimum viable description but has clear gaps in behavioral and output context.

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

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

Schema description coverage is 100%, so the schema already documents all 4 parameters (email, full, breach, list_all). The description does not add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain the relationship between parameters like 'list_all' and 'email'). Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with a specific verb ('Check') and resource ('if an email appears in known data breaches'), and it identifies the data source ('HaveIBeenPwned'). It distinguishes this tool from siblings by focusing on email breach checking, unlike other tools for DNS, IP, crypto, etc.

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 minimal usage guidance: it mentions a cost ('$0.05/query via x402'), which implies when to use based on budget, but does not specify when to use this tool versus alternatives (e.g., other security tools in the list like 'ip_reputation' or 'malware_url_check') or any prerequisites. No explicit when/when-not rules or named alternatives are provided.

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 discloses pricing and bundling but lacks critical behavioral traits: what the output looks like (formats, structure), rate limits, authentication needs, error handling, or whether it's a read-only or mutating operation. The mention of 'via x402' is unclear without context, adding confusion rather than transparency.

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

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

The description is brief (one sentence) and front-loaded with key information (bundle components and pricing), but it includes unclear jargon ('via x402') that doesn't earn its place. It could be more structured by separating purpose from pricing details for better 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?

Given the complexity of a bundled intelligence tool with no annotations and no output schema, the description is incomplete. It fails to explain what the bundle actually returns (e.g., combined reports, aggregated data), how it integrates the sources, or any operational constraints. This leaves significant gaps for an AI agent to understand and invoke the tool effectively.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and pricing. This meets the baseline for tools with no parameters, as it doesn't need to compensate for schema gaps.

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 provides a bundled intelligence service combining GitHub, Product Hunt, and Agent Intel at a discounted price, which gives a general purpose. However, it's vague about what specific intelligence or data is provided (e.g., trends, metrics, insights) and doesn't clearly distinguish it from sibling tools like 'agentic_insights_bundle' or 'opportunity_bundle' that might offer similar bundled services.

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

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

The description mentions pricing ($0.10/query via x402) and value comparison ($0.15 value for $0.10), which implies a cost-benefit context, but provides no explicit guidance on when to use this tool versus alternatives like individual tools (e.g., 'github_trending', 'producthunt', 'agent_intel') or other bundles. No prerequisites, exclusions, or specific use cases are stated.

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 for behavioral disclosure. It mentions the cost per query ('$0.02/query via x402'), which is valuable operational context. However, it fails to describe critical behaviors: whether this is a read-only operation, what format the data returns, if there are rate limits, authentication requirements, or error conditions. For a data query tool with zero annotation coverage, this leaves significant gaps.

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

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

The description is efficiently structured in a single sentence that communicates the core purpose, scope, and cost. Every element (data source, country count, pricing) serves a purpose. However, the pricing detail ('via x402') is somewhat cryptic and could be clearer, preventing a perfect score for absolute 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?

For a data query tool with 4 parameters, no annotations, and no output schema, the description is incomplete. While it covers the 'what' (World Bank indicators) and cost, it lacks crucial context: expected output format, error handling, authentication needs, rate limits, and practical usage examples. The agent would struggle to use this tool effectively without trial-and-error or external documentation.

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 schema already fully documents all four parameters. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain indicator code formats, country code standards beyond what's in the schema, or provide examples of typical queries. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.

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 'World Bank country development indicators' for '260+ countries', specifying both the data source and scope. It distinguishes itself from siblings like 'economic_indicators' by focusing specifically on World Bank country metrics. However, it doesn't specify the exact verb (e.g., 'retrieve', 'fetch', 'get') which prevents a perfect score.

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 'economic_indicators' or 'fx_rates'. It mentions a cost ('$0.02/query') which could imply usage considerations, but offers no explicit when/when-not instructions or comparison with sibling tools. The agent must infer usage context from the tool name alone.

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 the full burden of behavioral disclosure. It adds valuable context about the data source (CoinGecko), cost structure ($0.01/query via x402), and the specific data fields returned. However, it doesn't mention rate limits, authentication requirements, error conditions, or what format/latency to expect from 'live' prices.

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

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

The description is extremely concise and front-loaded with essential information in a single sentence. Every element (data source, data fields, cost) earns its place with zero wasted words or redundant information.

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

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

For a 2-parameter tool with no annotations and no output schema, the description provides adequate basic context about what the tool does and its cost structure. However, it lacks information about return format, error handling, rate limits, and how the 'live' aspect works. The presence of a similar sibling tool ('crypto_prices_live') creates ambiguity that isn't 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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions general data fields (market cap, 24h change, volume) but doesn't connect these to specific parameters or their 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 clearly states the tool's function: retrieving live cryptocurrency prices from CoinGecko with specific data fields (market cap, 24h change, volume). It distinguishes from siblings by specifying the data source (CoinGecko) and cost structure, though it doesn't explicitly differentiate from 'crypto_prices_live' which appears to be a similar sibling tool.

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

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

The description implies usage context by mentioning the data source (CoinGecko) and cost ($0.01/query), but provides no explicit guidance on when to use this tool versus alternatives like 'crypto_prices_live' or 'crypto_trending'. The cost information suggests usage considerations but doesn't define specific 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?

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: real-time nature, data source (CoinGecko), scope (10,000+ coins), cost implications ($0.02/query), and search support. It doesn't mention rate limits, authentication needs, or error handling, but covers essential operational 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 perfectly concise and front-loaded: first sentence establishes core purpose, second adds scope and key return fields, third mentions search capability, fourth provides cost information. Every sentence earns its place with zero wasted words.

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

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

For a data retrieval tool with no output schema, the description does well by listing return fields (price, market cap, etc.) and operational context (cost, real-time). However, without annotations or output schema, it could benefit from mentioning response format or error cases. The 100% schema coverage helps compensate.

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

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

Schema description coverage is 100%, providing good parameter documentation. The description adds some value by mentioning 'search by symbol' which aligns with the 'search' parameter, but doesn't provide additional semantic context beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verbs ('returns price, market cap, 24h volume, changes, ATH') and resources ('cryptocurrency prices from CoinGecko, 10,000+ coins'). It distinguishes from sibling 'crypto_prices' by specifying 'live' real-time data and mentioning search capability.

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

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

The description provides clear context for when to use this tool ('Real-time cryptocurrency prices') and mentions search functionality. However, it doesn't explicitly state when NOT to use it or name alternatives among siblings (like 'crypto_prices' or 'crypto_trending').

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 the full burden of behavioral disclosure. It adds useful context about the data source (CoinGecko) and cost ($0.02/query), but doesn't cover other important aspects like rate limits, authentication needs, error handling, or what the output looks like (e.g., format, fields).

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

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

The description is extremely concise—just one sentence that efficiently communicates the core functionality, data source, and cost. Every word earns its place with no redundancy or unnecessary elaboration.

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 moderate complexity (trending data with a cost factor), no annotations, and no output schema, the description is somewhat incomplete. It covers the what and how (via CoinGecko with cost), but lacks details on output format, error cases, or usage constraints beyond cost. However, for a single-parameter tool, it provides a reasonable foundation.

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

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

The input schema has 100% description coverage for its single parameter ('limit'), so the baseline is 3. The description doesn't add any parameter-specific information beyond what's in the schema, but since there's only one parameter and it's well-documented, this is acceptable. The description does imply the tool returns a list of trending items, which aligns with the 'limit' parameter's purpose.

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

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

The description clearly states the tool's purpose: retrieving trending cryptocurrencies based on search volume from CoinGecko. It specifies the verb ('Trending') and resource ('cryptocurrencies'), but doesn't explicitly differentiate from sibling tools like 'crypto_prices' or 'crypto_prices_live' which might have overlapping 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 provides no guidance on when to use this tool versus alternatives. It mentions a cost ('$0.02/query via x402'), which hints at usage considerations, but doesn't specify scenarios, prerequisites, or comparisons with sibling tools like 'crypto_prices' that might serve similar purposes.

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 the full burden of behavioral disclosure. It successfully reveals key behavioral traits: it's a search operation (not a mutation), it has a cost implication ('$0.03/query'), and it specifies the data source ('NIST National Vulnerability Database'). However, it doesn't mention rate limits, authentication requirements, or error handling.

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

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

The description is extremely efficient - two sentences that pack substantial information: purpose, data source, return fields, and cost. Every word earns its place with zero wasted text. The structure is front-loaded with the core functionality.

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 search tool with no output schema and no annotations, the description provides good coverage of what the tool does, what it returns, and operational constraints (cost). It could be more complete by mentioning the format of returned data or any limitations, but given the tool's relatively straightforward nature, it's reasonably 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 description coverage is 100%, so the schema already fully documents all 4 parameters. The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the 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 specific action ('Search the NIST National Vulnerability Database'), the resource ('CVEs'), and the key return fields ('CVE IDs, CVSS scores, severity, affected products, references'). It distinguishes this tool from sibling tools by focusing on CVE vulnerability data rather than general web search, OSINT, or other domains.

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 context through the mention of 'NIST National Vulnerability Database' and cost ('$0.03/query'), but doesn't explicitly state when to use this tool versus alternatives like general web_search or other security tools. It provides some operational context but lacks explicit guidance on when this tool is preferred over other search methods.

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 the full burden. It discloses cost ('$0.02/query via x402'), which is valuable behavioral context. However, it lacks other important details like rate limits, authentication needs, or what specific data format is returned (e.g., JSON structure, error handling).

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

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

The description is extremely concise (one sentence) and front-loaded with the core purpose. Every word earns its place, with no wasted text or 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 annotations, no output schema, and 3 parameters with full schema coverage, the description is minimally adequate. It covers the purpose and cost, but lacks details on return values, error cases, or deeper behavioral context that would be helpful for an AI agent.

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

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

Schema description coverage is 100%, so the schema fully documents the three parameters (chain, limit, category). The description adds no parameter-specific information beyond what's in the schema, meeting the baseline of 3 when schema coverage is high.

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

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

The description clearly states the tool's purpose: retrieving DeFi protocol TVL rankings from DefiLlama. It specifies the resource (TVL rankings) and source (DefiLlama), but doesn't explicitly differentiate from sibling tools like 'defi_yields' or 'crypto_prices' beyond the TVL focus.

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. It mentions a cost ('$0.02/query via x402'), but doesn't explain when this tool is preferred over other DeFi or crypto-related tools in the sibling list, such as 'defi_yields' or 'crypto_prices'.

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 the full burden of behavioral disclosure. It mentions a cost per query, which is valuable context about rate limits or pricing. However, it doesn't describe what the tool returns (format, structure), whether it's read-only, potential rate limits beyond cost, error conditions, or data freshness - significant gaps for a tool with 5 parameters and no output schema.

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 appropriately concise with two sentences that each serve a purpose: the first states the core functionality and data source, the second mentions the cost. There's no wasted verbiage, though it could be slightly more structured by separating functional and operational 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 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (yield data format, fields included), how results are presented, or provide sufficient behavioral context. The cost mention is helpful but doesn't compensate for the missing information about return values and operational characteristics.

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 schema already documents all 5 parameters thoroughly with descriptions and constraints. The description adds no additional parameter information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.

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

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

The description clearly states the tool retrieves 'Top DeFi yields across 18K+ pools from DefiLlama' with a specific data source and scope. It distinguishes itself from sibling tools like 'crypto_prices' or 'defi_protocols' by focusing specifically on yield data. However, it doesn't explicitly contrast with all possible alternatives in the sibling list.

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 'defi_protocols' or other crypto-related tools. It mentions a cost ('$0.03/query via x402') which could influence usage decisions, but offers no explicit when/when-not instructions or comparison to 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it performs DNS lookups and email security audits, uses a local resolver (implying no external dependencies), has a cost ($0.01/query), and returns specific data types. However, it lacks details on rate limits, error handling, or authentication needs, which would be valuable for a tool with potential costs and technical operations.

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

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

The description is efficiently structured in two sentences: the first lists capabilities, and the second adds operational details (local resolver, cost). It is front-loaded with core functionality and avoids unnecessary fluff, though it could be slightly more concise by integrating cost information into the first sentence.

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 annotations and no output schema, the description adequately covers the tool's purpose and key behaviors (local resolver, cost). However, it lacks details on output format (e.g., structure of returned DNS records or security scores), error cases, or usage constraints, which are important for a tool with multiple parameters and potential financial implications.

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 schema already documents all three parameters (ip, types, domain) with clear descriptions and a default for 'types'. The description adds no additional parameter semantics beyond what the schema provides, such as explaining interactions between 'ip' and 'domain' or format specifics for 'types'. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool performs DNS intelligence and email security audit, listing specific DNS record types (A, AAAA, MX, NS, TXT, CNAME, SOA, CAA) and security analyses (reverse DNS, SPF/DMARC/DKIM analysis, email security posture score). It distinguishes from siblings like 'domain_intel' or 'whois' by emphasizing the comprehensive DNS and email security focus.

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 'domain_intel' or 'whois' from the sibling list. It mentions technical details (100% local resolver, zero external API, $0.01/query) but does not specify use cases, prerequisites, or exclusions, leaving the agent without contextual direction.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the cost per query, which is useful context about pricing/rate limits, but doesn't describe what the tool actually returns (format, structure), error conditions, authentication needs, or whether it's read-only vs. destructive.

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

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

The description is efficiently structured in a single sentence that covers purpose and cost. However, it could be more front-loaded by explicitly stating it's a lookup/query tool rather than implying it through 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?

For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what intelligence data is returned, in what format, or how to interpret results. The cost mention is helpful but doesn't compensate for missing behavioral and output context.

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

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

Schema description coverage is 100% with one parameter ('domain') fully documented in the schema. The description adds no additional parameter semantics beyond what the schema provides (e.g., no examples, format clarifications, or constraints). Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the tool provides 'multi-source domain intelligence' covering DNS, SSL, geo, and threats, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'dns_lookup', 'ip_intel', or 'whois' that might offer overlapping 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 provides no guidance on when to use this tool versus alternatives. It mentions a cost ('$0.03/query via x402') which could imply usage considerations, but doesn't specify scenarios, prerequisites, or comparisons to sibling tools like 'dns_lookup' or 'whois'.

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 the full burden of behavioral disclosure. It mentions the cost per query, which is valuable operational context, but doesn't describe what the tool returns (format, structure), whether it requires authentication, rate limits, error conditions, or how it handles missing data. For a data retrieval tool with zero annotation coverage, this is insufficient.

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 appropriately concise with two clear components: what data it provides and the cost. Both sentences earn their place. It could be slightly more structured by separating functionality from operational details, but it's efficient with zero waste.

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

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

Given no annotations and no output schema, the description is incomplete for a data retrieval tool. It doesn't explain what format the data returns (tabular, JSON, time series), how indicators are presented, or what happens when invalid indicators are requested. The cost information is helpful, but overall context is lacking for proper agent usage.

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

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

Schema description coverage is 100%, so the schema already documents all 4 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting, though the description could have explained relationships between parameters like 'limit' and 'years'.

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 Federal Reserve (FRED) economic data with specific examples (GDP, CPI, unemployment, rates). It provides a specific verb ('economic data') and resource ('FRED'), but doesn't explicitly differentiate from sibling tools like 'country_metrics' or 'fx_rates' which might also provide economic indicators.

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. It mentions a cost ('$0.03/query via x402') which is useful context, but doesn't indicate when this tool is preferable to similar economic data tools or what scenarios it's designed for.

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 the full burden of behavioral disclosure. It usefully adds cost information ($0.03/query) and the data source (Etherscan), which are important operational details. However, it doesn't mention rate limits, error conditions, authentication requirements, or what format the balance is returned in.

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

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

The description is extremely concise - just one sentence that efficiently communicates the core functionality, data source, and cost. Every word earns its place with no wasted text.

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 single-parameter tool with no output schema and no annotations, the description provides adequate but minimal context. It covers what the tool does and operational cost, but doesn't explain return format, error handling, or when to choose this over similar tools, leaving some gaps in 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 the schema already fully documents the single 'address' parameter. The description doesn't add any additional parameter semantics beyond what's in the schema, maintaining the baseline score for high schema coverage.

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

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

The description clearly states the tool's purpose: retrieving Ethereum wallet balances via Etherscan. It specifies the resource (Ethereum wallet) and the action (balance retrieval), though it doesn't explicitly differentiate from sibling tools like 'wallet_osint' or 'crypto_prices'.

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. While it mentions the data source (Etherscan) and cost ($0.03/query), it doesn't indicate when this specific balance tool is preferred over other crypto-related tools in the sibling list.

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 the full burden of behavioral disclosure. It adds useful context: the data source (Etherscan), the real-time nature ('Live'), and the cost implication. However, it lacks details on rate limits, error handling, or response format, which are important for a tool with external dependencies and costs.

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

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

The description is extremely concise and front-loaded, consisting of a single sentence that efficiently conveys the core functionality, data source, and cost. Every word earns its place with no redundancy or unnecessary details.

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

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

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It covers the purpose and cost, but lacks information on output format, error conditions, or integration specifics, which could be helpful for an AI agent invoking this tool in a broader context.

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

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

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the inputs. The description doesn't need to add parameter information, and it appropriately doesn't mention any. This meets the baseline of 4 for zero-parameter tools.

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

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

The description clearly states the tool's purpose: retrieving live Ethereum gas prices via Etherscan. It specifies the data source (Etherscan) and the type of information (gas prices), though it doesn't explicitly differentiate from sibling tools like 'crypto_prices' or 'eth_transactions' beyond the specific focus on gas prices.

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. It mentions a cost ($0.02/query via x402), which hints at a usage consideration, but doesn't specify scenarios, prerequisites, or comparisons to sibling tools like 'crypto_prices' or 'eth_balance' that might overlap in crypto-related 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the cost ($0.03/query) which is valuable context about pricing, but doesn't describe rate limits, authentication requirements, error conditions, response format, or whether this is a read-only operation. For a tool with 3 parameters and no annotations, this leaves significant behavioral gaps.

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

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

The description is extremely concise (one sentence) and front-loaded with the core purpose. Every word earns its place, though it could potentially benefit from slightly more context about what 'transaction history' includes. No wasted words or redundant information.

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

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

For a tool with 3 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what data is returned (transaction details, timestamps, amounts), how pagination works, error handling, or typical use cases. The cost information is helpful but insufficient for a complete understanding of the tool's behavior.

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 description adds no parameter-specific information beyond what's already in the schema (which has 100% coverage). It doesn't explain the relationship between page/offset parameters, typical values, or address format requirements. With complete schema coverage, the baseline is 3, but the description doesn't enhance parameter understanding.

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

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

The description clearly states the tool's purpose: retrieving Ethereum transaction history via Etherscan. It specifies the resource (Ethereum transactions) and the data source (Etherscan), making the verb+resource relationship explicit. However, it doesn't distinguish this tool from its sibling 'eth_balance' or other crypto-related tools, which prevents a perfect score.

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. While it mentions the cost ($0.03/query via x402), it doesn't explain when this tool is appropriate compared to other transaction history tools or what specific use cases it addresses. No exclusions or prerequisites are mentioned.

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 the full burden of behavioral disclosure. It mentions the cost per query, which is valuable context, but doesn't describe other important behaviors like rate limits, authentication requirements, error conditions, or what the response format looks like. For a search tool with external API costs, more behavioral context would be helpful.

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 appropriately concise with two clear components: the core functionality and the cost information. Both sentences earn their place, though the structure could be slightly improved by front-loading the most critical information more explicitly.

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 moderate complexity (external API with costs), lack of annotations, and no output schema, the description is somewhat incomplete. It covers the basic purpose and cost but misses important context about response format, error handling, and usage constraints that would help an agent use it effectively.

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 description doesn't provide any parameter-specific information beyond what's already in the schema (which has 100% coverage). The schema already documents the single 'query' parameter as accepting 'Drug name, ingredient, or condition.' The description adds no additional semantic context about parameter usage 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?

The description clearly states the tool's purpose: searching OpenFDA drug labels for specific information categories (ingredients, warnings, dosage, interactions). It uses specific verbs ('search') and resources ('OpenFDA drug labels'), but doesn't distinguish from siblings since there are no other drug-related tools in the sibling list.

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. While it mentions a cost ('$0.03/query'), it doesn't specify when this tool is appropriate versus other search tools in the sibling list or what prerequisites might be needed.

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

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

With no annotations provided, the description carries the full burden. It discloses the data source (ECB), cost per query, and live nature of rates, which are useful behavioral traits. However, it misses details like rate limits, error handling, or response format, leaving gaps for a tool with potential financial implications.

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

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

The description is extremely concise—two sentences that efficiently cover purpose, scope, source, and cost. Every word earns its place with no redundancy, making it front-loaded and easy to parse quickly.

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 annotations and no output schema, the description provides basic context (source, cost, live data) but lacks details on return values, error cases, or operational constraints. For a financial data tool with potential cost implications, this is minimally adequate but leaves room for improvement in 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 the schema fully documents the two parameters (base and symbols). The description adds no additional parameter semantics beyond what's in the schema, such as examples or constraints. This meets the baseline score of 3 for high schema coverage.

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

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

The description clearly states the tool provides 'Live FX rates for 30+ currencies from ECB,' specifying the resource (FX rates), source (ECB), and scope (30+ currencies). However, it doesn't explicitly differentiate from sibling tools like 'crypto_prices' or 'economic_indicators,' which might also provide financial data, leaving some ambiguity about its unique 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 includes cost information ('$0.005/query via x402'), which hints at usage context, but it lacks explicit guidance on when to use this tool versus alternatives (e.g., 'crypto_prices' for cryptocurrencies or 'economic_indicators' for broader data). No when-not-to-use scenarios or prerequisites are mentioned.

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 the full burden. It discloses the service provider (Nominatim/OpenStreetMap) and cost per query, which are valuable behavioral traits. However, it misses details like rate limits, authentication needs, error handling, or whether it's read-only/destructive—critical for a paid external API 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 extremely concise—two brief phrases that efficiently convey purpose, method, and cost. Every word earns its place with no redundancy or fluff, making it easy to parse quickly.

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 5 parameters, no annotations, and no output schema, the description is incomplete. It covers the 'what' and 'how much' but lacks details on behavioral constraints (e.g., rate limits), output format, error cases, or prerequisites. Given the complexity and missing structured data, it should provide more operational context.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by clarifying the dual functionality (forward/reverse geocoding), which helps interpret parameters like 'q' vs. 'lat/lon'. It doesn't provide additional syntax or format details beyond the schema, but the context elevates it slightly.

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 with specific verbs ('forward + reverse geocoding') and identifies the service provider ('via Nominatim/OpenStreetMap'). It distinguishes itself from siblings like 'ip_geo' by specifying the geocoding method and data source.

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 implicitly indicates when to use the tool (for geocoding tasks) and mentions a cost ('$0.01/query'), which provides usage context. However, it lacks explicit guidance on when to choose this over alternatives like 'ip_geo' or other location-based tools in the sibling list.

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 cost ('$0.05/query'), which is a useful behavioral trait not in the schema. However, it lacks other critical details: it doesn't mention rate limits, authentication needs, error handling, or what the output looks like (e.g., list format, pagination). For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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

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

The description is brief and front-loaded: the first part clearly states the purpose, and the second adds cost information. Both sentences earn their place by providing essential context. However, it could be slightly more structured (e.g., separating purpose from metadata), but it avoids redundancy and waste.

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

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

Given the complexity (a query tool with 4 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain the return format (e.g., what data fields are included), error conditions, or usage constraints beyond cost. For a tool that likely returns structured data, more context is needed to help the agent use it effectively.

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 schema already fully documents all 4 parameters (limit, since, topic, language). The description adds no parameter-specific information beyond what's in the schema—it doesn't explain how 'star velocity' is calculated or how filters interact. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.

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: 'GitHub repos ranked by star velocity — discover trending open source.' It specifies the verb ('ranked'), resource ('GitHub repos'), and metric ('star velocity'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'crypto_trending' or 'producthunt', which might also show trending items in other domains.

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. It mentions a cost ('$0.05/query via x402'), which could imply usage considerations, but doesn't specify scenarios, prerequisites, or comparisons with other tools (e.g., 'web_search' or 'crypto_trending'). Without explicit when-to-use or when-not-to-use instructions, the agent lacks context for selection.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the cost per query, which is useful context, but lacks other critical details: whether this is a read-only operation, rate limits, authentication requirements, error handling, or what the output looks like (especially since there's no output schema).

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

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

The description is extremely concise—just one sentence with zero wasted words. It front-loads the core functionality and includes cost information efficiently. Every element earns its place.

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

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

Given the tool's complexity (geolocation/ASN lookup with cost implications), lack of annotations, and no output schema, the description is incomplete. It should explain what information is returned, error conditions, or usage constraints beyond cost. The cost mention is helpful but insufficient for full context.

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

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

Schema description coverage is 100%, so the schema fully documents both parameters. The description adds no additional parameter information beyond what's in the schema. This meets the baseline of 3 for high schema coverage, but doesn't compensate with extra semantic context.

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

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

The description clearly states the tool's function: 'IP geolocation + ASN lookup via GeoJS.' It specifies both geolocation and ASN lookup, and mentions the service provider (GeoJS). However, it doesn't explicitly differentiate from sibling tools like 'ip_intel' or 'ip_reputation,' which prevents a perfect score.

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. It mentions the cost ('$0.01/query via x402'), which could imply usage considerations, but offers no explicit when/when-not instructions or comparisons to sibling IP-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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about cost per query and the multi-source nature, but lacks details on rate limits, authentication needs, error handling, or what specific data is returned (e.g., format of vulnerabilities). This is adequate but has clear gaps for a tool with no annotation coverage.

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

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

The description is extremely concise and front-loaded, packing essential information into a single sentence with zero waste. It efficiently covers the tool's purpose, scope, and cost without unnecessary elaboration.

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 (intelligence gathering with multiple data types), no annotations, and no output schema, the description is minimally adequate. It covers the core purpose and cost but lacks details on output format, error cases, or integration with siblings, leaving gaps that could hinder effective use by an AI agent.

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

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

The input schema has 100% description coverage, with the 'ip' parameter clearly documented as an 'IPv4 address to investigate'. The description doesn't add any meaning beyond this, such as examples or constraints, so it meets the baseline of 3 where the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose as providing 'multi-source IP intelligence' with specific categories (geolocation, open ports, vulnerabilities, threats), which is a clear verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'ip_geo' or 'ip_reputation', which appear to offer overlapping functionality, preventing a perfect score.

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 'ip_geo' or 'ip_reputation' from the sibling list. It mentions a cost ('$0.03/query via x402'), which hints at a usage consideration, but doesn't explain when this tool is preferred over other IP-related tools or general contexts 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the external service (AbuseIPDB) and cost implications, which is valuable context. However, it doesn't describe what the tool actually returns (score format, confidence levels, error conditions), rate limits, authentication requirements, or whether it's a read-only 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?

The description is extremely concise - just two brief phrases that communicate essential information. Every element earns its place: the core function (IP abuse scoring), the service provider (AbuseIPDB), and cost implications. No wasted words.

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

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

For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns (score format, data structure), error handling, or practical usage considerations. The cost mention is helpful but doesn't compensate for missing behavioral and output 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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema. The baseline score of 3 is appropriate when the schema does the heavy lifting for parameter 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 states the tool's purpose: 'IP abuse scoring via AbuseIPDB' specifies the verb (scoring) and resource (IP abuse) using a specific service. However, it doesn't differentiate from sibling tools like 'ip_geo' or 'ip_intel' which might handle different IP-related functions.

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

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

The description provides no guidance on when to use this tool versus alternatives. While it mentions the service (AbuseIPDB) and cost ($0.03/query), it doesn't explain when this specific abuse scoring is appropriate compared to other IP tools in the sibling list.

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 of behavioral disclosure. It mentions a cost ('$0.05/query'), which is useful context about pricing, but lacks details on rate limits, authentication needs, output format, or potential side effects. The phrase 'keyword flywheel' is ambiguous and doesn't clarify operational behavior.

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

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

The description is brief and front-loaded with the core functionality ('keyword flywheel — seed keyword expansion with opportunity scoring'), followed by cost information. It avoids unnecessary elaboration, though the metaphorical term 'flywheel' could be more precise.

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 annotations and no output schema, the description is incomplete for a tool that likely returns complex data (keyword expansions with scores). It lacks details on output structure, error handling, or behavioral traits beyond cost, leaving significant gaps for an AI agent to understand its full context.

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

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

Schema description coverage is 100%, with the single parameter 'seed' documented as 'Seed keyword to expand'. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. With high schema coverage, the 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 states the tool performs 'keyword flywheel — seed keyword expansion with opportunity scoring', which indicates it expands keywords and scores opportunities. However, it's somewhat vague about the exact mechanism ('flywheel' is metaphorical) and doesn't clearly differentiate from sibling tools like 'opportunity_bundle' or 'web_search' that might also involve keyword analysis.

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

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

No explicit guidance is provided on when to use this tool versus alternatives. The description mentions a cost ('$0.05/query via x402'), which implies a pricing consideration, but doesn't specify use cases, prerequisites, or comparisons to sibling tools like 'opportunity_bundle' or 'web_search' for keyword-related tasks.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the cost ('$0.02/query via x402'), which is valuable context about pricing. However, it doesn't describe rate limits, authentication requirements, response format, pagination, or what constitutes a 'query' (per call? per result?). For a threat intelligence tool with 7 parameters, this leaves significant behavioral gaps.

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 appropriately concise with two clear components: the service description and pricing information. Both sentences earn their place by providing essential context. However, the structure could be improved by front-loading the core purpose more explicitly.

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 threat intelligence tool with 7 parameters, no annotations, and no output schema, the description is incomplete. It lacks information about response format, error conditions, authentication requirements, and how results are structured. The pricing mention is helpful but insufficient to compensate for missing behavioral and output context.

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

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

Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema. The baseline score of 3 reflects adequate coverage via schema alone, though the description doesn't enhance parameter understanding.

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

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

The description clearly states the tool provides 'URLhaus malware URL feed — real-time threat intel from abuse.ch', which specifies the resource (malware URL feed) and source (URLhaus/abuse.ch). It distinguishes from sibling 'malware_url_check' by offering a feed rather than a single URL check. However, it doesn't explicitly contrast with other intel tools like 'ip_intel' or 'domain_intel'.

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. While it mentions a cost ('$0.02/query via x402'), it doesn't explain when to choose this over the sibling 'malware_url_check' or other threat intelligence tools. No context about appropriate use cases or prerequisites is provided.

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 the full burden of behavioral disclosure. It mentions the cost per query, which is valuable operational context, but fails to describe critical behaviors: whether the tool makes external API calls, what the response format looks like, if there are rate limits beyond the cost, or what happens when 'wait' is false. For a security scanning tool with no annotation coverage, this leaves significant gaps.

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

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

The description is extremely concise at just two short phrases, with zero wasted words. It front-loads the core purpose ('VirusTotal URL scanning') and follows with important operational context (cost). Every element earns its place, making it highly efficient.

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

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

Given the complexity of a security scanning tool with no annotations and no output schema, the description is incomplete. It lacks information about what the scan returns (threat indicators, confidence scores, etc.), error conditions, authentication requirements, or how results are structured. The cost mention is helpful but insufficient for an agent to understand the tool's full behavior and outputs.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('url' and 'wait') adequately. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline score of 3 reflects that the schema does the heavy lifting, and the description doesn't compensate with additional semantic context about parameter 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 clearly states the tool's purpose as 'VirusTotal URL scanning' which specifies the action (scanning) and resource (URLs). It distinguishes itself from siblings like 'malware_feed' or 'ip_reputation' by focusing specifically on URL scanning. However, it doesn't explicitly mention what the scanning detects (malware, threats, etc.), which prevents a perfect score.

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. While it mentions the cost ('$0.05/query via x402'), it doesn't indicate when URL scanning is appropriate compared to other malware/intel tools in the sibling list, nor does it mention prerequisites or exclusions. The cost information is useful but doesn't constitute 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions a cost ('$0.10/query'), which is useful context about pricing, but doesn't cover other traits like rate limits, authentication needs, error handling, or what 'validated' entails in terms of data sources or reliability. This leaves significant gaps for a tool that provides business insights.

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 highly concise and front-loaded, consisting of two sentences that efficiently convey the core functionality and cost. Every sentence adds value: the first explains what the tool does, and the second provides pricing information, with no wasted words or 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 complexity of providing validated business ideas with revenue estimates, and with no annotations or output schema, the description is incomplete. It lacks details on output format, data sources, validation criteria, or how competition analysis is structured. The cost mention is helpful, but overall, it doesn't provide enough context for effective tool use in this domain.

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 1 parameter with 100% description coverage ('Filter by vertical'), so the schema already documents it adequately. The description doesn't add any parameter-specific information beyond what's in the schema, such as examples of verticals or how filtering affects results. With high schema coverage, a baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Validated micro-SaaS ideas with revenue estimates and competition analysis.' It specifies the verb ('validated') and resource ('micro-SaaS ideas') along with key outputs. However, it doesn't explicitly differentiate from sibling tools like 'opportunity_bundle' or 'pain_points' that might also involve business ideas or opportunities.

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. It mentions a cost ('$0.10/query via x402'), which could imply usage for paid queries, but doesn't specify contexts, prerequisites, or comparisons to sibling tools like 'opportunity_bundle' or 'pain_points' that might overlap in functionality.

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 cost information, which is useful behavioral context, but fails to mention other traits like rate limits, authentication needs, or what the tool returns (e.g., entity types, confidence scores). This leaves significant gaps for a tool with no structured 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 brief and front-loaded with the core purpose, followed by cost details. Both sentences earn their place by providing essential information without waste, though it could be slightly more structured for 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?

Given no annotations and no output schema, the description is incomplete. It covers purpose and cost but lacks details on behavioral traits, return values, or usage context relative to siblings. For a tool with such sparse structured data, more comprehensive description is 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 description coverage is 100%, with the single parameter 'text' documented as 'Text to analyze.' The description adds no additional parameter semantics beyond this, so it meets the baseline of 3 where the schema handles the heavy lifting.

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

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

The description clearly states the tool performs 'Named entity recognition (NER) via Hugging Face,' which specifies the action (NER) and implementation method. However, it doesn't differentiate from sibling tools like 'ml_sentiment' or 'ml_summarize' that also use machine learning, missing explicit distinction.

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. It mentions cost ('$0.03/query via x402'), which hints at usage considerations, but lacks explicit when/when-not instructions or references to sibling tools for 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the cost per query, which is useful context, but fails to describe other critical behaviors such as rate limits, authentication needs, response format, or error handling. For a tool with no annotations, this leaves significant gaps in understanding how it operates.

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

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

The description is concise and front-loaded, consisting of a single sentence that states the purpose and includes cost information. There's no wasted text, and it efficiently conveys key information without unnecessary details.

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

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., sentiment scores, labels), how to interpret results, or any behavioral aspects beyond cost. For a tool performing sentiment analysis, this leaves the agent without sufficient context to use it effectively.

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

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

The input schema has 100% description coverage, with the single parameter 'text' clearly documented. The description doesn't add any additional meaning beyond what the schema provides, such as examples or constraints on text length. Given the high schema coverage, a baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.

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

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

The description clearly states the tool's purpose as 'Text sentiment analysis via Hugging Face,' which specifies both the action (sentiment analysis) and the resource (text). However, it doesn't explicitly differentiate from the sibling tool 'sentiment'—though the mention of Hugging Face and pricing might imply a different implementation, this distinction isn't made explicit.

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 implied usage context by mentioning the cost ('$0.03/query via x402'), which suggests when to consider cost implications, but it doesn't explicitly state when to use this tool versus alternatives like the sibling 'sentiment' tool or other analysis tools. No clear exclusions or prerequisites are mentioned.

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 of behavioral disclosure. It mentions cost ('$0.03/query via x402'), which is useful context for rate limits or pricing, but fails to describe other critical behaviors such as response format, error handling, latency, or any limitations (e.g., text length constraints). This leaves significant gaps for a tool with no annotation coverage.

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

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

The description is extremely concise and front-loaded, consisting of a single sentence that efficiently conveys the core purpose and cost. Every word earns its place, with no redundant or unnecessary information, making it easy to parse quickly.

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 (an ML summarization service with cost implications), lack of annotations, and no output schema, the description is incomplete. It misses key details like output format, error cases, usage limits, and how the summarization model behaves, which are essential for an AI agent to use the tool effectively in varied contexts.

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 schema already documents both parameters ('text' and 'max_length') with descriptions. The description does not add any meaning beyond what the schema provides, such as explaining parameter interactions or default behavior details. Baseline 3 is appropriate when the schema handles parameter documentation effectively.

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: 'Text summarization via Hugging Face.' It specifies the verb ('summarization') and resource ('text'), and distinguishes it from siblings by focusing on summarization rather than other ML tasks like sentiment analysis or entity extraction found in sibling tools (e.g., ml_sentiment, ml_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 implies usage context by mentioning 'via Hugging Face' and cost ('$0.03/query via x402'), which suggests it's for paid summarization services. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., other summarization methods or sibling tools like web_scrape for text extraction), and does not specify prerequisites or exclusions.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions cost ('$0.02/query') which is useful context, but doesn't describe rate limits, authentication requirements, error conditions, response format, or what happens when a package doesn't exist. For a query tool with zero annotation coverage, this leaves significant behavioral gaps.

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 appropriately concise with two brief sentences. The first sentence states the purpose, and the second provides cost information. There's no unnecessary verbiage, though the cost information might be better placed elsewhere in the tool definition.

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

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

For a single-parameter query tool with no output schema, the description provides basic purpose and cost information but lacks details about response format, error handling, or typical use cases. Given the simplicity of the tool (1 required parameter, no nested objects), the description is minimally adequate but could be more 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 description coverage is 100% with the single 'name' parameter fully documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline of 3 when schema coverage is high.

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 NPM package metadata including downloads, dependencies, versions, and maintainers, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'pypi_package' or 'github_trending' that might also provide package/repository metadata.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions a cost ('$0.02/query via x402') which could imply usage considerations, but doesn't specify when this tool is appropriate compared to other package metadata sources or 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 annotations are provided, so the description carries the full burden of behavioral disclosure. It only mentions pricing and value, with no information on what the tool does operationally, its output format, rate limits, or any behavioral traits. This leaves the agent with insufficient context to understand the tool's behavior.

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

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

The description is brief but not front-loaded with actionable information—it leads with pricing rather than purpose. It's concise in length, but the content is inefficient, as it fails to clearly state what the tool does, making it less helpful for an agent.

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 annotations and output schema, the description is incomplete. It doesn't explain what the tool returns or how it behaves, leaving significant gaps for an agent to understand and use the tool effectively in context with its siblings.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter semantics, but this is acceptable given the lack of parameters, aligning with the baseline for 0 parameters.

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

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

The description states a bundle of 'Keywords + Pain Points + Micro-SaaS' and mentions pricing, but it's vague about what the tool actually does—it doesn't specify a verb or action (e.g., 'generate', 'analyze', 'fetch'). It distinguishes from some siblings like 'keyword_opportunities' or 'pain_points' by being a bundle, but the purpose remains unclear beyond a cost listing.

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

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

No guidance is provided on when to use this tool versus alternatives. The description mentions pricing and a value comparison, but it doesn't explain the context, prerequisites, or when this bundle is preferable over individual tools like 'keyword_opportunities' or 'micro_saas'.

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 the full burden of behavioral disclosure. It mentions the cost ('$2.00/query') and data sources, but lacks critical behavioral details: whether this is a read-only operation, potential rate limits, authentication requirements, data freshness, or error handling. For a paid query tool with extensive data aggregation, this represents significant gaps.

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 appropriately sized and front-loaded, starting with the core purpose ('Comprehensive company OSINT intelligence') followed by specific data sources and profile components. While efficient, the final cost mention could be better integrated, and some redundancy exists in listing both data sources and profile components.

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 (aggregating multiple data sources), lack of annotations, and absence of an output schema, the description is insufficiently complete. It doesn't explain what the returned profile structure looks like, how the 'risk score' is calculated, data latency, or error scenarios—critical gaps for an agent to effectively use this paid intelligence tool.

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

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

Schema description coverage is 100%, with the single parameter 'query' well-documented in the schema. The description adds no additional parameter semantics beyond what the schema already provides ('Company name or domain'), maintaining the baseline score of 3 for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('aggregates', 'builds') and resources (Wikidata, SEC EDGAR, GitHub, etc.), creating a comprehensive company profile. It distinctly differentiates from sibling tools like 'osint_person' (individual-focused) and 'domain_intel' (domain-specific) by emphasizing company-wide intelligence gathering.

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. While it mentions a cost ('$2.00/query'), it doesn't specify scenarios where this comprehensive approach is preferred over more targeted sibling tools like 'dns_lookup', 'whois', or 'web_search', leaving the agent without contextual usage direction.

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

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

No annotations are provided, so the description carries the full burden. It mentions the cost ('$1.00/query'), which is useful behavioral context, but lacks details on rate limits, authentication needs, error handling, or what happens if data sources are unavailable. The description doesn't disclose whether this is a read-only or mutating operation, though 'OSINT intelligence' implies read-only.

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 appropriately sized and front-loaded, starting with the core purpose. However, the second sentence could be more concise, and the cost mention might be better placed elsewhere. Overall, it's efficient with minimal waste.

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

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

Given the complexity of aggregating multiple data sources and no output schema, the description is incomplete. It lists sources but doesn't explain the output structure (e.g., what 'confidence score' means), potential data gaps, or how results are formatted. For a tool with no annotations and rich functionality, this leaves significant gaps for an AI agent.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters (name, email, github). The description adds no additional parameter semantics beyond what's in the schema, such as format examples or interaction effects between parameters. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Comprehensive person OSINT intelligence' that 'aggregates Wikidata/Wikipedia, GitHub, HackerNews, Semantic Scholar, Gravatar, PGP keyservers to build a full person profile.' It specifies the exact resources used and distinguishes itself from sibling tools like 'osint_company' by focusing on person intelligence rather than company intelligence.

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. It mentions the cost ('$1.00/query via x402') but doesn't indicate scenarios where this tool is preferred over other OSINT or search tools like 'web_search' or 'agent_intel.' There's no mention of prerequisites or 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions a cost ('$0.08/query via x402'), which is useful context, but fails to describe other critical traits such as rate limits, authentication needs, output format, or whether it's a read-only or mutating operation. This leaves significant gaps for an AI agent to understand how to interact with it effectively.

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

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

The description is concise and front-loaded, stating the core purpose and cost in a single sentence. There is no wasted text, but it could be slightly improved by structuring usage hints or behavioral details more clearly, though it remains efficient overall.

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 providing NLP-clustered insights and no output schema, the description is incomplete. It lacks details on return values, error handling, or example outputs, which are crucial for an AI agent to use the tool correctly. The cost mention adds some context, but overall, it does not compensate for the missing behavioral and output 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?

The input schema has 100% description coverage, with one parameter ('keyword') documented as 'Filter by keyword.' The description does not add any meaning beyond this, such as examples or constraints on keyword usage. With high schema coverage, the baseline score of 3 is appropriate, as the schema adequately handles parameter semantics without extra description 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 clearly states the tool provides 'NLP-clustered developer pain points with SaaS opportunity suggestions,' specifying both the resource (pain points) and the processing method (NLP-clustered). However, it does not distinguish this from sibling tools like 'keyword_opportunities' or 'opportunity_bundle,' which might offer similar market insights, leaving some ambiguity about its unique 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 provides no guidance on when to use this tool versus alternatives. With many sibling tools related to insights and opportunities (e.g., 'keyword_opportunities,' 'opportunity_bundle'), there is no indication of specific contexts, prerequisites, or exclusions for selecting this tool over others.

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 mentions pricing and AI-categorization, but lacks critical behavioral details: it doesn't specify whether this is a read-only operation, what the output format looks like (e.g., list of products with details), any rate limits beyond cost, or authentication requirements. The description adds minimal context beyond the basic purpose.

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 sentence that combines purpose, key features, and pricing. It's front-loaded with the core functionality. However, the pricing detail might be better placed elsewhere in structured metadata, slightly reducing conciseness.

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

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

Given no annotations and no output schema, the description is incomplete for a tool with behavioral complexity. It misses essential details like output format (e.g., structured data vs. raw text), error handling, or examples of AI categories. The pricing note is useful but doesn't compensate for the lack of operational context.

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

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

Schema description coverage is 100%, with both parameters ('limit' and 'category') clearly documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema, such as example categories or default limits. With high schema coverage, the 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 provides 'Daily Product Hunt launches, AI-categorized,' which specifies the resource (Product Hunt launches) and key features (daily, AI-categorized). However, it doesn't explicitly distinguish from sibling tools like 'github_trending' or 'web_search' that might also surface trending products, though the Product Hunt focus is reasonably distinctive.

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

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

The description includes pricing information ('$0.05/query via x402'), which hints at cost considerations, but provides no guidance on when to use this tool versus alternatives like 'github_trending' for tech trends or 'web_search' for broader product discovery. No explicit when/when-not instructions or named alternatives are given.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits such as the use of residential proxies, cost per query ($0.01), and billing method ('via x402'), which are crucial for an agent to understand usage implications. However, it lacks details on rate limits, error handling, or response formats, leaving some behavioral aspects unclear.

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

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

The description is extremely concise and front-loaded, consisting of a single sentence that efficiently conveys the core functionality, proxy type, and cost. Every word earns its place, with no redundant information, making it easy for an agent to quickly grasp the tool's 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 complexity of a proxy fetching tool with no annotations and no output schema, the description is somewhat incomplete. It covers the basic purpose and cost but lacks details on response behavior, error cases, or output structure, which are important for an agent to use the tool effectively in varied contexts.

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 schema already documents all parameters well. The description adds minimal semantic value beyond the schema, as it does not explain parameter interactions or provide additional context for 'target_url' or other fields. Baseline score of 3 is appropriate since the schema handles most of the parameter 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 states the tool's purpose with a specific verb ('fetch') and resource ('any URL'), and distinguishes it from siblings by specifying 'through residential proxies' and 'raw proxy relay', which differentiates it from tools like 'proxy_session' or 'web_scrape' that might handle sessions or structured scraping.

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

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

The description provides clear context for when to use this tool (e.g., for fetching URLs via residential proxies with cost implications), but does not explicitly state when not to use it or name alternatives among siblings like 'proxy_session' or 'web_scrape', which might be better for other proxy-related tasks.

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

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

With no annotations provided, the description carries the full burden. It effectively discloses key behavioral traits: it's a paid service ($0.50/session), provides residential proxies with sticky IPs, and is designed for multi-request workflows. However, it doesn't mention rate limits, authentication requirements, or what happens when sessions expire.

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

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

The description is extremely concise and front-loaded with essential information in just two sentences. Every word earns its place by communicating purpose, key features, and pricing without any redundancy or unnecessary elaboration.

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

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

For a 2-parameter tool with no annotations and no output schema, the description provides good context about what the tool does and its commercial nature. However, it doesn't explain what the tool returns (session ID, proxy details, etc.) or potential error conditions, leaving some gaps in 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%, providing complete documentation for both parameters. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation without adding extra 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 clearly states the tool's purpose: it creates a dedicated residential proxy session with a sticky IP for multi-request workflows. It specifies the resource (proxy session) and key behavioral trait (sticky IP), but doesn't explicitly differentiate from sibling tools like 'proxy_fetch' or 'proxy_status'.

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 context ('multi-request workflows') and mentions pricing, but doesn't provide explicit guidance on when to use this versus alternatives like 'proxy_fetch' or 'proxy_status'. No when-not-to-use scenarios or prerequisite conditions are specified.

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 but only states it's a health check without detailing behavioral traits like rate limits, authentication needs, response format, or what 'health' entails. It adds minimal context beyond the basic purpose.

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

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

The description is extremely concise and front-loaded, consisting of one efficient sentence that conveys the core purpose and cost. Every word earns its place with no wasted information.

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

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

Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns structured health data. It doesn't explain what 'health' means, what data is returned, or how to interpret results, leaving significant gaps for an AI agent.

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

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

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents inputs. The description doesn't need to add parameter details, and it appropriately avoids redundancy, earning a baseline high score for 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?

The description clearly states the tool's purpose with a specific verb ('Check') and resource ('proxy infrastructure health and available countries'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'proxy_fetch' or 'proxy_session', which prevents a perfect score.

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 'proxy_fetch' or 'proxy_session', nor does it mention any prerequisites or exclusions. The mention of 'Free' hints at cost but doesn't clarify usage context relative to 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions a cost ('$0.02/query via x402'), which adds useful context about pricing. However, it fails to describe other key behaviors such as rate limits, authentication needs, error handling, or what happens if the package doesn't exist. For a tool with no annotations, 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 appropriately sized and front-loaded, stating the core purpose in the first phrase. The additional cost information is relevant but could be more integrated. There is minimal waste, though it could be slightly more structured for 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?

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the purpose and cost, but lacks details on behavioral traits and usage guidelines. Without an output schema, it doesn't explain return values, which is a minor issue given the simple nature. Overall, it's adequate but not fully 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?

The schema description coverage is 100%, with the single parameter 'name' fully documented in the schema as 'PyPI package name (e.g., 'requests', 'fastapi')'. The description does not add any meaning beyond this, such as format constraints or examples. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema does the heavy lifting.

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

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

The description clearly states the tool retrieves 'PyPI package metadata — versions, dependencies, classifiers,' which specifies the verb (retrieves metadata) and resource (PyPI packages). It distinguishes from siblings by focusing on PyPI, unlike other tools for npm, crypto, weather, etc. However, it doesn't explicitly differentiate from similar metadata tools (e.g., 'npm_package'), so it's not a perfect 5.

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. It mentions a cost ('$0.02/query via x402'), which hints at usage context, but doesn't specify scenarios, prerequisites, or comparisons to other package metadata tools (like 'npm_package'). This lack of explicit when/when-not instructions results in a low score.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions real-time analysis and cost per query, which adds some context, but fails to describe critical traits like rate limits, authentication needs, data sources, accuracy, or output format. For a tool with no annotations, this leaves significant gaps in understanding its 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?

The description is extremely concise—two brief sentences that efficiently convey the tool's purpose and cost. Every word earns its place, with no redundancy or unnecessary details. It is front-loaded with the core functionality, making it easy to parse quickly.

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 sentiment analysis, lack of annotations, and no output schema, the description is incomplete. It omits details on behavior, output format, error handling, and comparison to siblings. While concise, it fails to provide enough context for an agent to use the tool effectively without additional assumptions.

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 one parameter ('topic') fully documented in the schema. The description does not add any meaning beyond the schema, such as examples or constraints on the topic. Since the schema handles the parameter documentation adequately, the baseline score of 3 is appropriate, but no extra value is provided.

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

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

The description clearly states the tool performs 'Real-time social sentiment analysis via Grok', specifying the action (analysis), resource (social sentiment), and method (via Grok). It distinguishes from siblings like 'ml_sentiment' by mentioning the specific provider (Grok), though not explicitly contrasting them. The purpose is specific but could be more explicit about differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'ml_sentiment' or other analysis tools in the sibling list. It mentions cost ('$0.02/query via x402'), which implies a usage consideration, but does not specify contexts, prerequisites, or exclusions. This leaves the agent without clear direction for tool selection.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the cost per query, which is valuable context about usage economics, but doesn't describe other important behaviors like rate limits, authentication requirements, error handling, or what the search results look like. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how it operates.

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

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

The description is extremely concise - just one sentence that efficiently communicates the core functionality and cost. Every word earns its place with no wasted text, making it easy to parse quickly.

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 search tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what kind of results to expect, how results are structured, whether there are limitations on query complexity, or how the tool handles different StackExchange sites. The cost information is helpful but doesn't compensate for the missing behavioral and output context.

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

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

The description adds no parameter-specific information beyond what's already in the schema (which has 100% coverage). It doesn't explain how parameters interact, provide examples of valid queries, or clarify the relationship between 'site' and 'tagged' parameters. With complete schema coverage, the baseline score of 3 is appropriate as the schema already documents all parameters adequately.

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

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

The description clearly states the tool searches StackOverflow and other StackExchange sites, providing a specific verb ('Search') and resource ('StackOverflow + 170 StackExchange sites'). However, it doesn't explicitly differentiate from sibling tools like 'web_search' or 'x_search' that might also perform search operations.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions a cost ('$0.02/query via x402'), which could imply cost considerations, but doesn't specify when this search tool is preferred over other search tools in the sibling list or what makes it unique for StackExchange content.

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 for behavioral disclosure. It mentions the cost ('$0.50/query via x402') which is valuable operational context not in the schema. However, it doesn't describe rate limits, authentication needs, error behavior, or what 'risk score' and 'counterparty labels' specifically entail, leaving significant behavioral gaps.

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 appropriately sized with two sentences: one stating the purpose and return values, another providing cost information. It's front-loaded with the core functionality. The cost information could potentially be moved to annotations, but overall it's efficient with minimal waste.

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

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

For a single-parameter tool with no output schema and no annotations, the description provides adequate purpose and cost context but lacks details about return format structure, error handling, or how the various intelligence components (risk score, counterparty labels) are calculated or presented. It's minimally viable but has clear gaps in behavioral transparency.

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 the single parameter 'address' well-documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 for high schema coverage without compensating 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 clearly states the tool performs 'Comprehensive Ethereum wallet OSINT intelligence' and lists specific return types (balance, ENS, tokens, etc.), providing a specific verb+resource combination. However, it doesn't explicitly distinguish from sibling tools like 'eth_balance' or 'eth_transactions' that might offer overlapping 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 provides no guidance on when to use this tool versus alternatives like 'eth_balance' or 'eth_transactions' from the sibling list. It mentions a cost ('$0.50/query') which implies a usage consideration, but offers no explicit when/when-not recommendations or comparison to similar 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?

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: the data source (Open-Meteo), pricing information ($0.02/query), and authentication method (via x402). It doesn't mention rate limits or error handling, but provides substantial operational context.

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

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

The description is extremely concise and front-loaded with all essential information in just two sentences. Every word earns its place, providing purpose, inputs, source, pricing, and authentication without any fluff or 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?

For a weather API tool with no annotations and no output schema, the description does well by covering purpose, inputs, source, pricing, and authentication. It could benefit from mentioning response format or error cases, but given the tool's relative simplicity and good schema coverage, it's mostly 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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'city name (q) or lat/lon' and the auto-geocoding feature, but doesn't provide additional syntax or format details. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with specific verb ('Current weather conditions') and resource ('via Open-Meteo'), and distinguishes it from sibling 'weather_forecast' by specifying it's for current conditions rather than forecasts. It explicitly mentions what it accepts (city name or lat/lon).

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 about when to use this tool (for current weather conditions) and implicitly distinguishes it from 'weather_forecast' by not mentioning forecasts. However, it doesn't explicitly state when NOT to use it or name alternatives beyond the sibling distinction.

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 and does well by disclosing cost ('$0.02/query via x402') and the two input methods (city name or lat/lon). It doesn't mention rate limits, error conditions, or response format, but provides useful operational 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?

Extremely concise and front-loaded: first sentence states core purpose, second adds input methods, third adds cost information. Every sentence earns its place with zero waste.

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

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

For a 5-parameter tool with no annotations and no output schema, the description provides good purpose and cost context but lacks information about response format, error handling, or what the forecast data actually contains. It's adequate but has clear gaps given the complexity.

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

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

Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value beyond the schema - it mentions 'city name (q) or lat/lon' which is already in the schema descriptions. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: '7-day weather forecast via Open-Meteo' with specific verb ('forecast') and resource ('weather'). It distinguishes from sibling 'weather_current' by specifying forecast vs current conditions.

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 about when to use this tool (for 7-day forecasts) and mentions the data source (Open-Meteo). It doesn't explicitly state when not to use it or name alternatives, but the sibling tool 'weather_current' is clearly a different use case.

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 full burden but lacks behavioral details. It mentions cost and data types (prizes, dates, platforms, chains), but does not disclose rate limits, authentication needs, data freshness, or what 'live' entails (e.g., real-time updates).

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 and efficient: the first phrase states the core purpose, followed by key data points and cost. Every sentence adds value with no wasted words, making it easy to scan.

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

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

Given no annotations and no output schema, the description is minimally adequate. It covers purpose and data scope but lacks details on return values, error handling, or operational constraints, leaving gaps for a tool with potential complexity.

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

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

Schema description coverage is 100%, so the schema already documents both parameters. The description implies filtering by chains and platforms but adds no syntax, format, or example details beyond what the schema provides, 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 tool's purpose with specific verbs and resources: 'tracker' indicates it retrieves information, and 'web3 hackathon' specifies the domain. It distinguishes from siblings by focusing on hackathon data rather than prices, trends, or other intel tools.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions cost ('$0.05/query via x402'), but does not specify contexts, prerequisites, or comparisons to sibling tools like 'crypto_trending' or 'opportunity_bundle' for related data.

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

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

No annotations are provided, so the description carries full burden. It mentions 'proxy rotation' (suggesting IP cycling to avoid blocks) and 'content extraction' (hinting at parsing beyond raw HTML), which adds useful behavioral context. However, it lacks details on rate limits, error handling, authentication needs, or what 'extract main content' entails. The pricing disclosure is helpful but incomplete.

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 sentence that front-loads the core functionality. The pricing note is arguably relevant but could be considered slightly extraneous. Overall, it's concise with minimal waste, though it could be more structured (e.g., separating capabilities from cost).

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 annotations and no output schema, the description is incomplete for a tool with 5 parameters and behavioral complexity (proxy rotation, extraction). It lacks details on output format, error cases, performance expectations, or how 'proxy rotation' works in practice. The pricing note adds some context but doesn't compensate for major gaps in operational transparency.

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 schema already documents all parameters thoroughly. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't clarify 'proxy rotation' relates to the 'country' parameter or 'content extraction' to 'extract'). Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose with a specific verb ('scrape') and resource ('any URL'), and mentions additional capabilities ('proxy rotation + content extraction'). It doesn't explicitly differentiate from sibling tools like 'proxy_fetch' or 'web_search', but the core function 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. With sibling tools like 'proxy_fetch' and 'web_search' available, there's no indication of how this tool differs in scope, capabilities, or appropriate use cases. The mention of pricing ('$0.02/query') hints at cost considerations but doesn't define usage boundaries.

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 proxy rotation (a behavioral trait) and cost ('$0.01/query via x402'), which are useful beyond basic functionality. However, it lacks details on rate limits, error handling, or output format, leaving gaps in behavioral context.

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

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

The description is extremely concise and front-loaded, consisting of just two short sentences. Every word earns its place by conveying key information (proxy rotation and cost) without redundancy or fluff, making it highly efficient.

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

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

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is partially complete. It covers behavioral aspects like proxy rotation and cost but lacks details on output format, error handling, or integration context. This leaves some gaps for an agent to use it effectively.

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 schema fully documents the three parameters (q, site, count). The description adds no parameter-specific information beyond what the schema provides. According to guidelines, this results in a baseline score of 3, as the schema handles the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Web search with proxy rotation.' It specifies the verb ('search') and resource ('web'), but does not explicitly differentiate it from sibling tools like 'x_search' or 'web_scrape' beyond mentioning proxy rotation and cost. This makes it clear but not fully sibling-distinctive.

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. It mentions proxy rotation and cost, but does not specify use cases, prerequisites, or exclusions compared to siblings like 'x_search' or 'web_scrape'. This leaves the agent without clear usage direction.

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

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

No annotations are provided, so the description carries the full burden. It discloses a cost ('$0.25/query via x402'), which is useful behavioral context. However, it lacks details on other traits like rate limits, authentication needs, or what 'synthesis' entails (e.g., format, data sources). The description doesn't contradict annotations, but it's incomplete for a tool with no annotation coverage.

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 highly concise and front-loaded: it states the core purpose in the first phrase and adds cost information in a second, efficient clause. There's no wasted text, and every sentence earns its place by providing essential context 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 tool has no parameters, no annotations, and no output schema, the description is moderately complete. It covers the purpose and cost, but for a 'comprehensive synthesis' tool, it lacks details on output format, data sources, or behavioral constraints. This leaves gaps that could hinder an agent's ability to use it effectively, though it's adequate for a simple, parameter-less tool.

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

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

The tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for 0 parameters is 4, as the description doesn't need to compensate for any gaps. It appropriately avoids discussing parameters, focusing on the tool's purpose and cost.

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: 'Comprehensive weekly intelligence synthesis — all feeds combined.' It specifies the verb 'synthesis' and resource 'intelligence' with scope 'weekly' and 'all feeds combined.' However, it doesn't explicitly differentiate from sibling tools like 'agentic_insights_bundle' or 'opportunity_bundle,' which may also provide intelligence or insights, leaving some ambiguity about uniqueness.

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. It mentions a cost ('$0.25/query via x402'), but this doesn't help an agent decide between this and sibling tools like 'agent_intel' or 'osint_company' for intelligence needs. There are no explicit when/when-not statements or named 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates key traits: it's a lookup/query operation (implied by 'lookup'), includes cost information ('$0.02/query via x402'), and specifies the data sources (WHOIS/RDAP). It doesn't mention rate limits, authentication needs, or error handling, but provides useful context beyond basic functionality.

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 sentence that efficiently packs purpose, key data points, and cost information with zero wasted words. It's front-loaded with the core function and appropriately sized for a simple lookup 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?

For a single-parameter lookup tool with no annotations and no output schema, the description is reasonably complete: it states what it does, what data it returns, and includes cost information. However, it doesn't describe the output format or structure (e.g., whether it returns raw WHOIS text or parsed fields), which would be helpful given the lack of output schema.

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

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

The input schema has 100% description coverage, with the 'domain' parameter clearly documented in the schema. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., no format examples beyond the schema's 'e.g., google.com'), so it meets the baseline of 3 for high schema coverage.

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

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

The description clearly states the specific action ('WHOIS/RDAP domain registration lookup') and resource ('domain'), listing key data points returned (registrar, dates, nameservers, status, DNSSEC). It distinguishes from sibling tools like 'dns_lookup' or 'domain_intel' by specifying the registration lookup function.

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

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

The description implies usage context by listing the data points returned, suggesting it's for domain registration information. However, it doesn't explicitly state when to use this tool versus alternatives like 'dns_lookup' (which might provide different DNS data) or 'domain_intel' (which might offer broader intelligence), nor does it mention prerequisites or exclusions.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: real-time/live nature, cost per query, and the data source (X/Twitter via Grok). However, it lacks details on rate limits, authentication needs, error handling, or what the output looks like (e.g., format, pagination). The cost disclosure is valuable 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?

The description is extremely concise—two brief sentences with zero waste. It front-loads the core purpose and includes essential cost information efficiently. Every word earns its place 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 annotations and no output schema, the description is incomplete for a tool with 6 parameters and real-time data access. It covers purpose and cost but misses behavioral details like response format, error cases, or usage constraints. For a search tool with financial implications, more context on outputs and limitations would be helpful.

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 schema fully documents all 6 parameters. The description adds no parameter-specific information beyond implying a search query is involved. It doesn't explain parameter interactions, defaults beyond the schema, or usage examples. Baseline 3 is appropriate as the schema handles the heavy lifting.

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

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

The description clearly states the tool performs 'Real-time X/Twitter search via Grok' with the purpose of providing 'live social intelligence.' It specifies the verb (search), resource (X/Twitter), and method (via Grok), though it doesn't explicitly differentiate from sibling tools like 'web_search' or 'ml_sentiment' that might also involve search or social data.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions a cost ('$0.75/query via x402'), which implies a financial consideration, but doesn't specify contexts where this tool is preferred over other search-related siblings (e.g., 'web_search' or 'ml_sentiment'). No exclusions or prerequisites are stated.

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