botoi-mcp

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

The annotation readOnlyHint=true already indicates this is a safe read operation. The description adds no additional behavioral context (e.g., error handling for invalid expressions, rate limits, or output format details). It doesn't contradict annotations, but adds minimal value beyond 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 a single, efficient sentence that directly states the tool's function without any unnecessary words. It is front-loaded and wastes no space, 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 low complexity (one parameter, read-only, no output schema), the description is reasonably complete. It clearly states what the tool does, though it could benefit from mentioning output format or error handling. With annotations covering safety, it's adequate but not exhaustive.

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 parameter 'expression' fully documented as a 'Five-field cron expression'. The description doesn't add any meaning beyond this, such as examples or format specifics, so it meets the baseline 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 a specific verb ('convert') and resource ('cron expression'), and it distinguishes itself from all sibling tools by focusing on cron expression interpretation rather than hashing, encoding, validation, or other 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 implies usage when a human-readable description of a cron expression is needed, but it doesn't explicitly state when to use this tool versus alternatives (e.g., manual interpretation or other cron tools) or any prerequisites. The context is clear but lacks explicit 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?

The description adds useful context about the return value ('Returns added, removed, and unchanged lines'), which is not covered by the readOnlyHint annotation. However, it does not disclose other behavioral traits like performance, error handling, or input size limits. No contradiction with annotations exists.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by usage context and return details in subsequent sentences, all without wasted words. Each sentence adds value, making it efficient and well-structured.

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

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

Given the tool's moderate complexity (simple diff operation), rich annotations (readOnlyHint), and full schema coverage, the description is mostly complete. However, without an output schema, it could benefit from more detail on the diff format (e.g., unified diff structure), though the return statement partially compensates.

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

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

With 100% schema description coverage, the input schema already documents both parameters clearly. The description does not add meaning beyond the schema, such as explaining how the diff algorithm works or special handling for empty strings, so it meets the baseline 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 ('Compute a unified diff') and resource ('between two text strings'), distinguishing it from sibling tools like dev_hash or dev_regex_test that perform different operations on text. It goes beyond just restating the name/title by specifying the diff type and what it compares.

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

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

The description provides clear context for when to use the tool ('when comparing file versions, config changes, or code revisions'), giving practical scenarios. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as dev_regex_test for pattern matching instead of diffing.

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 useful context about common use cases (checksums, data integrity, fingerprinting) beyond what the readOnlyHint annotation provides. However, it doesn't mention behavioral details like whether the hash is deterministic, if there are any rate limits, or what format the output takes (hex string, base64, etc.). The annotation indicates it's read-only, which aligns with the description's 'generate' action.

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 with two sentences that each serve a distinct purpose: the first states the core functionality, and the second provides usage context. There's no wasted language 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 hash generation tool with good annotations and schema coverage, the description provides adequate context about purpose and usage. The main gap is the lack of output format information (which isn't covered by an output schema), but given the tool's straightforward nature, the description is 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?

With 100% schema description coverage, the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't explain algorithm differences or text encoding considerations). This meets the baseline 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 ('generate a hash') and resources ('input text'), and lists the supported algorithms (MD5, SHA-1, SHA-256, SHA-512). It distinguishes from sibling tools by focusing on cryptographic hashing rather than encoding, validation, or other text transformations.

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

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

The description provides clear usage context ('use for checksums, data integrity, or fingerprinting'), which helps the agent understand when this tool is appropriate. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools (e.g., security_encrypt for encryption vs. hashing).

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 annotations indicate readOnlyHint=true, suggesting a safe operation, which aligns with the description's focus on creation without implying destructive effects. The description adds context about signing with a secret, but does not disclose additional behavioral traits like rate limits, authentication needs, or error handling. No contradiction with annotations exists.

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 directly states the tool's purpose without unnecessary words. It is front-loaded with the core action ('Create and sign'), making it easy to understand quickly. Every part of the sentence contributes essential 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 tool's moderate complexity (JWT signing with 3 parameters), the description is minimal but covers the basic purpose. However, with no output schema and annotations only indicating read-only status, it lacks details on return values (e.g., token format), error cases, or algorithm specifics. It is adequate but has clear gaps for full contextual understanding.

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 parameters like 'secret' (HMAC key) and 'payload' (JSON to encode). The description mentions 'payload and secret' but adds no extra semantic details beyond the schema. With high coverage, the baseline score of 3 is appropriate as the description does not compensate with additional insights.

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 ('Create and sign') and resource ('a JWT'), with the payload and secret as key components. It distinguishes itself from sibling tools like 'dev_jwt_verify' (which verifies rather than creates tokens) and 'security_encrypt' (which is for general encryption, not JWT-specific signing).

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 generating JWTs with given inputs, but does not explicitly state when to use this tool versus alternatives like 'dev_jwt_verify' or 'security_encrypt'. It provides basic context (creating signed tokens) but lacks guidance on exclusions or specific scenarios where this tool is preferred.

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 annotations indicate readOnlyHint=true, which already tells the agent this is a safe read operation. The description adds useful context about the tool's purpose in debugging authentication tokens, but does not disclose additional behavioral traits like error handling, output format, or performance characteristics. There is no contradiction with annotations.

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

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

The description is extremely concise and front-loaded, consisting of just two sentences that directly state the tool's purpose and usage context. Every word earns its place, with no unnecessary information 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 tool's low complexity (single parameter, read-only operation) and the presence of annotations covering safety, the description is reasonably complete. However, the lack of an output schema means the description could benefit from mentioning what the tool returns (e.g., decoded claims), though this is not critical for a debugging 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% description coverage, with the single parameter 'token' clearly documented. The description does not add any additional meaning beyond what the schema provides, such as token format or validation rules, so it meets the baseline 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 specific action ('verify and decode') and resource ('a JWT'), distinguishing it from sibling tools like dev_jwt_sign (which creates tokens) and other tools that handle different data types. It precisely defines what the tool does without being vague or tautological.

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 context for when to use the tool ('when debugging authentication tokens'), which helps differentiate it from other tools. However, it does not explicitly state when not to use it or name specific alternatives, such as using dev_jwt_sign for token creation instead.

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

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

The annotations indicate 'readOnlyHint': true, which the description does not contradict—it describes a generation action without implying data modification. However, the description adds minimal behavioral context beyond this. It mentions 'configurable length and complexity,' hinting at customization, but does not disclose details like randomness quality, security implications, or output format (e.g., plain text strings). With annotations covering safety, the description provides some value but lacks depth.

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

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

The description is a single, efficient sentence: 'Generate a random password with configurable length and complexity.' It is front-loaded with the core purpose, avoids redundancy, and uses clear language. Every word contributes to understanding the tool's function, making it appropriately 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 (7 parameters, no output schema, and annotations providing read-only safety), the description is minimally adequate. It states what the tool does but lacks details on output (e.g., format, security notes) and usage context. With annotations covering safety, the description meets basic needs but does not fully address the tool's role among siblings or practical application scenarios, leaving 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?

The input schema has 100% description coverage, with each parameter well-documented (e.g., 'length' as 'Password length (4-256)'). The description adds little beyond this, only vaguely referencing 'configurable length and complexity' without elaborating on parameter interactions or defaults. Given the high schema coverage, a baseline score of 3 is appropriate, as the description does not significantly 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: 'Generate a random password with configurable length and complexity.' It specifies the verb ('generate'), resource ('random password'), and scope ('configurable length and complexity'). However, it does not explicitly differentiate from sibling tools like 'security_totp_generate' or 'dev_uuid', which also generate values, leaving room for improvement.

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 does not mention sibling tools like 'security_totp_generate' for time-based one-time passwords or 'dev_uuid' for unique identifiers, nor does it specify contexts where password generation is appropriate (e.g., user account creation vs. API keys). This lack of comparative or contextual advice limits its utility for an AI 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?

The annotations indicate readOnlyHint=true, confirming it's a safe read operation. The description adds minimal behavioral context beyond this, stating it 'returns matches' but not detailing output format (e.g., array of matches, groups) or error handling for invalid patterns. It doesn't contradict annotations, but with annotations covering safety, the description adds only basic value without rich behavioral 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 a single, efficient sentence: 'Test a regex pattern against a string and return matches.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a straightforward tool. Every part of the sentence contributes essential information 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's low complexity (simple regex testing), 100% schema coverage, and readOnlyHint annotation, the description is minimally adequate. However, it lacks output schema, and the description doesn't explain return values or error cases, leaving gaps in contextual understanding. It meets basic needs but could be more complete for agent invocation.

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

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

Schema description coverage is 100%, with all parameters (pattern, testString, flags) well-documented in the schema. The description doesn't add any semantic details beyond what the schema provides (e.g., it doesn't explain regex syntax or flag usage). Given the high schema coverage, the baseline score of 3 is appropriate, 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: 'Test a regex pattern against a string and return matches.' It specifies the verb ('test'), resource ('regex pattern'), and outcome ('return matches'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools (e.g., other text processing tools like dev_diff or text_json_validate), 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 lacks context about prerequisites, such as needing valid regex syntax, or comparisons to similar tools (e.g., text processing siblings). Without any usage instructions, the agent must infer applicability, which is insufficient for effective 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?

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds value by specifying that it performs both parsing AND validation, which goes beyond the annotations. However, it doesn't disclose additional behavioral traits like error handling for invalid input, return format details, or performance 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 perfectly front-loaded with the core purpose in the first clause, followed by usage guidance. Both sentences earn their place by providing essential information without any 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 parsing/validation tool with one parameter, 100% schema coverage, and readOnlyHint annotation, the description provides adequate context. However, without an output schema, it could benefit from mentioning what the parsed components look like in the response. The usage guidance helps compensate for this gap.

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

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

Schema description coverage is 100%, with the single parameter 'version' fully documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline 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 specific action ('parse and validate'), resource ('a semver string'), and output components ('major, minor, patch, pre-release, and build components'). It distinguishes from sibling tools by focusing exclusively on semantic version parsing, unlike other dev tools that handle different operations like hashing, encoding, or timestamp conversion.

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

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

The description explicitly provides when-to-use guidance: 'Use when checking version compatibility or sorting releases.' This gives clear context for application without needing to mention specific alternatives, as the tool's purpose is distinct among siblings.

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

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

The description adds valuable behavioral context beyond the readOnlyHint annotation by specifying that it 'returns both Unix and ISO representations,' which clarifies the output format. It does not contradict the readOnlyHint, and while it doesn't mention rate limits or authentication needs, it provides useful output information.

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

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

The description is front-loaded with the core purpose, followed by usage context and return value, all in three concise sentences with no wasted words. Each sentence adds distinct value.

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

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

For a read-only conversion tool with full schema coverage and no output schema, the description is mostly complete, covering purpose, usage, and return format. However, it could slightly improve by mentioning input format detection or error handling for invalid timestamps.

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

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

With 100% schema description coverage, the schema already documents all parameters thoroughly. The description does not add significant meaning beyond the schema, such as explaining parameter interactions or edge cases, so it meets the baseline 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 ('convert between') and resources ('Unix timestamps and ISO 8601 dates'), distinguishing it from sibling tools that handle different data transformations like encoding, hashing, or validation.

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

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

The description provides clear context for when to use the tool ('normalizing date formats across APIs, logs, or databases'), but does not explicitly state when not to use it or name specific alternatives among 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?

The description adds context beyond annotations by specifying the input ('percent-encoded string') and output ('readable text'), though annotations already declare readOnlyHint=true for safety. It doesn't mention error handling or performance limits, but the added context is valuable for a decoding operation.

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

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

Two sentences with zero waste: the first states the purpose, the second provides usage guidelines. It's front-loaded and efficiently structured, with every sentence earning 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 low complexity, rich annotations (readOnlyHint), and full schema coverage, the description is complete enough. It covers purpose, usage, and context without needing to explain parameters or output values, making it well-rounded for this tool type.

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

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

With 100% schema description coverage, the schema fully documents both parameters. The description doesn't add meaning beyond the schema, such as explaining the difference between decodeURIComponent and decodeURI. Baseline 3 is appropriate as the schema handles 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 specific action ('URL-decode') and resource ('a percent-encoded string'), distinguishing it from sibling tools like 'dev_url_encode' and other text processing tools. It precisely defines the transformation from encoded to readable 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?

It explicitly provides when-to-use guidance with concrete examples: 'parsing query parameters, redirect URIs, or encoded form values.' This helps the agent differentiate from other decoding tools (e.g., 'text_base64_decode') and understand appropriate contexts.

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 annotations provide readOnlyHint=true, indicating it's a safe read operation. The description adds value by explaining the practical use cases (query parameters, form data, safe URLs), which goes beyond the annotation. However, it doesn't mention any rate limits, error conditions, or output format details, leaving some behavioral aspects uncovered.

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 two sentences that directly state the purpose and usage guidelines without any unnecessary information. Every sentence earns its place by providing essential context.

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

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

Given the tool's low complexity (simple encoding function), 100% schema coverage, and readOnlyHint annotation, the description is mostly complete. It covers purpose and usage well. However, without an output schema, it doesn't describe the return value (e.g., the encoded string format), which is a minor gap for a tool that produces 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 description coverage is 100%, so the schema fully documents both parameters (text and component). The description doesn't add any parameter-specific details beyond what's in the schema, such as explaining the difference between encodeURIComponent and encodeURI in practical terms. Baseline 3 is appropriate when the schema handles 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 ('URL-encode') and resource ('a string'), and distinguishes it from its sibling 'dev_url_decode' by focusing on encoding rather than decoding. It specifies escaping special characters, which is 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?

The description explicitly provides usage guidelines by stating 'Use when building query parameters, form data, or safe URLs that contain reserved characters.' This gives clear context for when to apply this tool, differentiating it from other encoding or transformation 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?

The annotation readOnlyHint=true already indicates this is a safe read operation. The description adds context by mentioning UUID versions (v4 or v7) and use cases, but does not disclose behavioral traits like rate limits, output format, or whether it generates random or time-based UUIDs. No contradiction with annotations exists.

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

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

The description is a single, well-structured sentence that efficiently conveys purpose and usage guidelines without unnecessary words. It is front-loaded with the core action and includes only essential information, making it highly concise.

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

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

Given the tool's simplicity (0 parameters, read-only annotation, no output schema), the description is mostly complete. It covers purpose and usage well, but lacks details on behavioral aspects like output format or generation specifics, which could be helpful despite the 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?

With 0 parameters and 100% schema description coverage, the baseline is 4. The description does not need to explain parameters, as the input schema is empty, and it appropriately focuses on the tool's purpose and usage without redundant parameter details.

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

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

The description clearly states the verb ('Generate') and resource ('UUIDs'), specifying versions (v4 or v7). It distinguishes from sibling tools like dev_password_generate or security_totp_generate by focusing on UUID generation rather than other types of identifiers or security tokens.

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 ('when you need unique identifiers for database records, request tracing, or idempotency keys'), offering clear context. However, it does not specify when not to use it or name alternatives among siblings, such as dev_password_generate for different identifier needs.

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

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

Annotations indicate readOnlyHint=true and openWorldHint=true, which the description does not contradict. The description adds valuable behavioral context beyond annotations by specifying the scope ('10 basic accessibility checks') and output format ('score, issues list, and summary'), though it lacks details on rate limits 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 front-loaded and highly concise, consisting of only two sentences that efficiently convey purpose, output, and usage guidelines without any 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 tool's moderate complexity (single parameter, no output schema), the description is mostly complete, covering purpose, output, and usage. However, it lacks details on potential limitations (e.g., what the '10 basic checks' entail) or error cases, which would enhance completeness for an agent.

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

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

Schema description coverage is 100%, with the input schema fully documenting the 'url' parameter and its format requirements. The description does not add any parameter-specific information beyond what the schema provides, so it meets the baseline score of 3 without compensating for 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 clearly states the specific action ('Run 10 basic accessibility checks'), target resource ('on a webpage URL'), and output ('Returns a score, issues list, and summary'). It distinguishes itself from sibling tools by focusing on accessibility auditing rather than other lookup functions like DNS, IP, or email checks.

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

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

The description provides explicit guidance on when to use this tool ('Use when you need a quick accessibility audit'), which gives clear context. However, it does not specify when NOT to use it or mention alternatives (e.g., for more comprehensive audits), preventing a perfect 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?

Annotations already indicate readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe, non-destructive operation with open-world data. The description adds value by specifying the return data (structured components, GPS, confidence score), but does not disclose additional behavioral traits like rate limits, authentication needs, 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 front-loaded with the core purpose in the first sentence, followed by usage guidance. Both sentences are essential—no wasted words—making it highly efficient and well-structured.

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

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

Given the tool's low complexity (1 parameter, 100% schema coverage, read-only operation with annotations), the description is mostly complete. It explains the purpose, output, and usage context. However, without an output schema, it could benefit from more detail on return format (e.g., JSON structure), but this is a minor gap.

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

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

Schema description coverage is 100%, with the single parameter 'address' documented as 'Freeform address to validate'. The description adds minimal semantic context by mentioning 'freeform address' and implying validation/parsing, but does not provide extra details beyond what the schema already covers.

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 ('validate a freeform address'), the resource ('address'), and the output ('structured components, GPS coordinates, and confidence score'). It distinguishes itself from sibling tools like 'lookup_ip' or 'lookup_phone' by focusing on address validation and parsing.

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

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

The description provides clear context for when to use the tool ('when you need to verify or parse an address'), but it does not explicitly mention when not to use it or name specific alternatives among the sibling tools (e.g., 'lookup_domain_availability' for domain-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?

Annotations indicate readOnlyHint=true and openWorldHint=true, which already convey that this is a safe, read-only operation with potentially incomplete data. The description adds value by mentioning the return type ('Returns breach count') and the k-Anonymity method, but does not provide additional behavioral details like rate limits, privacy implications, 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 two sentences that are front-loaded with the core functionality and efficiently include usage guidance. Every word contributes to understanding the tool's purpose and context without 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 simple single parameter, high schema coverage, and annotations covering safety, the description is mostly complete. However, without an output schema, it could benefit from more detail on return values (e.g., what breach count means) or error cases, though the mention of 'Returns breach count' partially addresses this.

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 'password' fully documented in the schema. The description does not add any parameter-specific semantics beyond what the schema provides, such as format requirements or sensitivity warnings, so it meets the baseline 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 ('Check if a password has appeared in known data breaches') and the resource ('password'), using the technical method 'k-Anonymity'. It distinguishes from sibling tools by focusing on breach checking rather than other lookup functions like DNS or email validation.

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 context for when to use the tool ('Use when you need to verify password safety'), which clearly differentiates it from other security tools like encryption or PII detection. However, it does not specify when NOT to use it or name specific alternatives among siblings.

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

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

Annotations already declare readOnlyHint=true (safe operation) and openWorldHint=true (may return partial data). The description adds value by specifying the return fields, which helps set expectations. However, it does not disclose additional behavioral traits like rate limits, authentication needs, or error handling, which would be useful context beyond annotations.

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

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

The description is two concise sentences with zero waste: the first states purpose and returns, the second provides usage guidelines. It is front-loaded with core functionality and efficiently 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 low complexity (1 parameter, no output schema) and rich annotations (readOnlyHint, openWorldHint), the description is mostly complete. It covers purpose, returns, and usage context. A minor gap is lack of output format details (e.g., JSON structure), but annotations help mitigate this, so it's 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?

Schema description coverage is 100%, with the single parameter 'domain' fully documented in the schema. The description adds no additional parameter details beyond implying it's for company lookup, so it meets the baseline score 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 action ('Look up company information'), resource ('by domain name'), and specific return fields ('name, industry, employee count, location, and social profiles'). It distinguishes this from sibling tools like 'lookup_domain_availability' or 'lookup_whois' by focusing on company enrichment rather than domain status or registration details.

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 usage contexts ('Use when enriching leads or researching organizations'), which gives clear guidance on when to invoke this tool. However, it does not specify when NOT to use it or name alternatives among sibling tools (e.g., 'lookup_email' for contact enrichment), which prevents a perfect 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?

Annotations indicate read-only and open-world operations, which the description aligns with by describing a query function. The description adds value by specifying the scope ('DNS configuration or troubleshoot domain resolution') and listing supported record types, providing context beyond the annotations. No contradiction exists.

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 with zero waste: the first states the purpose, and the second provides usage guidelines. It's front-loaded with essential information and efficiently 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, annotations cover safety (read-only), and the schema fully describes inputs, the description is mostly complete. It lacks output details (no output schema), but this is mitigated by the clear purpose. It could be slightly enhanced with example 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%, with clear descriptions for both parameters. The description mentions the record types but doesn't add semantic details beyond what the schema provides (e.g., default behavior or format examples). Baseline 3 is appropriate as the schema adequately documents parameters.

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

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

The description clearly states the action ('Query DNS records') and resource ('for a domain'), specifying the exact record types supported. It distinguishes this tool from sibling DNS-related tools like 'lookup_dns_monitor' by focusing on immediate DNS configuration checking rather than monitoring.

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

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

The description explicitly states when to use this tool: 'Use when you need to check DNS configuration or troubleshoot domain resolution.' This provides clear context for its application, helping the agent choose it over alternatives like 'lookup_domain_availability' or 'lookup_whois' for DNS-specific 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?

Annotations indicate read-only and open-world hints, which the description aligns with by describing a checking/comparison function. The description adds value by mentioning comparison with previous snapshots and change detection, which are behavioral traits not covered by annotations. However, it lacks details on rate limits, authentication needs, or what 'previous snapshot' entails (e.g., storage, retention).

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

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

The description is front-loaded with core functionality in the first sentence, uses two concise sentences without waste, and efficiently communicates purpose and usage guidelines in a structured manner.

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 (2 parameters, read-only, no output schema), the description is mostly complete—it covers purpose, usage, and behavioral context like change detection. However, it lacks details on output format (e.g., what the comparison result looks like) and snapshot mechanics, which could be important for an agent to interpret results.

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 full documentation of parameters. The description does not add any parameter-specific semantics beyond what the schema states (e.g., it doesn't clarify default record types or domain format), so it meets the baseline of 3 for high schema coverage without 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 with specific verbs ('Check DNS records', 'compare against previous snapshot', 'Detects record changes over time') and distinguishes it from sibling tools like 'lookup_dns' by emphasizing monitoring and change detection over time rather than just a one-time lookup.

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

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

The description provides explicit guidance on when to use this tool ('Use when monitoring DNS configuration'), which gives clear context. However, it does not specify when not to use it or name alternatives (e.g., 'lookup_dns' for one-time checks), preventing a perfect 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?

The annotations indicate read-only and open-world hints, but the description adds valuable behavioral context: it specifies that the tool returns 'availability status and WHOIS data if registered,' clarifying the output beyond what annotations provide. This enhances transparency about the tool's behavior without contradicting annotations.

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

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

The description is front-loaded with the core purpose, followed by usage guidelines and output details in two concise sentences. Each sentence adds value without redundancy, making it efficient and easy for an agent 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 low complexity (1 parameter, no output schema) and rich annotations, the description is mostly complete. It covers purpose, usage, and output behavior. However, it could slightly improve by mentioning rate limits or error cases, though not critical for this simple 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% description coverage, with the 'domain' parameter well-documented. The description does not add additional meaning beyond the schema, such as format examples or constraints, so it meets the baseline score of 3 where 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 specific action ('Check if a domain name is available for registration') and the resource ('domain name'), distinguishing it from sibling tools like 'lookup_whois' which focuses on WHOIS data without availability checking. It precisely defines the tool's function beyond just restating the name.

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 usage context ('when brainstorming project names or validating domain ideas'), which helps the agent understand when to invoke this tool. However, it does not specify when not to use it or mention alternatives like 'lookup_whois' for detailed WHOIS information, which could improve differentiation.

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

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

Annotations indicate readOnlyHint=true and openWorldHint=true, which already convey that this is a safe, read-only operation with potentially open-ended results. The description adds value by specifying the validation checks performed (syntax, MX record, disposable check), which provides context beyond the annotations. It does not contradict the annotations, as validation aligns with read-only 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 two sentences that are front-loaded with the tool's purpose and followed by usage guidance. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.

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 annotations covering safety and open-world hints, the description provides sufficient context by detailing the validation scope and usage scenario. However, without an output schema, it does not describe the return format (e.g., validation results, error codes), leaving a minor gap 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?

The input schema has 100% description coverage, with the single parameter 'email' documented as 'Email address to validate.' The description does not add further details about the parameter beyond what the schema provides, such as format examples or constraints. Given the 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 specific action ('validate an email address') and enumerates what aspects are validated (syntax, MX record, disposable check), which distinguishes it from sibling tools like lookup_dns or lookup_breach_check that perform different types of lookups. It goes beyond just restating the name/title by detailing the validation scope.

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

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

The description explicitly states 'Use when you need to verify if an email address is real and deliverable,' providing clear guidance on when to invoke this tool versus alternatives. This directly addresses the intended use case without being misleading or vague.

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

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

Annotations already indicate readOnlyHint=true and openWorldHint=true, but the description adds valuable context by specifying what exactly gets fetched (HTTP response headers) and the types of analysis it supports. It doesn't contradict annotations and provides useful behavioral context beyond what annotations offer.

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 with two sentences that each serve distinct purposes: the first states the core functionality, the second provides usage guidance. There's no wasted verbiage and it's front-loaded with the essential 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 that this is a read-only tool with no parameters and no output schema, the description provides excellent context about what the tool does and when to use it. The only minor gap is not explicitly mentioning the format of the returned headers, but this is reasonable given the tool's simplicity and the annotations provided.

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

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

With 0 parameters and 100% schema description coverage, the baseline would be 4. The description appropriately doesn't discuss parameters since there are none, and it focuses on the tool's purpose and usage instead.

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 ('Fetch HTTP response headers') and resource ('for a URL'), distinguishing it from sibling tools like lookup_dns or lookup_url_metadata by focusing on HTTP headers specifically. It avoids tautology by not just repeating the tool name.

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

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

The description explicitly provides three concrete use cases ('inspecting server configuration, security headers, or caching policies'), giving clear guidance on when to use this tool. It effectively distinguishes it from other lookup tools by specifying its domain of application.

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

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

The description adds behavioral context beyond the annotations by specifying what data is returned (geolocation, ISP, AS number), while the annotations (readOnlyHint: true, openWorldHint: true) already indicate it's a safe read operation with open-world data. No contradictions exist, and the description usefully clarifies the scope of information provided.

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

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

The description is front-loaded with the tool's purpose in the first sentence and usage guidance in the second, with no wasted words. Each sentence adds distinct value, making it efficient and easy to parse.

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 optional parameter), high schema coverage, and annotations covering safety, the description is mostly complete. However, it lacks details on output format or potential errors, which could be useful since there's no output schema, leaving a minor gap 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?

The input schema has 100% description coverage, clearly documenting the optional 'ip' parameter and its default behavior. The description does not add further parameter details, so it meets the baseline of 3 by not compensating unnecessarily for the well-documented schema.

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

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

The description clearly states the tool's purpose with specific verbs ('look up') and resources ('geolocation, ISP, and network details for an IP address'), and it distinguishes this from sibling tools by focusing on IP-specific data rather than other lookup types like DNS, email, or SSL.

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 ('when you need to determine the physical location, internet provider, or AS number'), but it does not mention when not to use it or name specific alternatives among the sibling tools, such as lookup_dns for DNS-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?

Annotations already indicate readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe, non-destructive operation with open-world semantics. The description adds useful context about what information is returned (country, carrier, line type, E.164 format), but doesn't disclose behavioral traits like rate limits, authentication needs, or error conditions beyond what annotations provide.

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 with two sentences that each earn their place: the first explains what the tool does, the second explains when to use it. No wasted words, and the most important 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?

For a single-parameter tool with good annotations and clear purpose, the description is reasonably complete. However, without an output schema, the description could benefit from more detail about the return structure or potential error cases, though it does list the types of information returned.

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 'phone' documented as 'Phone number to validate'. The description adds marginal value by implying the tool handles parsing and validation of various phone number formats, but doesn't provide additional syntax or format details beyond what the schema already covers.

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 ('parse and validate') and resource ('a phone number'), and distinguishes it from sibling tools by focusing on phone number processing rather than other lookup types like email, IP, or DNS.

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 usage contexts ('when normalizing user-submitted phone numbers or verifying contact data'), which gives clear guidance on when to use this tool. However, it doesn't specify when NOT to use it or mention alternatives among sibling tools.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating safe, read-only operations with potentially incomplete data. The description adds context about checking 'SSL certificate details and expiry,' which aligns with these annotations but does not provide additional behavioral traits like rate limits, authentication needs, or specific data formats returned.

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 two sentences that directly state the purpose and usage guidelines without unnecessary details, making it efficient and easy to understand.

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) and annotations covering safety and data completeness, the description is mostly complete. It could improve by specifying return value details (e.g., expiry date format) since there's no output schema, but it adequately covers the core functionality.

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

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

Schema description coverage is 100%, with the parameter 'domain' fully documented in the schema. The description adds no extra parameter details beyond what the schema provides, such as domain format examples or validation rules, so it meets the baseline 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 ('check SSL certificate details and expiry') and resource ('for a domain'), and it distinguishes itself from sibling tools like 'lookup_ssl_cert_expiry' by implying broader certificate details beyond just expiry.

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 usage context ('when verifying HTTPS configuration or checking certificate validity'), which clearly indicates when to use this tool. However, it does not specify when not to use it or mention alternatives like 'lookup_ssl_cert_expiry' for focused expiry checks.

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

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

Annotations indicate readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe, read-only operation with open-world assumptions. The description adds value by specifying what the tool returns ('issuer, valid dates, days remaining, and expired/expiring-soon flags'), which goes beyond annotations. However, it does not disclose additional behavioral traits like rate limits, authentication needs, or error handling. No contradiction with annotations is present.

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

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

The description is concise and well-structured: two sentences that efficiently convey the tool's purpose, return values, and usage context without unnecessary details. Every sentence adds value, making it front-loaded and easy to parse.

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), annotations cover safety and world assumptions, and the description specifies return values and usage context. This is mostly complete for a read-only lookup tool. However, without an output schema, the description could benefit from more detail on return format or error cases, but it adequately covers the essentials.

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, clearly documenting the 'domain' parameter. The description does not add any semantic details beyond what the schema provides (e.g., format examples or constraints). With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate but also does not 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: 'Check SSL certificate expiry for a domain.' It specifies the verb ('check') and resource ('SSL certificate expiry'), but does not explicitly differentiate it from sibling tools like 'lookup_ssl' or 'lookup_dns', which might have overlapping functionality. This makes it clear but not fully distinct from alternatives.

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 some guidance: 'Use when monitoring certificate health.' This implies a context (monitoring) but does not specify when to use this tool over similar siblings (e.g., 'lookup_ssl' or 'lookup_dns_monitor') or any exclusions. It offers basic context without explicit alternatives or detailed when-not-to-use advice.

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

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

Annotations indicate readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe, read-only operation with open-world semantics. The description adds value by specifying the use case ('analyzing a competitor's tech stack'), but it does not disclose additional behavioral traits like rate limits, authentication needs, or what specific technologies might be detected. With annotations covering safety, a baseline score is appropriate.

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

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

The description is two sentences, front-loaded with the purpose and followed by usage guidance. Every sentence earns its place by providing essential information without waste, making it appropriately sized 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 complexity is low (one required parameter), annotations cover safety, and there is no output schema, the description is mostly complete. It states the purpose and usage context clearly. However, it could improve by hinting at the return format or potential limitations, but for a simple lookup tool, it's adequate.

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

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

The input schema has 100% description coverage, with the 'url' parameter fully documented in the schema. The description does not add any meaning beyond the schema, such as format examples or constraints not already stated. According to the rules, when schema coverage is high (>80%), the baseline score is 3, 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: 'Detect technologies used by a website (frameworks, CMS, analytics).' It specifies the verb ('detect') and resource ('technologies used by a website'), making it understandable. However, it does not explicitly differentiate from sibling tools like 'lookup_headers' or 'lookup_url_metadata', which might also analyze websites, so it falls short of 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 clear context for when to use the tool: 'Use when analyzing a competitor's tech stack.' This gives a specific scenario, but it does not mention when not to use it or name alternatives among sibling tools, such as 'lookup_headers' for different types of website analysis, preventing a higher 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?

Annotations indicate readOnlyHint=true and openWorldHint=true, showing it's a safe, read-only operation that may fetch external data. The description adds context by specifying what metadata is extracted (title, description, OG tags, favicon), which goes beyond annotations. However, it does not disclose behavioral traits like rate limits, error handling, or network dependencies, leaving some gaps in 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 two sentences, front-loaded with the core purpose and followed by usage guidance. Every sentence earns its place without redundancy, making it efficient and well-structured for quick understanding.

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

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

For a tool with one parameter, rich annotations (readOnlyHint, openWorldHint), and no output schema, the description is fairly complete. It explains what metadata is extracted and when to use it, but could benefit from mentioning potential limitations (e.g., network failures, unsupported URLs) or output format details to enhance completeness for an external data-fetching 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% description coverage, with the 'url' parameter fully documented. The description does not add meaning beyond the schema, as it only mentions the URL implicitly. Given the high schema coverage, the baseline score of 3 is appropriate, as the description provides no extra parameter details.

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: 'Extract title, description, OG tags, and favicon from a URL.' It specifies the verb ('extract') and resources (metadata fields), but does not explicitly differentiate from sibling tools like 'lookup_headers' or 'lookup_dns', which also fetch URL-related data. 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 clear context for usage: 'Use when you need to preview or summarize a webpage.' This gives a specific scenario, but it does not mention when not to use it or name alternatives among siblings, such as 'lookup_headers' for HTTP headers instead of metadata. The guidance is helpful but lacks explicit exclusions or comparisons.

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 annotations already declare readOnlyHint=true and openWorldHint=true, indicating this is a safe read operation with potentially incomplete data. The description adds valuable context by specifying what types of connections it detects (VPN, proxy, Tor exit nodes) and what information it returns (detection type and provider details), which goes beyond what the annotations provide about safety and data completeness.

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 with three tightly focused sentences: one stating the purpose, one providing usage context, and one describing the return value. Every sentence earns its place with no wasted words, and the information is front-loaded appropriately.

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 read-only tool with good annotations, the description provides sufficient context about what the tool does, when to use it, and what it returns. The main gap is the lack of an output schema, but the description partially compensates by mentioning the return format. Given the tool's simplicity and the annotations covering safety aspects, 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?

With 100% schema description coverage, the input schema already fully documents the single 'ip' parameter as an IPv4 address. The description doesn't add any additional parameter semantics beyond what's in the schema, so it meets the baseline expectation without providing 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 specific action ('Check if an IP address is a VPN, proxy, or Tor exit node') and identifies the resource (IP address). It distinguishes itself from sibling tools like 'lookup_ip' or 'lookup_tech_detect' by focusing specifically on VPN/proxy/Tor detection rather than general IP information or technology detection.

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

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

The description provides clear context for when to use the tool ('when assessing connection trustworthiness or flagging suspicious traffic'), which gives practical guidance. However, it doesn't explicitly mention when NOT to use it or name specific alternatives among the sibling tools, though the context makes the primary use case evident.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating a safe, read-only operation with potentially incomplete data. The description adds value by specifying the type of data returned (ownership, registrar, expiration), but does not disclose additional behavioral traits like rate limits, authentication needs, or data freshness constraints.

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 concise sentences with zero waste: the first states the purpose, and the second provides usage context. It is front-loaded with the core function and efficiently communicates essential information 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's low complexity (single parameter, no output schema), annotations covering safety and data completeness, and clear purpose, the description is mostly complete. However, it could slightly improve by hinting at output format or limitations, though not strictly required.

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

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

Schema description coverage is 100%, with the 'domain' parameter fully documented in the schema. The description does not add meaning beyond what the schema provides (e.g., no format examples or validation rules), so it meets the baseline for high schema coverage without extra param details.

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

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

The description clearly states the verb ('Get') and resource ('WHOIS registration data for a domain'), specifying the exact data retrieved. It distinguishes from sibling tools like 'lookup_dns' or 'lookup_domain_availability' by focusing on ownership, registrar, and expiration details rather than DNS records or availability checks.

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

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

The description provides clear context for when to use it ('when you need to find domain ownership, registrar, or expiration date'), but does not explicitly state when not to use it or name specific alternatives among siblings. This gives adequate guidance without full exclusion rules.

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 annotation 'idempotentHint: true' already indicates this is a safe, repeatable operation. The description adds value by specifying the encryption algorithm (AES-256-GCM) and that it uses a passphrase, which are not covered by annotations. However, it does not disclose additional behavioral traits like error handling, performance, or security implications beyond the basic 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 a single, efficient sentence that front-loads the core functionality ('Decrypt AES-256-GCM encrypted text') and includes essential details ('with a passphrase'). There is no wasted verbiage, and it directly communicates the tool's purpose 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 (cryptographic decryption), the description is reasonably complete for a tool with good annotations (idempotentHint) and full schema coverage. It specifies the algorithm and method, which are critical for usage. However, without an output schema, it does not describe return values or potential errors, leaving some gaps in full contextual understanding.

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

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

Schema description coverage is 100%, with clear descriptions for both parameters ('password' and 'ciphertext'). The description adds minimal semantic context by mentioning 'AES-256-GCM' and 'passphrase', which aligns with the parameters but does not provide significant additional meaning beyond what the schema already documents.

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 ('Decrypt'), resource ('AES-256-GCM encrypted text'), and method ('with a passphrase'). It distinguishes from sibling tools like 'security_encrypt' by focusing on decryption rather than encryption, and from other security tools by specifying the cryptographic algorithm and use case.

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 (decrypting AES-256-GCM encrypted text with a passphrase) but does not explicitly state when to use this tool versus alternatives like 'security_encrypt' or other decryption methods. It provides clear functional context but lacks explicit guidance on exclusions or prerequisites.

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

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

The description adds that encryption uses AES-256-GCM with a passphrase, which provides useful context beyond the idempotentHint annotation (which only indicates repeatable operations). However, it lacks details on security implications, error handling, or output format. No contradiction with annotations exists.

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 directly states the tool's purpose and method without any unnecessary words. It is front-loaded and wastes no space, making it easy for an agent 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 moderate complexity (encryption with specific algorithm), lack of output schema, and minimal annotations, the description is somewhat complete but could benefit from details on output format or error cases. It covers the core action but leaves gaps in full behavioral 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?

With 100% schema description coverage, the input schema fully documents the two parameters (plaintext and password). The description does not add any extra semantic details about the parameters, such as format requirements or constraints, so it meets the baseline 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 ('Encrypt text') and the method used ('using AES-256-GCM with a passphrase'), which distinguishes it from sibling tools like security_decrypt or dev_hash. It directly answers what the tool does without being vague or tautological.

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 does not mention sibling tools like security_decrypt for decryption or dev_hash for hashing, nor does it specify contexts or exclusions for its use, leaving the agent without 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?

Annotations provide readOnlyHint=true, indicating this is a safe read operation. The description adds value by specifying the types of PII detected (emails, phones, SSNs), which isn't covered by annotations. However, it lacks details on behavioral traits like accuracy, confidence thresholds, or rate limits. No contradiction with annotations exists.

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 with zero waste. It's front-loaded with the core purpose and includes specific examples (emails, phones, SSNs) that add clarity without verbosity. 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?

Given the tool's moderate complexity (PII detection), lack of output schema, and annotations covering only read-only status, the description is adequate but incomplete. It specifies what PII types are detected but doesn't cover return format, error handling, or performance characteristics. With no output schema, more detail on results 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%, with the single parameter 'text' well-documented in the schema. The description adds minimal semantic context by implying the text is scanned for PII, but doesn't provide additional details like input length limits or format requirements. Baseline 3 is appropriate given 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: 'Detect personally identifiable information (emails, phones, SSNs) in text.' It specifies the verb (detect), resource (PII), and types of PII detected. However, it doesn't explicitly differentiate from sibling tools like 'lookup_email' or 'lookup_phone' which might have 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. It doesn't mention prerequisites, context, or exclusions. For example, it doesn't clarify if this is for bulk text analysis versus single-field validation, or how it differs from sibling tools like 'lookup_email' that might check email validity.

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

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

Annotations provide readOnlyHint=true, indicating it's a safe read operation. The description adds value by specifying what is generated ('TOTP secret and provisioning URI'), which clarifies the output beyond just 'generate.' However, it doesn't disclose behavioral details like rate limits, authentication requirements, or whether the secret is stored or transient, leaving some 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 a single, efficient sentence that front-loads the key action and purpose without unnecessary words. Every part of the sentence ('Generate a TOTP secret and provisioning URI for 2FA setup') contributes directly to understanding the tool's function, 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 (generating TOTP data), annotations cover safety (readOnlyHint), and schema fully documents parameters, the description is mostly complete. It specifies the output ('TOTP secret and provisioning URI'), but since there's no output schema, it could benefit from more detail on return format (e.g., JSON structure). However, it's sufficient for basic understanding.

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

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

Schema description coverage is 100%, with clear descriptions for all parameters (digits, period, secret). The description doesn't add any parameter-specific semantics beyond what the schema provides, such as explaining the format of the provisioning URI or default behaviors. Baseline score of 3 is appropriate as the schema handles 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 specific action ('Generate'), the resource ('TOTP secret and provisioning URI'), and the purpose ('for 2FA setup'). It distinguishes itself from sibling tools like 'security_encrypt' or 'dev_password_generate' by focusing specifically on TOTP generation for authentication setup.

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 ('for 2FA setup'), suggesting it's used when setting up two-factor authentication. However, it doesn't explicitly state when to use this tool versus alternatives (e.g., other security tools like 'security_encrypt'), nor does it mention prerequisites or exclusions. The guidance is present but not comprehensive.

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

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

Annotations indicate readOnlyHint=true, which the description aligns with by implying a validation operation without side effects. The description adds context about performing a Luhn check and network detection, which are specific behavioral traits not covered by annotations. However, it doesn't disclose potential limitations like input format requirements or error handling, leaving some 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—a single, front-loaded sentence that efficiently conveys the core functionality without any fluff. Every word earns its place by specifying the action and methods (Luhn check, network detection), 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 low complexity (one parameter, read-only) and high schema coverage, the description is minimally complete. However, with no output schema, it doesn't explain return values (e.g., validation result, detected network), and it lacks error handling or usage context. This leaves some gaps for an agent to fully understand tool 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%, with the parameter 'number' fully documented in the schema. The description mentions 'credit card number to validate', which restates the schema without adding new semantic details like expected formats (e.g., with or without spaces) or validation scope. Baseline 3 is appropriate as the schema handles 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 with specific verbs ('validate', 'check', 'detect') and resource ('credit card number'), making it easy to understand what it does. However, it doesn't differentiate from sibling tools like 'security_pii_detect' or 'lookup_credit_card' (if they existed), which would require explicit comparison for 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 doesn't mention prerequisites, context (e.g., for payment processing or data validation), or compare it to sibling tools like 'security_pii_detect' for handling sensitive data. This lack of usage context leaves the agent to infer when this tool is appropriate.

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 the readOnlyHint annotation by specifying the return type ('Returns the original plaintext string') and the output encoding ('UTF-8 text'). It doesn't contradict annotations and provides useful behavioral details about the transformation result.

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 with three focused sentences: purpose, usage guidelines, and return value. Every sentence earns its place with zero wasted words, and the information is front-loaded appropriately.

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 transformation tool with readOnlyHint annotation and no output schema, the description provides complete context: purpose, usage scenarios, and return value. It adequately compensates for the lack of output schema by specifying what the tool returns.

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

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

With 100% schema description coverage, the input schema already fully documents both parameters. The description doesn't add additional parameter semantics beyond what's in the schema, so it meets the baseline expectation without providing 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 specific action ('Decode a Base64 string') and resource ('back to UTF-8 text'), distinguishing it from sibling tools like 'text_base64_encode' by specifying the reverse operation. It provides a complete, unambiguous purpose statement.

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

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

The description explicitly states when to use the tool ('Use when extracting data from Base64-encoded API responses, tokens, or email headers'), providing clear context and distinguishing it from alternatives like 'dev_url_decode' or 'security_decrypt' by focusing on Base64-specific 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?

The annotation readOnlyHint=true already indicates this is a safe read operation. The description adds useful context about practical applications (URL/API embedding) but doesn't disclose additional behavioral traits like performance characteristics, error handling, or size limitations. No contradiction with annotations exists.

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 with two sentences that each earn their place: the first states the core functionality, the second provides usage context. No wasted words, and the most important information (what the tool does) 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?

For a simple transformation tool with good annotations and full schema coverage, the description is reasonably complete. It explains the purpose and context well. The main gap is the lack of output schema, so the description doesn't specify the return format (though Base64 encoding implies a string 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?

With 100% schema description coverage, the input schema fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3. It doesn't explain the implications of the urlSafe parameter beyond what the schema states.

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 verb ('Encode') and resource ('a UTF-8 string to Base64'), distinguishing it from sibling tools like 'text_base64_decode' by specifying the encoding direction. It's precise about the input format (UTF-8 string) and output format (Base64).

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 ('for embedding data in URLs or APIs that require Base64'), which helps differentiate it from other encoding tools like 'dev_url_encode'. However, it doesn't explicitly state when not to use it or name specific alternatives beyond the obvious decode sibling.

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

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

Annotations indicate readOnlyHint=true, which the description aligns with by describing a parsing/conversion operation without implying mutation. The description adds value by specifying the output format ('JSON array of objects') and use cases, but does not disclose additional behavioral traits like error handling, performance limits, or input size constraints beyond what annotations provide.

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 with zero waste: the first states the core functionality, and the second provides usage context. It is front-loaded with the main purpose and appropriately sized for a straightforward tool.

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

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

Given the tool's low complexity (data conversion), rich schema coverage (100%), and annotations (readOnlyHint), the description is mostly complete. It lacks an output schema, but the description specifies the output format ('JSON array of objects'). A minor gap is the absence of details on error cases or output structure variations.

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 parameters are well-documented in the schema itself. The description does not add meaning beyond the schema, as it does not explain parameter interactions, default behaviors, or examples. The baseline score of 3 reflects adequate parameter understanding from the schema alone.

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

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

The description clearly states the tool's purpose with specific verbs ('parse', 'convert') and resources ('CSV text', 'JSON array of objects'). It distinguishes this tool from siblings by focusing on CSV-to-JSON conversion, unlike other text tools that handle different formats (e.g., XML, YAML, HTML).

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 ('when converting spreadsheet or tabular data into structured JSON for processing or storage'). However, it does not explicitly mention when not to use it or name alternatives among sibling tools (e.g., text_xml_to_json or text_yaml_to_json for other formats).

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

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

Annotations indicate readOnlyHint=true, confirming this is a safe read operation. The description adds value by specifying 'clean Markdown' as the output, implying formatting or sanitization, and mentions use cases like web content extraction. However, it doesn't detail behavioral traits like error handling, performance limits, or output specifics beyond what annotations cover.

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 two sentences that directly state the tool's purpose and usage guidelines without unnecessary details. Every sentence contributes essential information, making it efficient and well-structured.

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

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

Given the tool's low complexity (one parameter, read-only operation) and high schema coverage, the description is mostly complete. It covers purpose and usage well but lacks details on output format or potential limitations, which could be helpful since there's no output schema. However, this is a minor gap for such a simple 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 'html' fully documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides, such as HTML format requirements or examples. This meets the baseline 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 verb 'Convert' and resource 'HTML to clean Markdown', making the purpose explicit. It distinguishes this tool from its sibling 'text_markdown_to_html' by specifying the opposite conversion direction, avoiding redundancy.

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

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

The description explicitly provides usage scenarios: 'when extracting readable content from web pages or migrating HTML docs to Markdown format.' This gives clear context for when to use the tool, though it doesn't mention alternatives or exclusions, which is acceptable given the tool's straightforward purpose.

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

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

Annotations already declare readOnlyHint=true, indicating a safe operation. The description adds useful behavioral context: it's for formatting/pretty-printing JSON, which implies no data modification beyond formatting. No contradictions with annotations.

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

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

Two sentences, front-loaded with core purpose, zero waste. First sentence states what it does, second provides usage guidance—each sentence earns its place efficiently.

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

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

For a simple formatting tool with read-only annotations and full schema coverage, the description is largely complete. It could benefit from mentioning output format (e.g., returns formatted JSON string) since there's no output schema, but the purpose and usage are well-covered.

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 parameters are fully documented in the schema. The description mentions 'configurable indentation' which aligns with the indent parameter but doesn't add significant semantic value 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 description clearly states the tool's purpose with specific verbs ('format and pretty-print') and resource ('JSON string'), plus configurable indentation. It distinguishes from siblings like text_json_validate (validation) and text_json_to_yaml (conversion).

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

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

Explicitly states when to use: 'when making minified or compact JSON readable for debugging or documentation.' This provides clear context and distinguishes from alternatives like text_json_validate for validation or text_json_to_yaml for conversion.

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

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

Annotations already declare readOnlyHint=true, indicating a safe read-only operation. The description adds useful context about the tool's purpose (conversion for human-readable configs) but does not disclose additional behavioral traits like error handling, performance characteristics, or limitations (e.g., handling of complex JSON structures). No contradiction with annotations exists.

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 with zero waste: the first states the core function, and the second provides usage context. It is front-loaded with the main purpose and appropriately sized 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's moderate complexity (data transformation), rich annotations (readOnlyHint), and full schema coverage, the description is mostly complete. However, without an output schema, it could benefit from mentioning the return type (YAML string) or potential errors, though the context is sufficient for basic 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 description coverage is 100%, so the schema fully documents both parameters ('data' as JSON object to convert, 'indent' as indentation spaces with default). The description does not add meaning beyond this, such as explaining parameter interactions or edge cases, but the high schema coverage justifies the baseline score.

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 ('Convert JSON data to YAML format') and resource ('JSON data structures'), distinguishing it from sibling tools like 'text_yaml_to_json' (reverse conversion) and 'text_json_format' (formatting without conversion). It explicitly identifies the transformation purpose.

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

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

The description provides clear context for when to use this tool ('when generating human-readable config files from JSON data structures'), giving a practical application scenario. However, it does not explicitly mention when NOT to use it or name specific alternatives among siblings (e.g., 'text_json_format' for formatting without conversion).

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

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

The description adds behavioral context beyond the readOnlyHint annotation by specifying that it 'report[s] parsing errors,' which clarifies the tool's response behavior. However, it doesn't detail aspects like error format, performance, or rate limits, leaving some transparency 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 a single, efficient sentence that front-loads the core purpose ('validate whether a string is valid JSON') and adds essential outcome detail ('report parsing errors'). There is no wasted verbiage, 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 low complexity (one parameter, no output schema) and the readOnlyHint annotation, the description is mostly complete for a validation tool. However, it could benefit from mentioning the output format (e.g., success/failure with error details) to fully compensate for 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?

With 100% schema description coverage, the input schema fully documents the single parameter 'json' as a 'JSON string to validate.' The description adds no additional semantic details beyond this, so it meets the baseline 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 specific action ('validate') and resource ('JSON string') with the outcome ('report parsing errors'). It distinguishes itself from sibling tools like text_json_format or text_json_to_yaml by focusing on validation rather than transformation or conversion.

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 checking JSON validity, but provides no explicit guidance on when to use this tool versus alternatives (e.g., text_json_format for formatting, text_json_to_yaml for conversion) or any prerequisites. It lacks context about when-not scenarios or comparisons with 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?

The description adds value beyond the readOnlyHint annotation by specifying that the output is 'sanitized HTML' (though the schema already documents the sanitize parameter). It does not disclose additional behavioral traits like performance characteristics, error handling, or specific sanitization details beyond what the schema provides. No contradiction with annotations exists.

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 only two sentences, both of which earn their place by stating the tool's purpose and usage context. It is front-loaded with the core functionality and wastes no words.

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

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

Given the tool's straightforward transformation purpose, the presence of a readOnlyHint annotation, and a fully described input schema, the description is reasonably complete. However, the lack of an output schema means the description could benefit from more detail about the return format (e.g., structure of the HTML output), though it does mention the return type.

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

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

With 100% schema description coverage, the schema fully documents all three parameters (markdown, gfm, sanitize). The description mentions 'sanitized HTML' which aligns with the sanitize parameter but does not add meaningful semantic context beyond what the schema already provides. The baseline score of 3 is appropriate given the comprehensive schema.

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

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

The description clearly states the tool's purpose with a specific verb ('Convert') and resource ('Markdown text to HTML'), distinguishing it from sibling tools like 'text_html_to_markdown' which performs the inverse operation. It explicitly names the transformation being performed.

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

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

The description provides clear context for when to use the tool ('when rendering Markdown content for web display or email templates'), but does not explicitly mention when not to use it or name specific alternatives. It implies usage scenarios without detailing exclusions.

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

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

Annotations indicate readOnlyHint=true, which the description aligns with by implying a conversion process without mutation. The description adds context about use cases (API responses, feeds, config files) and the benefit ('easier processing'), but does not disclose additional behavioral traits like error handling, performance limits, or output structure beyond what annotations provide.

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 concise, consisting of two sentences that efficiently convey purpose and usage without waste. Every sentence adds value, making it easy to understand 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 low complexity (1 parameter, no output schema, simple conversion task) and annotations covering safety (readOnlyHint=true), the description is mostly complete. It explains what the tool does and when to use it, but lacks details on output format or error handling, which could be helpful despite the annotations.

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

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

The input schema has 100% description coverage, with the 'xml' parameter fully documented. The description does not add meaning beyond the schema, as it only implies the parameter is an XML string without extra details. Baseline score of 3 is appropriate since 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 with a specific verb ('Convert') and resource ('XML documents to JSON'), distinguishing it from sibling tools like 'text_csv_to_json' or 'text_yaml_to_json' by focusing on XML conversion. It explicitly mentions the transformation from XML to JSON format.

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

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

The description provides clear context for when to use the tool ('parsing XML API responses, feeds, or config files into a structured JSON format for easier processing'), but it does not explicitly state when not to use it or name alternatives among siblings (e.g., 'text_csv_to_json' for CSV files). This gives good guidance but lacks exclusion details.

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 annotation readOnlyHint=true already indicates this is a safe read operation. The description adds useful context about the tool's use cases (parsing config files, API consumption) and specifies the return type ('Returns a JSON object'), but does not disclose additional behavioral traits like error handling, performance limits, or input constraints beyond the 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 front-loaded with the core purpose, followed by usage guidance and return value, all in three concise sentences with zero wasted words. Each sentence adds distinct value without redundancy.

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

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

Given the tool's low complexity (single parameter, no output schema), the description is reasonably complete: it covers purpose, usage context, and return type. However, it lacks details on potential errors (e.g., invalid YAML handling) or performance considerations, which could enhance completeness for a data conversion 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%, so the schema fully documents the single 'yaml' parameter. The description does not add any parameter-specific details beyond what the schema provides, such as YAML syntax requirements or examples, which aligns with 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 with a specific verb ('Convert') and resource ('YAML configuration or data to JSON'), and distinguishes it from siblings like text_json_to_yaml and text_csv_to_json by focusing on YAML-to-JSON conversion specifically.

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

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

The description provides clear context for when to use the tool ('when parsing YAML config files for programmatic access or API consumption'), but does not explicitly mention when not to use it or name specific alternatives among siblings like text_json_to_yaml for reverse conversion.

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 some behavioral context beyond the readOnlyHint annotation. It mentions 'language-aware indentation and style rules' and lists supported languages (JS, TS, Python, Go, Rust, and more), which provides insight into how the tool behaves. However, it doesn't disclose details like formatting rules, error handling, or output format. The annotation (readOnlyHint: true) already indicates it's a safe read operation, so the description adds moderate value without contradictions.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by supporting details (supported languages and usage guidelines) in the second. Both sentences are concise and directly relevant, with no wasted words or redundant information. It efficiently communicates essential information in a structured manner.

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 (formatting code with language-specific rules), the description is reasonably complete. It covers purpose, supported languages, and usage scenarios. The readOnlyHint annotation handles safety, and the schema fully documents parameters. However, without an output schema, the description doesn't explain what the formatted code looks like (e.g., indentation style), leaving a minor gap 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?

The input schema has 100% description coverage, with clear documentation for both parameters ('code' and 'language'). The description doesn't add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain format details or constraints). With high schema coverage, the baseline score of 3 is appropriate, 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: 'Format source code with language-aware indentation and style rules.' It specifies the verb ('format') and resource ('source code'), and mentions supported languages. However, it doesn't explicitly differentiate from sibling tools like 'text_json_format' or 'transform_sql_format', which perform similar formatting operations on different data types.

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

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

The description provides clear usage context: 'Use when standardizing code style or preparing snippets for documentation.' This gives practical scenarios for when to invoke the tool. It doesn't explicitly state when NOT to use it or name alternatives among sibling tools (e.g., 'text_json_format' for JSON), but the guidance is sufficient for an agent to understand appropriate contexts.

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 annotations already declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds useful context about the tool's purpose (generating interfaces for type safety), but doesn't disclose additional behavioral traits like error handling, performance characteristics, or limitations of the conversion process. With annotations covering safety, this earns a baseline score.

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 with just two sentences that each serve a distinct purpose: the first states what the tool does, and the second provides usage guidance. There's no wasted language 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 transformation tool with read-only annotations and no output schema, the description provides adequate context about the tool's purpose and use cases. However, it doesn't mention what the output looks like (interface format, naming conventions, etc.), which would be helpful given the lack of output schema. The description is complete enough for basic understanding but could be more 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?

With 100% schema description coverage, the input schema already fully documents both parameters (json and name). The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation without providing 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 specific action ('Generate TypeScript interfaces') and the resource ('from a JSON sample'), distinguishing it from sibling tools like text_json_format or text_json_validate. It provides a concrete verb+resource combination that 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 includes explicit usage context ('Use when building type-safe API clients or converting API responses into TypeScript types'), which gives clear guidance on when to apply this tool. However, it doesn't specify when NOT to use it or name alternative tools for similar tasks, preventing a perfect 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?

The annotations already indicate readOnlyHint=true, confirming this is a safe read operation. The description adds useful context about what the minification process entails (removing whitespace, comments, redundant rules), but does not disclose additional behavioral traits like performance characteristics, error handling, or output format 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 front-loaded with the core purpose in the first sentence and includes a concise usage guideline in the second sentence, with 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?

Given the tool's low complexity (single parameter, read-only operation) and high schema coverage, the description is mostly complete. However, the lack of an output schema means the description could benefit from mentioning what the tool returns (e.g., minified CSS string), though this is a minor gap.

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

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

The input schema has 100% description coverage, with the 'code' parameter fully documented. The description does not add any parameter-specific semantics beyond what the schema provides, so it 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 specific action ('Minify CSS stylesheets') and the resources affected ('by removing whitespace, comments, and redundant rules'), distinguishing it from sibling tools like 'transform_minify_js' (for JavaScript) and 'transform_code_format' (for formatting rather than minification).

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

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

It provides clear context for when to use the tool ('Use when reducing stylesheet size for faster page loads'), but does not explicitly mention when not to use it or name alternatives (e.g., 'transform_code_format' for formatting instead of minification).

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

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

Annotations indicate readOnlyHint=true, so the agent knows it's a safe read operation. The description adds useful context about the minification process (what gets removed/shortened), but does not disclose behavioral traits like performance, error handling, or output format beyond what annotations provide.

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

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

The description is front-loaded with the purpose in the first sentence and usage in the second, with no wasted words. Every sentence earns its place by providing essential information efficiently.

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

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

Given the tool's low complexity, one required parameter with full schema coverage, and annotations covering safety, the description is mostly complete. However, without an output schema, it could benefit from mentioning the return type (e.g., minified string) for better completeness.

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

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

Schema description coverage is 100%, with the parameter 'code' fully documented in the schema. The description does not add meaning beyond the schema, such as examples or constraints, so it meets the baseline of 3.

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

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

The description clearly states the specific action ('Minify JavaScript code') and what it does ('removing whitespace, comments, and shortening variable names'), distinguishing it from sibling tools like 'transform_minify_css' by specifying the language (JavaScript).

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

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

It provides clear context for when to use the tool ('when optimizing bundle size for production deployment'), but does not explicitly mention when not to use it or name alternatives like 'transform_code_format' for formatting instead of minifying.

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

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

Annotations provide readOnlyHint=true, indicating it's a safe read operation. The description adds context about beautifying SQL, which aligns with annotations but doesn't disclose additional behavioral traits like rate limits, error handling, or output format. It doesn't contradict annotations, so a baseline score is appropriate.

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

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

Two concise sentences: the first states the purpose, and the second provides usage guidelines. No wasted words, and it's front-loaded with key information, making it efficient and easy to parse.

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 (formatting only), annotations cover safety, and schema fully describes inputs, the description is mostly complete. However, without an output schema, it could hint at the return format (e.g., formatted string), but the usage guidelines partially compensate. Slightly incomplete 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 the 'sql' and 'indent' parameters. The description implies formatting but doesn't add meaning beyond the schema (e.g., explaining SQL syntax support). Baseline 3 is correct as the schema handles parameter details.

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 ('format and beautify') and resource ('SQL queries'), plus details on what formatting entails ('proper indentation and keyword casing'). It distinguishes from sibling tools like 'transform_code_format' by specifying SQL queries.

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

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

Explicitly states when to use the tool: 'when cleaning up inline SQL for code reviews, documentation, or debugging.' This provides clear context and use cases, though it doesn't mention alternatives like 'text_json_format' for other formats, which is fine given the specificity.

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