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

With no annotations, the description adds value by disclosing the cost ($0.03/call via x402) and technical setup (SearXNG + local LLM). However, it does not explain behavioral aspects such as response time, token limits, or potential errors, which would help the agent make informed decisions.

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 sentences that front-load the purpose and key details (technology and cost). It is efficient, though the cost detail could be secondary; still, no waste.

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

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

For a single-parameter tool with no output schema, the description covers the main purpose and cost. However, it omits what the answer format looks like (e.g., structure of citations) and does not set expectations for response quality, which is important given siblings like 'news_briefing' and 'summarize'.

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

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

The input schema covers 100% of parameters, with 'q' described as 'Question to answer'. The description adds little beyond this, though mentioning 'search answer with citations' implies the question should be fact-seeking. Given high schema coverage, baseline 3 is appropriate.

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

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

The description clearly states the tool provides an 'AI-powered search answer with citations' using SearXNG and a local LLM, specifying the action and resource. However, it does not explicitly differentiate from sibling tools like 'web_search' or 'summarize', which could lead to ambiguity.

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 when a synthesized answer with citations is needed, but lacks explicit guidance on when not to use it or how it compares to alternatives like 'web_search' or 'summarize'. The cost mention is useful but does not address usage conditions.

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

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

Without annotations, the description must fully disclose behaviors. It mentions web research and cost, but does not state whether the tool is read-only, what authentication is required, how results are returned, or any limitations.

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

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

The description is two sentences long, efficient, and front-loaded with purpose and cost. However, it omits details that could be added without significant bloat.

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

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

The tool has one parameter, no output schema, and no annotations, yet the description does not explain output format, response structure, or how the analysis is conducted. With many similar sibling tools, this is insufficient for an agent to choose correctly.

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

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

The schema covers 100% of parameters with a description for 'topic'. The tool description adds no additional semantics beyond 'topic to analyze', so it adds no extra value; 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 phrase 'Deep AI analysis with web research' indicates a general analysis tool, but it does not specify the type of analysis or how it differs from sibling tools like 'deep_research' or 'competitor_analysis'. The verb 'analyze' is broad, and the resource is undefined.

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 a cost of $0.10/call but gives no guidance on when to use this tool versus alternatives. No criteria, prerequisites, or exclusions 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?

Without annotations, the description partially discloses behavior by listing the three components (SEC filings, web research, SWOT analysis). However, it does not specify output format, mutability (read-only), or potential side effects. The price note is useful but not behavioral.

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

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

The description is concise, using one sentence to convey the core functionality and a second sentence for the pricing detail. It is front-loaded with the key purpose. The brevity is efficient, though it omits some useful 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 no output schema, no annotations, and a single parameter, the description lacks completeness. It does not explain the return value (e.g., format, structure) or provide guidance on usage context, leaving significant gaps for an agent to correctly interpret the tool's output.

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

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

Schema coverage is 100% for the single parameter 'company', which is described as 'Company name or ticker'. The description does not add any extra semantics beyond the schema, meeting the baseline for high coverage but offering no additional 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 produces a 'Company intelligence dossier' combining SEC filings, web research, and LLM SWOT analysis. This specific verb and resource combination effectively distinguishes it from sibling tools like sec_filing_analyzer (filings only) and competitor_analysis (specific to competitors).

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 the price ($0.10/call) but provides no guidance on when to use this tool versus alternatives like sec_filing_analyzer, competitor_analysis, or deep_research. It lacks any context about prerequisites, exclusions, or recommended scenarios.

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

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

With no annotations, the description must disclose behavioral traits. It mentions pricing and LLM usage but does not specify whether the tool is read-only, whether it has side effects, or what data sources it accesses. This lacks sufficient behavioral context for an agent.

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

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

The description is short with two sentences, efficiently conveying purpose and pricing. The pricing detail is somewhat extraneous but does not significantly detract from 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?

The description lacks information about return value format, example usage, or how the comparison matrix is structured. Given no output schema, the description leaves the agent with incomplete understanding of what the tool produces.

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

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

The schema already describes the 'company' parameter as 'Company name' with 100% coverage. The description does not add extra meaning such as expected format or examples, so it provides minimal enhancement beyond the schema.

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

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

The description states it performs competitor analysis and produces a comparison matrix using an LLM, which is a specific verb and resource. It gives a clear idea of the tool's output, though it doesn't explicitly differentiate from sibling tools like 'company_intel' or 'analyze'.

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, nor any prerequisites or context for invocation. The description omits any when-to-use or when-not-to-use information.

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 includes pricing and payment method (x402 on Base mainnet), adding some behavioral context. However, it does not mention rate limits, read-only nature, or any other operational traits, so transparency is moderate.

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 brief and front-loaded with the purpose. Every sentence is relevant, but it omits some potentially useful details, earning a high conciseness score.

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 lookup tool with one optional parameter and no output schema, the description is fairly complete. It covers the core functionality and pricing. However, it does not describe the return format or limitations.

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% (the parameter 'id' is fully documented in the schema). The description adds no additional semantic meaning beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description clearly states the tool fetches a live crypto price. However, it does not distinguish from a sibling like 'crypto_research' which may also provide price data, so clarity is strong but not fully differentiated.

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 such as 'stock_quote' or 'crypto_research'. The description offers no 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?

Description mentions a cost model ($0.05/call via x402 on Base mainnet), which is useful but does not disclose other behaviors such as data sources, rate limits, or whether the tool is read-only. No annotations exist to compensate.

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

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

Description is concise: one sentence stating purpose plus a separate pricing note. Core functionality is front-loaded, though the pricing note could be more integrated.

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

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

No output schema exists, yet the description does not describe the output format or structure. With sibling tools having overlapping names, the tool's scope remains unclear.

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

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

Input schema covers all parameters (100% coverage) with a clear description for the single optional 'coin' parameter. The description adds no additional meaning beyond the schema.

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

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

Description clearly states it provides a crypto research brief combining price, on-chain, news, and LLM analysis. However, it does not explicitly distinguish itself from sibling tools like 'crypto_price' or 'news_briefing', leaving ambiguity about whether it is a superset or a separate 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?

No guidance on when to use this tool versus alternatives like 'crypto_price' or 'deep_research'. Description also lacks any 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 provided, so description carries full burden. It discloses price and that the report has sources, but not execution time, required credentials, data sources, or response format. Minimal 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?

Very concise: one sentence for purpose and one for price. No wasted words. However, price is secondary information that could be in annotations; still 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?

Minimal description for a tool with no annotations or output schema. Fails to explain nature of report, differentiation from siblings, or any usage constraints beyond price.

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

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

Schema coverage is 100% (only one parameter, topic, with description). Description adds no extra meaning beyond the schema, but baseline 3 is appropriate when schema is sufficient.

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 'Deep research report with sources' which clearly implies the tool produces a research report. However, it lacks an explicit verb (e.g., 'generates') and does not differentiate from sibling research tools like crypto_research or company_intel beyond the broader 'deep' scope.

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

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

No guidance on when to use this tool over alternatives. It only mentions price, not context or exclusions. With many sibling research tools, this omission is significant.

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

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

No annotations provided, so description carries full burden. It mentions pricing but does not disclose behaviors such as error handling, rate limits, or whether the call is destructive. The pricing info is helpful but insufficient for full 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?

Two sentences: one for purpose, one for pricing. Every word earns its place. No redundancy.

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

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

For a simple tool with one parameter and no output schema, the description covers inputs, sources, output type, and pricing. It does not specify the exact return format, but it's sufficient for an AI agent to understand the tool's capability.

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

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

Input schema has one parameter 'domain' with a basic description. The description adds value by specifying the assessment components (WHOIS, SSL, DNS, LLM) and the output score range, going beyond the schema.

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

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

The description clearly states the tool assesses domain trust score using WHOIS, SSL, DNS, and LLM, outputting a 0-100 score. It distinguishes itself from siblings like 'whois' which only performs a WHOIS lookup.

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

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

No explicit guidance on when to use this tool versus alternatives is provided. The purpose is clear, but usage context is implied rather than 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?

Behavioral transparency is limited. While the description mentions cost and payment method ($0.01/call via x402 on Base mainnet), it does not disclose whether the tool requires authentication, what data it returns, or any side effects. Since no annotations are present, the description carries the full burden but falls short.

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 with just two sentences: one for purpose and one for pricing. Every sentence adds value without redundancy.

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

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

The description lacks completeness for a tool with no output schema. It does not explain what the tool returns (e.g., company data, filing information) or any prerequisites. Given that the tool has only one optional parameter, more context on expected output would significantly improve usability.

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

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

Schema coverage is 100% with one optional 'ticker' parameter described as 'Ticker (default: AAPL).' The description does not add any additional meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.

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

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

The description states 'SEC EDGAR company lookup,' which clearly identifies the tool's purpose as looking up companies on EDGAR. The name 'edgar_lookup' aligns precisely. However, it does not differentiate from sibling tools like 'sec_filing_analyzer,' which might serve a related but distinct 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 only includes pricing information without any context about appropriate use cases 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 burden. It explicitly discloses a cost of $0.002/call via x402 (USDC on Base mainnet), which is a key behavioral trait beyond the snapshot itself. No other traits are discussed, but the cost disclosure is valuable.

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

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

The description is two sentences, front-loading the purpose and then adding cost. Every sentence earns its place with no 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 simple tool with no parameters and no output schema, the description is mostly adequate but fails to describe the return format (e.g., numeric value, units). This gap reduces completeness, but given simplicity, a 3 is reasonable.

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?

There are no parameters, so schema coverage is 100%. The description does not need to add parameter meaning. A baseline of 4 is appropriate per rules for zero parameters.

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

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

The description clearly states it provides a 'Live Base gas snapshot', which is a specific verb (snapshot) and resource (Base gas). It distinguishes itself from sibling tools like crypto_price and market_pulse by specifying the blockchain and metric.

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

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

The description implies usage for obtaining live gas price on Base. While no explicit when-to-use or alternatives are given, the tool's simplicity and name make its context clear. A score of 4 reflects adequate guidance for a zero-parameter tool.

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

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

No annotations are provided, so the description must fully disclose behavior. It lists algorithms and pricing but omits important details like output format (e.g., hex string), error handling for invalid algorithms, case sensitivity, or rate limits. This is insufficient for a tool with no other behavioral cues.

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—only three clauses—and front-loads the core action and available algorithms. Every word serves a purpose; there is no 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 simple hashing tool with full schema coverage and no output schema, the description is mostly complete. It covers the core functionality and pricing. A minor gap is the lack of return value specification (e.g., hex string), but this is partially mitigated by the tool's inherent simplicity.

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

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

Schema coverage is 100%, with each parameter having a description. The description adds value by explicitly listing the three valid algorithms (SHA-256, SHA-512, MD5), which the schema only hints at via the default. This helps the agent understand valid inputs beyond the schema's minimal info.

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

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

The description clearly states the tool's action: hash text using specific algorithms (SHA-256, SHA-512, MD5). It is concise and distinctly differentiates from sibling tools which are analysis, search, and data retrieval tools, none of which perform hashing.

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 when hashing text is needed, but offers no explicit guidance on when to use this tool vs alternatives. There are no sibling hashing tools, so the context is clear, but the absence of usage constraints or prerequisites keeps the score at a minimum viable level.

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 exist, so the description bears full burden. It only discloses pricing, but lacks details on handling nested objects, empty arrays, or error 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?

Very short two-sentence description with no extraneous words. Includes relevant pricing info. However, could better separate pricing from 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?

Simple tool with one parameter and no output schema. The description covers the basic conversion and pricing but omits output format details.

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% (parameter 'data' described as 'JSON array to convert'). The description adds no additional meaning beyond that.

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 'Convert JSON array to CSV', using a specific verb and resource that distinguishes it from sibling tools which are mostly about crypto, finance, and research.

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

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

No guidance on when to use this tool versus alternatives. It does not mention any similar tools or conditions for use.

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

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

No annotations are provided, so the description carries the full burden. It discloses the cost and payment method ($0.04/call via x402 on Base mainnet) but does not describe error behavior, rate limits, authentication needs, or data freshness. The tool's behavior beyond contents is opaque.

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

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

Two sentences with no redundancy. First sentence states the tool's content; the second states the cost. Every word 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?

The tool has no output schema, but the description does not explain the output format (e.g., whether it's a summary, table, or JSON). It lists contents but not structure. Missing details on error cases and data sources.

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

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

The schema covers the one parameter (region) with a description, so baseline is 3. The tool description does not add extra meaning (e.g., possible values or format), but it's adequate given full 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 a macro economic dashboard with Treasury yields, CPI, Fed, and LLM commentary. It distinguishes from siblings like treasury_yields (likely only yields) and market_pulse (broader market). However, it lacks a specific verb indicating the action (e.g., 'get', 'retrieve').

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

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

No guidance on when to use this tool versus alternatives. The description does not mention prerequisites, limitations, or when not to use it. The price mention is not 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 provided, so description carries burden. It mentions pricing and payment method but does not disclose rate limits, output format, or whether the LLM analysis is generated on each call.

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?

Very concise one-sentence description that effectively conveys purpose and cost. Could be slightly more structured but is 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?

For a simple tool with one optional parameter and no output schema, the description is fairly complete, covering purpose and cost. However, some information about output format 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 coverage is 100% with a clear description of the single parameter. The tool description adds no additional meaning beyond the schema.

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

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

The description clearly states the tool provides a stock market pulse combining quote, news, analyst sentiment, and LLM. It distinguishes itself from siblings like stock_quote and news_briefing by being a comprehensive overview.

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

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

No guidance on when to use this tool versus alternatives like stock_quote or news_briefing. No context for when it is appropriate or not.

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 includes useful behavioral context such as pricing ($0.05/call) and the use of x402 payment. However, it omits other important traits like return format, authentication requirements, or rate limits.

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 with two sentences: the first front-loads the core functionality, and the second adds critical cost information. No word is wasted.

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

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

Given the tool's low complexity (one parameter, no output schema), the description covers the essential purpose and cost. However, it could be more complete by specifying output format or providing usage context among many sibling tools.

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

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

Schema coverage is 100% with a single parameter 'topic'. The description adds meaning by specifying that the briefing includes multi-source search, LLM summary, and sentiment, which helps the agent understand the tool's output beyond the raw parameter.

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

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

The description clearly states 'AI news briefing' and explains the functionality as 'multi-source search + LLM summary with sentiment'. This specific verb+resource combination distinguishes it from sibling tools like 'summarize' or 'market_pulse'.

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

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

The description does not provide explicit when-to-use or when-not-to-use guidance relative to sibling tools. While the purpose is clear, there is no indication of alternatives or exclusion criteria.

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 only mentions the purpose and pricing, but fails to disclose behavioral traits such as whether it is read-only, destructive, or any required authentication or rate limits.

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 concise, front-loading the purpose in the first sentence. The pricing information is extra but not bloated. It could be more structured but is 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 simplicity of the tool (one parameter, no output schema), the description is minimally adequate but lacks details on output format, 'LLM impact analysis' specifics, and how monitoring works.

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

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

Schema coverage is 100% for the single parameter 'topic'. The description adds no additional meaning beyond the schema's description 'Topic to monitor'.

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

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

The description clearly states it monitors regulatory filings from government sites with LLM impact analysis. It is specific about the verb and resource, but does not explicitly differentiate from the sibling tool 'sec_filing_analyzer'.

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

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

No guidance on when to use this tool versus alternatives. The description lacks any usage context, 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 provided, so the description carries the transparency burden. It discloses the cost ($0.08/call via x402 on Base mainnet) and the LLM-based risk extraction approach, but lacks information on side effects, authentication needs, or whether the operation is 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?

Two concise sentences: first states the purpose, second states the pricing. No unnecessary words, and key information is front-loaded.

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

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

Lacks description of the output format or content (e.g., what the LLM extracts, how results are returned). No mention of limitations (e.g., supported filing types beyond 10-K/10-Q) or error conditions. For a paid tool, more detail 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% (both ticker and filing_type have descriptions). The description adds no additional meaning beyond the schema; it mentions '10-K/10-Q' but does not elaborate on 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?

Description clearly states it performs deep SEC filing analysis for 10-K/10-Q with LLM risk extraction. This distinguishes it from sibling tools like edgar_lookup (likely raw filing retrieval) and company_intel (broader company info), though no explicit differentiation is made.

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

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

No guidance on when to use this tool versus alternatives such as edgar_lookup or company_intel. The description only mentions the cost, not the appropriate context 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, the description must carry the full burden of behavioral disclosure. It mentions a price and payment method, which is useful, but does not state that the tool is read-only or idempotent, nor does it describe any side effects, rate limits, or authentication needs. The agent is left uncertain about safety.

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 concise sentence that front-loads the purpose. Every word earns its place, though the pricing detail, while useful, is secondary to the function. It is not overly verbose but could include more context without being long.

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

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

Given the tool's simplicity (one parameter, no output schema), the description is minimally adequate but incomplete. It does not explain what the output looks like (e.g., sentiment scores, labels), which platforms are covered, or how to interpret results. The agent lacks enough information to reliably use the tool.

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

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

The input schema has 100% coverage: the single 'topic' parameter is described as 'Topic to analyze'. The description adds no additional meaning beyond the schema. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool performs cross-platform social sentiment analysis using an LLM. The verb 'analysis' is implied, and 'social sentiment' distinguishes it from many sibling tools like stock_quote or news_briefing. However, it does not explicitly differentiate from similar tools like crypto_research or market_pulse, so it's not a 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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, when it is appropriate, or when to avoid it. The description only states what it does and the price, offering no decision support for the agent.

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

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

With no annotations, the description carries full disclosure burden. It mentions a cost per call, which is unusual, but fails to disclose authorization needs, rate limits, or side effects. The pricing info is behavioral 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 short (one sentence) with no wasted words. However, it could be restructured to include standard info like return value before pricing.

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

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

No output schema exists, so the description should explain the return format. It fails to mention what data the quote includes (e.g., price, volume, timestamp). The pricing detail is not standard for completeness.

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

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

Schema coverage is 100%, so the description adds no extra value beyond the schema's parameter description ('Ticker (default: AAPL)'). Baseline score of 3 is appropriate since the schema already documents the parameter.

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

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

The description states 'Live stock quote', which clearly indicates the tool returns a stock price. It distinguishes from sibling tools like 'crypto_price' for crypto assets. However, it doesn't specify what the quote includes (e.g., price, change) or that it's a read operation.

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

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

No guidance on when to use this tool versus alternatives. The description mentions pricing but does not specify when to prefer this over tools like 'market_pulse' or 'news_briefing' for broader market 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?

With no annotations, the description carries full burden. It mentions AI-powered and local LLM but fails to disclose behaviors like whether the operation is read-only, any side effects, or response format. The pricing detail is useful but insufficient for full 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 and front-loaded with the core purpose. The pricing detail adds relevant context without excessive verbosity, earning a high conciseness score.

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

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

The tool has no output schema and no annotations, so the description should clarify return format, length limits, or quality guarantees. It fails to do so, making it incomplete for an agent to fully understand 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?

Schema description coverage is 100% for the single parameter 'text', which already describes its role. The description adds no new semantics beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states the tool's function: 'AI-powered text summary via local LLM.' This is specific and distinguishes it from sibling tools like 'analyze' or 'deep_research' which have broader purposes.

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 such as 'ai_answer' or 'analyze'. It only mentions pricing, which is relevant but not usage 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 exist, so description is the sole source. It discloses the pricing model and payment method, but does not discuss other behaviors like idempotency, rate limits, or whether it is 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 concise with one sentence plus pricing. Every word is functional and front-loaded.

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

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

Given the simplicity of the tool (two string inputs), the description is mostly adequate but lacks details on output format (e.g., plain diff output or detailed report), which may be needed for proper use.

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

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

Schema has 100% description coverage with simple descriptions ('First text', 'Second text'). The description adds no additional meaning beyond the schema, meeting the baseline.

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

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

The description specifies the tool compares two text strings, which aligns with the tool name 'text_diff'. However, it does not elaborate on the type of comparison (e.g., character-level diff), leaving some ambiguity.

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 usage guidelines provided; no indication of when to use this tool versus other text analysis tools among siblings (e.g., analyze, summarize).

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

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

No annotations provided, so description must carry full burden. It discloses that calls cost $0.03 via x402 (USDC on Base mainnet), which is a key behavioral trait. However, it does not mention data freshness, caching, rate limits, or whether results are static or dynamic.

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

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

Two sentences: one for purpose, one for pricing. Extremely concise with no wasted words. Could be slightly improved by front-loading a verb, but it is efficient.

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

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

Given no output schema, description should explain what the tool returns. 'US Treasury yield curve' is vague—does it return a list of rates, a chart, or structured data? No mention of maturities, return format, or examples. Incomplete for a tool with no parameters and no output schema.

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

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

Input schema has no parameters and is 100% covered by description (since none exist). Description adds no additional parameter semantics beyond the schema, which is acceptable given zero parameters. Baseline of 3 applies.

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

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

Description states 'US Treasury yield curve', which clearly indicates the tool returns yield curve data. However, it lacks a verb like 'Get' or 'Retrieve', and does not specify which yields (e.g., 2yr, 10yr) or whether it's current or historical. It distinguishes from siblings by focusing on treasury yields.

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

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

No guidance on when to use this tool vs. siblings like macro_dashboard or market_pulse. The pricing mentions a cost, but does not explain scenarios where this tool is preferred 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?

With no annotations, the description carries the full burden. It adds price and payment info but fails to disclose other behavioral traits like idempotency, error handling, or output characteristics.

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 with two sentences covering purpose and pricing. It is front-loaded with the primary function, leaving no room for fluff.

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

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

For a simple tool with one parameter and no output schema, the description provides essential details about function and cost. However, it lacks information on output format or error scenarios, leaving some gaps.

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

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

Schema coverage is 100% with the only parameter 'url' described as 'URL to extract'. The description adds no additional meaning or context beyond what the schema provides.

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

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

The verb 'Extract' and resource 'clean text from any URL' clearly state the tool's function. It distinguishes itself from siblings like web_search, which returns search results, and summarize, which condenses text.

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

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

No guidance on when to use this tool versus alternatives. It does not specify prerequisites, such as URL format or accessibility, nor does it mention when not to use it.

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

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

With no annotations, the description carries the full burden. It adds the cost ($0.02/call via x402) which is useful, but does not mention rate limits, result format, pagination, or other behavioral traits.

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

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

Two concise sentences: one for core function, one for pricing. No wasted words, highly scannable.

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 simplicity (one required param, no output schema), the description is mostly complete: it states the action and cost. However, it omits the expected return format, which could help the agent. Still adequate for a straightforward tool.

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

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

Schema coverage is 100%: the single parameter 'q' has a clear description. The tool description adds no further meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states it performs 'Web search via SearXNG', specifying both the resource (web) and the engine (SearXNG). This leaves no ambiguity about the tool's 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 provides no guidance on when to use this tool versus siblings like 'deep_research' or 'news_briefing'. There is no context about scope, depth, or alternatives.

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

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

With no annotations, the description carries the full burden. It only discloses cost and payment method, but lacks information on rate limits, data freshness, or behavior for invalid domains.

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 with one sentence that front-loads the purpose. Every word earns its place, including the price note which is relevant for cost-sensitive agents.

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 one-parameter tool with no output schema, the description is minimally adequate but lacks details on output format or typical use cases.

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% (parameter 'domain' described as 'Domain to lookup'). The description adds no further meaning beyond the schema, so baseline score of 3 is appropriate.

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

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

Clearly states it performs WHOIS/RDAP domain lookup, specifying the verb and resource. However, it does not explicitly differentiate it from sibling tools like domain_trust_score, which may also involve domain information.

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

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

Provides no guidance on when to use this tool versus alternatives. The mention of price and payment method does not help an agent decide when to invoke it.

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