botoi-mcp

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

Annotations provide readOnlyHint=true, which covers the safety profile. The description adds no behavioral details beyond the core purpose, such as handling of invalid expressions or output format specifics. It does not contradict annotations, but it also offers no extra context.

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

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

The description is a single, front-loaded sentence with zero unnecessary words. It immediately communicates function 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?

For a tool with one simple parameter and no output schema, the description fully conveys the input and the nature of the output ('human-readable description'). It is complete for the tool's complexity.

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

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

Input schema coverage is 100%, with a clear description 'Five-field cron expression' for the only parameter. The tool description adds no additional parameter semantics beyond restating that it accepts a cron expression. Baseline 3 is appropriate since schema already carries the load.

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

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

The description clearly states the tool's function: 'Convert a cron expression to a human-readable description.' It uses a specific verb ('Convert') and resource ('cron expression'), and the result is clear. It distinguishes from sibling tools like dev_timestamp_convert and dev_regex_test by focusing on cron expressions.

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?

Usage is implied: the tool should be used when one has a cron expression and needs a human-readable explanation. However, there is no explicit statement of when to use it vs. alternatives, nor any exclusions. The description is minimal and relies on the tool's name and obvious 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?

The annotations declare readOnlyHint=true, covering safety. The description adds that it returns added, removed, and unchanged lines, which is basic output behavior. It does not disclose any potential limitations (e.g., large input handling, format details) or other behavioral nuances, but given annotations cover the safety profile, a score of 3 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 only two sentences, with the action verb first, followed by use cases and output summary. Every sentence provides necessary information without redundancy, making it highly efficient and front-loaded.

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

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

For a simple tool with two required string parameters and no output schema, the description is complete: it explains the input, the operation, and the nature of the return value. The low complexity and rich annotations mean minimal additional context is needed.

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

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

Schema description coverage is 100%, and each parameter (original, modified) is briefly described. The description does not add additional semantic details beyond the schema, so it relies on the schema to explain parameters. Baseline 3 applies when schema does the heavy lifting.

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

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

The description states 'Compute a unified diff between two text strings', which uses a specific verb and resource, clearly distinguishing it from sibling tools like text formatting or encoding utilities. It also specifies the comparison scope (file versions, config changes, code revisions), making the purpose unambiguous.

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

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

The description explicitly provides use cases ('Use when comparing file versions, config changes, or code revisions'), giving clear context for when to apply the tool. It does not mention when not to use it or alternatives, so it lacks explicit exclusions, but the context is solidly provided.

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

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

The annotation readOnlyHint=true already covers safety, and the description adds the context of generating a hash (a pure computation) without side effects. It does not detail output format or edge cases, but with the annotation, the bar is lower, and the description adds minimal extra behavioral context.

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

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

The description consists of two focused sentences: one for the operation and one for use cases. It is concise, front-loaded with the action, and contains no unnecessary words.

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

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

For a simple tool with two well-defined parameters and a read-only annotation, the description covers purpose and use cases adequately. The main gap is not describing the output format (e.g., hexadecimal string), but this is not critical for such a straightforward operation.

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 provides complete descriptions for both parameters (text and algorithm) with 100% coverage. The description restates the algorithm list but omits sha384 (which appears in the schema enum), adding little beyond what the schema already provides. Baseline 3 is appropriate.

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

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

The description clearly identifies the tool as generating hashes (MD5, SHA-1, SHA-256, SHA-512) of input text. This distinguishes it from sibling tools like encryption or JWT signing, which serve different purposes.

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

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

The description provides clear use cases (checksums, data integrity, fingerprinting), which helps the agent decide when to use this tool. However, it does not explicitly state when not to use it or mention alternatives, stopping short of a full 5.

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

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

The description discloses the core behavior of creating and signing a JWT, which is the primary action. However, it adds little beyond the readOnlyHint annotation; for example, it does not mention that the token includes an expiration timestamp or that signing is a pure function. The annotation covers safety, so the description is adequate but not enriching.

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, focused sentence that immediately communicates the tool's purpose. It has no filler or redundant wording, earning every character it uses.

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

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

The description is minimally complete for a simple tool: it states the action and inputs, and the schema covers parameters. However, it does not mention the return value (the signed token string) or the optional 'expires_in' parameter by name, which could leave an agent unsure about the full output or options.

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 provides full coverage for all parameters, including descriptions for payload, secret, and expires_in. The description only repeats 'payload and secret' without adding extra meaning or constraints. Since schema coverage is 100%, the baseline score of 3 is appropriate.

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

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

The description clearly states the tool's function: 'Create and sign a JWT with a given payload and secret.' It uses a specific verb ('create and sign') and resource ('JWT'), distinguishing it from sibling tools like dev_jwt_verify. The purpose is unambiguous.

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

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

The description implies when to use the tool (whenever a JWT needs to be created) but does not explicitly mention alternatives or contrast with dev_jwt_verify. There is no guidance on when not to use it, which is a missed opportunity given the sibling context.

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

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

The annotation readOnlyHint=true already declares the non-destructive nature, and the description adds the 'verify and decode' behavior. However, it does not disclose details like error behavior on invalid signatures or whether the decoded payload is returned as a whole, which would be useful for debugging. With the annotation covering safety, a score of 3 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 consists of two short sentences, front-loaded with the primary action ('Verify and decode a JWT') followed by the usage context. Every word earns its place; there is zero redundancy or filler.

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 no output schema, the description sufficiently conveys the core purpose and use case. It does not detail return values or error handling, but the simplicity of the tool reduces the need for such information, making the description 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?

The input schema has 100% coverage: the single parameter 'token' is described as 'JWT token to decode', which is exactly what the description implies. The description adds no additional meaning beyond the schema, so the baseline of 3 is used.

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

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

The description clearly states the tool's function: 'Verify and decode a JWT.' It uses a specific verb ('verify') and resource ('JWT'), and the sibling tool dev_jwt_sign makes the contrast between signing and verifying explicit, distinguishing this tool from its most likely alternative.

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 a clear usage context: 'Use when debugging authentication tokens.' This implies the tool is for inspection/validation rather than creation. It does not explicitly mention alternatives, but the sibling dev_jwt_sign naturally signals the complementary use case, so the guidance is adequate.

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 readOnlyHint annotation already signals a safe, side-effect-free operation. The description adds no further behavioral detail (e.g., character set guarantees, entropy, or return format). It repeats the 'random' aspect but adds minimal value beyond what the annotation and schema 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 a single, front-loaded sentence that communicates the core purpose without waste. Every word earns its place, and the length is appropriate for the tool's simplicity.

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 nature of the tool, the rich schema (7 self-explanatory parameters), and the readOnly annotation, the description is mostly complete. It does not explicitly state the return format (e.g., a single string vs. an array when count > 1), but the schema's 'count' parameter and common sense make this 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 every parameter documented. The description's phrase 'configurable length and complexity' covers the length and boolean character-set parameters at a high level, but adds no syntax or constraint information beyond the schema. This meets the baseline.

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

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

The description clearly states a specific verb+resource: 'Generate a random password.' It also mentions configurable length and complexity, which distinguishes it from all sibling tools (none handle password generation). The title and name align, and the description goes beyond a tautology.

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 the tool's purpose (generating passwords) and the context is clear. There are no sibling password tools to exclude, so explicit alternatives are unnecessary. It doesn't state specific when-to-use scenarios, but the tool is self-contained with obvious applicability.

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 little beyond the readOnlyHint annotation. It states 'return matches' but does not clarify output format, behavior with invalid patterns, or edge cases (e.g., no matches). This is a minimal disclosure that does not go beyond the tool's core purpose.

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

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

The description is a single, concise sentence (9 words) that front-loads the core purpose. Every word contributes meaning, with no fluff or repetition.

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

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

For a simple tool with three documented parameters, a readOnly annotation, and no output schema, the description is mostly sufficient. It states the action and output but could benefit from clarifying the return format or behavior on invalid regex. Overall, adequate for the tool's simplicity.

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

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

Schema description coverage is 100% for all three parameters, so the schema already documents each parameter clearly. The description adds no parameter-level meaning beyond what the schema provides, warranting the baseline score 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 uses a specific verb ('Test') and resource ('regex pattern against a string') and clearly states the output ('return matches'). It distinguishes the tool from all siblings, none of which are regex-related.

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

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

The description gives clear context for use: testing regex patterns against strings. It does not explicitly mention alternatives, but there are no competing sibling tools, so exclusions are unnecessary. The intended use case is obvious.

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 the readOnlyHint annotation by specifying that it both parses and validates, and enumerates the output components. However, it does not disclose error handling behavior for invalid semver strings or the exact return format, which is a minor gap.

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

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

Two sentences, no filler. The first sentence defines the function, the second provides usage guidance. Very efficient.

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

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

For a simple tool with one parameter and no output schema, the description covers the input expected and the output components, and gives usage context. It could mention behavior on invalid input, but overall it's sufficient.

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

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

The schema covers the 'version' parameter with a generic description ('Version string to validate'). The description adds that it expects a 'semver string', which is more specific and clarifies the expected input format, adding value over 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 a specific verb ('Parse and validate'), a specific resource ('semver string'), and the output components ('major, minor, patch, pre-release, and build'). It distinguishes itself from sibling tools like dev_regex_test by focusing on semantic versioning.

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

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

Provides explicit use cases: 'Use when checking version compatibility or sorting releases.' This gives clear context for when to choose this tool, though it does not name alternative tools or explicitly state when not to use it.

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

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

Annotations declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds value by stating that the tool returns both Unix and ISO representations, clarifying the output behavior beyond the annotation.

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 every sentence adds value. No wasted words.

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

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

Given the tool's simplicity and rich schema (all params described), the description covers the return behavior ('Returns both Unix and ISO representations') which is essential without an output schema. It is complete enough for effective selection and invocation.

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

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

Schema description coverage is 100%, so all parameters (to, from, timestamp) are described in the schema. The description does not add significant meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states the tool converts between Unix timestamps and ISO 8601 dates, using specific verbs and resource. It distinguishes from sibling tools, as no other sibling handles 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 provides explicit 'when to use' context: normalizing date formats across APIs, logs, or databases. It does not include when-not-to-use or alternatives, but given no sibling tool overlaps, this context is sufficient.

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

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

Annotations declare readOnlyHint=true, so the safety profile is known. The description adds the transformation behavior (percent-encoded to readable text) but does not discuss edge cases like malformed input or the difference between decodeURIComponent and decodeURI, which the schema covers. It adds some context but not rich behavioral detail.

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

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

Two sentences, no fluff, front-loaded with the core action. 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?

For a simple utility with good annotations and full schema coverage, the description is complete enough. It explains purpose and usage, though it could mention return format or error behavior, but that is optional for this simple operation.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-level detail beyond the schema, but the schema already documents both 'text' and 'component' clearly.

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

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

The description states a specific action ('URL-decode a percent-encoded string') and the resource ('back to readable text'), clearly distinguishing it from sibling tools like dev_url_encode. It also provides concrete use cases (query parameters, redirect URIs, form values).

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

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

The description explicitly says 'Use when parsing query parameters, redirect URIs, or encoded form values', giving clear context for when to use the tool. It does not mention alternatives or when not to use it, but the context is sufficient.

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 readOnlyHint annotation already establishes that this is a safe, side-effect-free operation. The description adds the note about 'escaping special characters,' but does not explain differences between the two encoding modes (component=false vs true) or edge cases. With annotations present, the added behavioral detail is minimal but not contradictory.

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 primary function and followed by usage context. Every sentence earns its place with no redundant content.

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

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

For a simple utility with two well-documented parameters and a readOnly annotation, the description adequately covers purpose and usage. The return value is self-evident for URL encoding, and the schema clarifies the component parameter. A minor gap is the lack of explicit mention that the output is the encoded string, but this is implied.

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

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

Schema description coverage is 100%, with both 'text' and 'component' already described. The description only restates the purpose without adding new meaning to the parameters, especially the component boolean. Baseline 3 is appropriate.

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

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

The description clearly states the tool's function: 'URL-encode a string by escaping special characters.' It specifies the resource (a string) and the action (URL-encoding), distinguishing it from sibling tools like dev_url_decode and other encoding utilities.

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

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

Provides explicit use cases: 'building query parameters, form data, or safe URLs.' It gives clear context for when to use the tool, though it does not name specific alternative tools or state when not to use it.

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

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

Annotations already declare readOnlyHint=true, and the description does not contradict this. The description adds version info (v4 or v7) and use cases, but it does not explain the return format or the meaning of 'one or more' given the tool has zero parameters. The behavioral disclosure is adequate but minimal beyond the annotation.

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 primary action, and every word earns its place. It is concise without sacrificing necessary guidance.

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 (no parameters, no output schema) and the readOnlyHint annotation, the description adequately covers purpose and usage. The only gap is the unresolved 'one or more' phrasing, which leaves minor ambiguity about output multiplicity, but overall it is complete enough for effective selection and 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?

The tool has zero parameters, so the baseline is 4. However, the description's phrase 'one or more' implies a count or multiplicity that is not reflected in the empty input schema. This creates ambiguity rather than adding semantic value, so I deduct a point.

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

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

The description clearly states the tool's function: 'Generate one or more UUIDs (v4 or v7).' It specifies the resource (UUIDs) and the versions supported, and it is distinct from sibling tools like dev_hash or dev_password_generate. The verb 'generate' is specific and unambiguous.

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

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

The description provides explicit use cases: 'when you need unique identifiers for database records, request tracing, or idempotency keys.' This tells the agent when to select this tool, but it does not mention when not to use it or alternatives, so it falls short of a 5.

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

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

Annotations already provide readOnlyHint and openWorldHint. The description adds valuable context: it runs exactly 10 checks and returns a score, issues list, and summary. It doesn't cover edge cases like invalid URLs, but for a simple read-only tool this is sufficient.

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

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

Two sentences, front-loaded with the action and result. The description is extremely compact with zero filler.

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 well-documented parameter, no output schema, and safety annotations, the description is fully complete. It states what it does, what it returns, and when to use it, leaving no important gaps.

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

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

Schema covers the 'url' parameter with 100% coverage, including the 'must start with http:// or https://' constraint. The description only says 'webpage URL,' which adds no new meaning beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states a specific verb+resource: 'Run 10 basic accessibility checks on a webpage URL.' It also specifies distinct outputs (score, issues list, summary), which differentiates it from the many other lookup_* sibling tools like DNS, IP, or WHOIS.

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 says 'Use when you need a quick accessibility audit,' providing a clear use case. It doesn't mention when not to use it or alternatives, but there are no other accessibility tools in the sibling list, so no exclusion is needed.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds that the tool returns GPS coordinates and a confidence score, but it does not disclose potential failure modes (e.g., invalid addresses) or elaborate on network dependencies beyond what openWorldHint implies. It does not contradict the annotations.

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

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

The description is two sentences: the first leads with the verb and outcome, listing return fields; the second provides a usage directive. There is no filler or redundant phrasing.

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

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

The tool has only one parameter and no output schema, so the description carries the burden of documenting return values. It lists the structured components and confidence score, which is helpful. It lacks specifics on coordinate format or error behavior, but for a simple lookup tool this is 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 schema has 100% coverage for the single parameter 'address', with description 'Freeform address to validate.' The description's mention of 'freeform address' adds no additional semantic detail beyond the schema, so the baseline of 3 is appropriate.

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

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

The description explicitly states the action ('validate') and the resource ('freeform address'), and lists the output components (street, city, state, postal code, country, GPS coordinates, confidence score). This clearly distinguishes it from sibling lookup/decoding tools by focusing on address parsing and verification.

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

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

The description includes a direct usage directive: 'Use when you need to verify or parse an address.' It does not explicitly mention when not to use the tool or alternative tools, but no sibling tool is an obvious alternative for address validation, so the context is clear.

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

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

Annotations mark it read-only and open-world. The description adds that it uses k-Anonymity, implying privacy-preserving behavior, and states the return value (breach count). This goes beyond annotations without contradiction.

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

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

Two concise sentences that front-load the main action and return value, then provide usage guidance. No wasteful wording.

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 no output schema, the description adequately covers what it does, returns, and when to use it. No output schema means the 'Returns breach count' is sufficient.

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

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

The input schema fully covers the single parameter with 'Password to check' (100% coverage). The description doesn't add additional constraints like hashing or format, so it meets the baseline but no 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 'Check if a password has appeared in known data breaches using k-Anonymity' and specifies it returns a breach count. This distinguishes it from sibling tools like dev_password_generate or other lookup_* tools by focusing on breach verification for passwords.

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

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

Provides a clear usage context: 'Use when you need to verify password safety.' It doesn't explicitly name alternatives or when not to use, but the tool's purpose is unique enough that the context is sufficient.

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 cover safety (readOnlyHint=true) and open-world nature (openWorldHint=true), so the description only needs to add context. It does list the returned fields, but doesn't disclose behavior for invalid domains or data freshness, which is acceptable but not rich.

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: the first states purpose and output, the second gives the use case. Every word earns its place, and the description is appropriately short.

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 one-parameter tool with no output schema, the description covers purpose, input, output fields, and use case. It doesn't mention fallback behavior for missing domains, but openWorldHint annotation and simplicity keep it sufficiently complete.

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

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

The schema already fully documents the single 'domain' parameter with a clear description. The tool description merely restates 'by domain name' without adding format or validation details, so it adds no value beyond the schema.

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

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

The description clearly states the tool looks up company information by domain name and lists specific return fields. It differentiates from sibling lookup tools (e.g., lookup_email, lookup_dns) by targeting companies 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?

Provides an explicit use case: 'Use when enriching leads or researching organizations.' This gives clear context but no explicit when-not-to-use or alternative tool names.

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 and openWorldHint, so the safety profile is known. The description adds little beyond that—it doesn't clarify that this performs a live DNS query, potential latency, or how results are returned. However, it doesn't contradict annotations.

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

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

The description is two sentences, front-loaded with the action and resource, and includes a usage clause. Every word earns its place with no fluff.

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

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

The tool is simple and annotations cover safety, but the description lacks information about the response format (no output schema) and omits some record types (SOA, PTR) that are in the schema. Slight gaps for external behavior and return values prevent a higher score.

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

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

Schema coverage is 100% for parameters, so the baseline is 3. The description lists some record types but omits SOA and PTR from the schema, and it doesn't add semantic detail beyond what the schema provides. It does reiterate 'domain' context but adds no new meaning.

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 queries DNS records for a domain, listing specific record types. It uses a specific verb ('Query') and resource ('DNS records'), and the 'Use when' phrasing distinguishes it from related lookup tools like lookup_dns_monitor.

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

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

The description explicitly says 'Use when you need to check DNS configuration or troubleshoot domain resolution,' providing clear usage context. It does not name alternatives or exclusions, but the stated use case sufficiently guides selection 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 declare readOnlyHint=true, so the safety profile is already known. The description adds meaningful behavioral context by revealing that the tool maintains a snapshot and compares against it, which is a stateful behavior not apparent from annotations. It does not cover edge cases like first-run behavior, but it goes beyond the annotations.

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

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

The description is exactly two sentences, front-loaded with the primary action and key differentiator, and contains zero filler. 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?

For a simple read-only tool with annotated safety and 100% schema coverage, the description covers purpose, usage, and a key behavioral trait (snapshot comparison). However, with no output schema, it does not explicitly describe the return value; it only implies the output includes the detected changes. This is a minor gap, but the tool context is still sufficiently complete for selection and 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% (both 'domain' and 'types' have clear descriptions). The tool description does not add any parameter-specific guidance beyond the schema, which already fully explains the parameters. The baseline of 3 applies because 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 states a specific action ('Check DNS records for a domain'), adds the distinctive feature 'compare against the previous snapshot' and 'Detects record changes over time', and clearly separates this tool from sibling lookup_dns by focusing on monitoring and change detection. It is a specific verb+resource with clear scope.

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

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

The description provides explicit usage context with 'Use when monitoring DNS configuration.' It does not, however, mention when not to use the tool or explicitly name an alternative (e.g., lookup_dns), so it lacks the full when/when-not/alternatives structure that would merit a 5.

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, so the safety profile is known. The description adds behavioral detail beyond the annotations by stating what returns: 'availability status and WHOIS data if registered.' This is useful context that the annotations do not 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, front-loaded with the core purpose and immediately followed by usage context and return information. Every sentence adds value with no waste.

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

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

Given the low complexity (single parameter, no output schema, simple lookup), the description is complete. It mentions the return payload (availability status, WHOIS data) and the context of use, which is sufficient for an agent to invoke correctly.

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

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

The input schema covers the single parameter 'domain' with a clear example ('example.com'), giving 100% coverage. The tool description does not add extra parameter semantics beyond what the schema already provides, so the baseline of 3 is appropriate.

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

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

The description clearly states the tool's function: 'Check if a domain name is available for registration.' It uses a specific verb and resource, and the context 'brainstorming project names or validating domain ideas' distinguishes it from other lookup tools.

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

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

It provides explicit use cases ('Use when brainstorming project names or validating domain ideas'), but does not mention alternatives or when not to use it, such as distinguishing from lookup_whois. This is a clear context, though not fully differentiating from similar 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 indicate readOnlyHint=true, so the read-only nature is known. The description adds the specific validation methods (syntax, MX, disposable), which is useful. However, it does not describe the return value format or edge cases (e.g., missing MX), and openWorldHint is not explained, leaving some behavioral ambiguity.

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

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

The description is two sentences with no fluff. The purpose and usage are front-loaded, making it easy to scan and 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?

For a single-parameter read-only lookup tool with annotations, the description covers purpose, usage, and validation components. It lacks return value details, but the tool is simple enough that this is not a critical 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 schema covers the single parameter 'email' with a description, and the tool description also references email addresses. No additional parameter semantics are provided beyond the schema, meeting the baseline for 100% 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 identifies the tool as validating an email address with specific checks (syntax, MX record, disposable check). It distinguishes from sibling tools like lookup_address_validate and lookup_whois by explicitly focusing on email deliverability.

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

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

The description gives a clear usage context: 'Use when you need to verify if an email address is real and deliverable.' It does not mention alternatives or exclusions, but the context is sufficiently specific for this simple tool.

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

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

Annotations already declare readOnlyHint: true and openWorldHint: true, so the safety profile is covered. The description adds that it fetches headers for a URL, but it does not disclose details like redirect behavior, response size limits, or authentication requirements. It provides some context but is not rich in behavioral traits.

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

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

The description is concise at two sentences, with the primary action front-loaded. Every sentence adds value: the first defines the action and resource, the second gives concrete use cases. No wasted words.

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

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

The tool has no output schema, so the description could explain what the response contains, but it only says 'fetch HTTP response headers' without detailing the response format. Also, the empty schema and lack of URL parameter create ambiguity about how the target URL is specified. Annotations cover safety, but the description is incomplete for a practical tool.

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

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

The tool has zero parameters, and the schema is empty. Per rubric, 0 params is a baseline of 4. The description mentions 'for a URL' but does not specify how the URL is provided, which is a minor ambiguity, but since there are no parameters to document, the baseline holds.

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 'Fetch HTTP response headers for a URL' with a specific verb and resource. It implicitly distinguishes from sibling lookup_* tools by focusing on HTTP headers, but it does not explicitly name alternatives or contrast with other tools.

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

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

The description provides explicit use cases: 'inspecting server configuration, security headers, or caching policies.' It gives context for when to use the tool, though it does not mention when not to use it or list alternative tools.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds that it returns geolocation, ISP, and network details, but the optional IP behavior is already in the schema. It does not contradict annotations, but it adds only moderate behavioral context beyond what structured fields 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?

Two tightly written sentences: the first states action and scope, the second gives a usage directive. No filler 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?

With no output schema, the description lists the return categories (geolocation, ISP, network details, AS number). It also covers the optional IP default via schema. For a simple, well-annotated tool, this is fairly complete, though it could mention specifics like IPv6 support or response format.

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

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

Schema coverage is 100% because the only parameter 'ip' has a detailed description including the default behavior. The tool description adds no additional parameter semantics, so the baseline of 3 applies.

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

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

The description uses a specific verb ('Look up') and resource ('IP address') and lists concrete data types (geolocation, ISP, network details). This clearly distinguishes it from siblings like lookup_dns or lookup_whois, which serve different lookup purposes.

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

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

The description explicitly states when to use: 'Use when you need to determine the physical location, internet provider, or AS number for a given IP.' This provides a clear context, though it does not mention exclusions or alternative tools, so it stops short of a perfect 5.

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 include readOnlyHint=true and openWorldHint=true, establishing the read-only nature. The description adds behavioral details such as validation and the specific return fields (country, carrier, line type, E.164 format), which goes beyond annotations. It does not disclose error handling or format limitations, but the annotations already cover the safety profile.

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 compact: two sentences, with the first sentence stating the core function and output, and the second providing usage guidance. No redundant or filler content.

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

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

Given the tool's simplicity (one parameter, no output schema), the description adequately covers purpose, usage, and expected output fields. It lists the key return values, making the tool understandable without an output schema. Minor gaps like potential error behavior or supported regions do not detract significantly for such a lightweight 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 full coverage (100%) with a description for the 'phone' parameter ('Phone number to validate'). The tool description adds minimal extra semantic value about the parameter itself, only hinting at E.164 format in the output context. Therefore, baseline 3 is appropriate since 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 function: 'Parse and validate a phone number, returning country, carrier, line type, and E.164 format.' This specific verb+resource combination sets it apart from sibling lookup tools like lookup_email or lookup_ip.

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 normalizing user-submitted phone numbers or verifying contact data.' This tells the agent when to invoke this tool, though it does not explicitly mention alternatives or exclusion 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?

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds only the purpose-level detail ('details and expiry') without extra behavioral context like output format, latency, or network behavior, which is what would be needed to go beyond annotations.

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

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

The description is two sentences, front-loaded with the action and resource, then immediately followed by a usage rationale. Every word earns its place with no filler 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 one-parameter read-only lookup, the description sufficiently conveys the scope and use case. The absence of an output schema is mitigated by the straightforward nature of the tool, though 'details' remains somewhat vague as to what specific certificate data is 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?

The input schema covers the single 'domain' parameter with a description ('Domain to check') at 100% coverage. The tool description merely echoes 'for a domain', adding no essential meaning beyond what the schema already provides, so baseline score is appropriate.

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

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

The description states a specific action ('Check SSL certificate details and expiry') and resource (domain), making the purpose clear. However, it doesn't explicitly differentiate from the closely named sibling tool lookup_ssl_cert_expiry, so it lacks distinct sibling differentiation.

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

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

The description gives clear when-to-use context: 'Use when verifying HTTPS configuration or checking certificate validity.' It provides a solid usage context but does not mention exclusions or when to prefer an alternative such as lookup_ssl_cert_expiry.

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, covering the safety profile. The description adds what data is returned (valid dates, days remaining, flags) but does not disclose additional behavioral traits like potential network latency, error conditions, or output formatting. This adds some context beyond annotations but is not extensive.

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

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

Three short sentences: action, return data, usage context. All information is relevant, no filler. The structure is front-loaded with the primary purpose and follows with useful details.

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

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

For a simple read-only tool with one parameter and no output schema, the description is remarkably complete. It explains the purpose, the return data, and the usage scenario. Given the low complexity and the presence of annotations, no critical information is missing.

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

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

The input schema already describes the only parameter 'domain' as 'Domain to check' with 100% coverage. The tool description repeats this without adding format constraints, examples, or validation rules. Since schema coverage is high, the description adds minimal value beyond the schema itself.

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

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

The description states a specific action: 'Check SSL certificate expiry for a domain.' It lists the exact return values (issuer, valid dates, days remaining, expired/expiring-soon flags), making it distinct from broader sibling tools like lookup_ssl. The verb 'check' plus the resource 'SSL certificate expiry' is unambiguous.

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

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

The description includes 'Use when monitoring certificate health,' providing clear context for when to invoke this tool. It does not explicitly name alternatives or exclusion scenarios, such as contrasting with lookup_ssl, but the stated use case is sufficient for most situations.

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 (readOnlyHint=true, openWorldHint=true) already cover safety and the external nature of the input. The description adds the specific scope of detection (frameworks, CMS, analytics) but does not disclose any additional behavioral traits such as potential rate limits, network requirements, or likely output format. It adds some value but not rich behavioral context.

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

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

The description is two concise sentences, front-loaded with the primary purpose and followed by a practical use case. Every sentence contributes value, and there is no redundant or verbose language.

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

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

For a simple tool with one parameter, no output schema, and safety annotations, the description adequately covers purpose and usage. It does not explicitly describe the return format or limitations, but the intended behavior is clear enough for a competent agent. The description is complete for the tool's complexity, though slightly more detail on output would push it to a 5.

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

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

The input schema has 100% description coverage, with the 'url' parameter fully described as 'The full URL to analyze (must start with http:// or https://).' The tool description adds no extra meaning beyond what the schema provides, so the baseline of 3 applies.

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

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

The description clearly states the tool's function: 'Detect technologies used by a website (frameworks, CMS, analytics).' This uses a specific verb and resource, and the examples (frameworks, CMS, analytics) distinguish it from sibling lookup tools like lookup_headers or lookup_dns, which focus on other aspects of a website.

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 a clear use case: 'Use when analyzing a competitor's tech stack.' This gives context for when to employ the tool but does not explicitly mention alternatives or when not to use it, so it stops short of a 5.

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, covering the safety profile. The description adds the specific metadata fields extracted but does not disclose network fetch behavior, error handling, or response format, which are relevant for an open-world tool.

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

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

Two concise sentences, front-loaded with the action and a clear usage cue. Every word earns its place; there is no unnecessary detail or repetition.

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

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

For a simple single-parameter tool with annotations covering safety, the description provides purpose and usage context sufficiently. It lacks details about network behavior and potential failures, but these are not critical for a basic metadata extraction tool with no output schema.

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

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

The schema covers the single 'url' parameter fully (100% coverage) with description 'URL to fetch and extract metadata from'. The tool description does not add additional parameter semantics beyond the schema's existing description, so the baseline 3 applies.

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

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

The description uses the specific verb 'extract' with the resource 'URL metadata' and lists concrete items (title, description, OG tags, favicon), making the tool's purpose unmistakable. It clearly distinguishes itself from sibling lookup tools like lookup_headers or lookup_tech_detect.

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

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

Provides explicit usage context: 'Use when you need to preview or summarize a webpage.' This clearly signals the intended scenario but does not mention when not to use it or alternatives, so it stops short of a 5.

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 operation. The description adds that it 'Returns detection type and provider details,' but does not disclose any limitations, data privacy aspects, or behavioral nuances beyond that.

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-loads the primary purpose, then provides usage guidance and return value info. Every sentence earns its place with no redundancy or filler.

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

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

For a simple single-parameter lookup tool with annotations and a clear return type mention, the description is complete. It conveys what, when, and what to expect, requiring no further elaboration given the simplicity and no output schema.

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

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

The input schema fully documents the sole parameter 'ip' as 'IPv4 address to check' (100% coverage). The tool description does not add additional semantics beyond the schema, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool's function: 'Check if an IP address is a VPN, proxy, or Tor exit node.' It uses a specific verb and resource, and the detection types distinguish it from sibling tools like lookup_ip or lookup_tech_detect.

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: 'Use when assessing connection trustworthiness or flagging suspicious traffic.' It does not explicitly name alternatives or exclusions, but the scenario is clear and distinct from 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, covering the safety and external-data profile. The description adds specificity about the returned fields (ownership, registrar, expiration) but doesn't disclose any caveats like rate limits or privacy redaction, which would be valuable for a WHOIS lookup. Thus a 3.

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 and 22 words, with the primary action front-loaded. Every sentence contributes: the first states what it does, the second states when to use it.

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

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

For a simple one-parameter read-only tool with no output schema, the description adequately conveys the purpose and the type of data returned (ownership, registrar, expiration). It doesn't mention output format or common WHOIS limitations, but the simplicity and annotations keep it 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?

The single 'domain' parameter is fully described in the schema ('Domain name to look up'), achieving 100% schema coverage. The description doesn't add any additional syntax, format, or normalization guidance for the domain parameter, so it stays at the baseline.

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

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

The description clearly states a specific verb ('Get') and resource ('WHOIS registration data for a domain'), and enumerates the key data types (ownership, registrar, expiration date), which distinguishes it from sibling lookup tools like lookup_dns or lookup_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 explicitly says 'Use when you need to find domain ownership, registrar, or expiration date', providing clear context for when to invoke this tool. It doesn't mention exclusions or alternatives, so it falls short of a 5.

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 include idempotentHint: true, which the description does not contradict. The description adds behavioral context by specifying the AES-256-GCM algorithm and the need for a passphrase, going beyond the annotation's mere idempotency hint.

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, clear sentence that directly states the tool's function without unnecessary words. It is well-structured and front-loaded with the core action.

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

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

For a simple decryption function with two well-documented parameters and no output schema, the description sufficiently captures the tool's purpose and method. It includes the algorithm and passphrase requirement, making it complete enough for an agent to select and invoke correctly.

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

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

The input schema already documents both parameters fully (base64-encoded ciphertext and password used during encryption). The description's mention of 'passphrase' adds little beyond the schema's 'password' field, so it relies on the schema for parameter semantics.

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

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

The description uses a specific verb 'Decrypt' and identifies the resource as 'AES-256-GCM encrypted text' with a 'passphrase', clearly distinguishing it from siblings like security_encrypt or text_base64_decode. It precisely states what the tool does.

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 makes the use case clear: decrypting AES-256-GCM ciphertext with a passphrase. It does not explicitly name alternatives or exclusions, but the algorithm and ciphertext format strongly imply when this tool should be used.

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

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

Beyond the ambiguous idempotentHint, the description does not disclose key derivation details, IV/nonce behavior, output format (e.g., base64, hex, tags), or reversibility. This is a significant gap for an encryption tool that must be correctly invoked.

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

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

The description is a single concise sentence with no irrelevant content. It is front-loaded and to the point.

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

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

No output schema exists, and the description does not explain what the encrypted result looks like or any security context. Given the simplicity of the tool, it should at least mention the output format or passphrase handling.

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

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

Schema coverage is 100% with clear parameter descriptions, so the baseline is 3. The description itself adds no further semantic detail beyond what the schema already provides.

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

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

The description clearly states the tool encrypts text using a specific algorithm (AES-256-GCM) with a passphrase. The verb 'encrypt' and resource 'text' are explicit, and it distinguishes from siblings like security_decrypt and dev_hash.

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?

Usage is implied by the name and description, but there is no explicit guidance on when to use this tool versus alternatives such as hashing, signing, or decryption. No exclusions or alternative tool references are provided.

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

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

Annotations already provide readOnlyHint=true, and the description adds the types of PII detected (emails, phones, SSNs). However, it does not disclose output format, confidence, false-positive behavior, or any side effects. The bar is lowered due to annotations, but the description still leaves uncertainty about what the result looks like.

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 sentence, front-loaded with the core action, and contains no fluff. It efficiently communicates the tool's purpose and example PII types.

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

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

The tool has no output schema, and the description does not explain what the return value looks like (e.g., list of matches, positions, types). It also omits any limitations or edge cases. While the input parameter is well covered, the user is left without clarity on how 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% for the single 'text' parameter, which already states it is the text to scan. The description in the tool adds no additional parameter meaning beyond what the schema provides. Baseline for high coverage is 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 uses the specific verb 'detect' with the resource 'personally identifiable information (emails, phones, SSNs) in text.' This clearly distinguishes it from sibling tools like lookup_email or lookup_phone, which are lookups by known values rather than detection within 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?

The description implies when to use the tool—when you need to detect PII in text—but it does not explicitly state alternatives or exclusions. No mention of when not to use it or comparisons to other tools. The context is inferred from the tool's name and description.

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 covers the safety profile. The description adds that it produces both a secret and a provisioning URI, but it does not clarify that the 'secret' parameter must be provided as input, potentially implying a random secret is generated. This ambiguity is a meaningful gap beyond what annotations convey.

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

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

The description is a single concise sentence with no filler or repetition. Every word earns its place, and it is easily scannable.

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

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

The description conveys the core purpose and benefits from well-documented parameters and annotations, but it leaves ambiguity about whether the secret is generated or provided as input, and the exact return format is unspecified. No output schema further increases the need for description detail, so this is a moderate 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?

All three parameters have detailed descriptions in the input schema (digits, period, and base32 secret), so the schema already provides full parameter semantics. The tool description adds no additional information about parameters 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 uses the specific verb 'Generate' and clearly names the two outputs ('TOTP secret and provisioning URI'), which distinguishes this from sibling tools like dev_password_generate or security_encrypt. Adding 'for 2FA setup' provides clear scope and 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 phrase 'for 2FA setup' gives clear context on when to use the tool. However, it does not explicitly mention when not to use it or alternatives among sibling tools. This is a clear context without exclusions, hence a 4.

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 meaningful behavioral detail beyond the readOnlyHint annotation by specifying the validation methods (Luhn check, network detection). However, it doesn't specify the return value format (boolean vs. detailed object), which is a minor gap given no output schema is 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 a single, front-loaded sentence that conveys the core purpose and key behavioral details without any fluff. 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?

While the tool is simple with one parameter and clear annotations, the lack of an output schema and the ambiguity around 'network detection' (does it return a boolean, the network name, or a full validation report?) leaves the description slightly incomplete. A more explicit statement about the return value would make it fully complete.

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

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

The schema already provides a clear description for the 'number' parameter ('Credit card number to validate'), achieving 100% coverage. The tool description adds context about what validation is performed but doesn't elaborate on parameter formatting or constraints beyond the schema.

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

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

The description clearly states the tool validates a credit card number using specific algorithms (Luhn check, network detection). The verb 'validate' and resource 'credit card number' are precise, and it distinguishes itself from sibling tools like security_decrypt or lookup_breach_check by focusing exclusively on credit card 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 context is clear: use this tool when you need to validate a credit card number. While it doesn't explicitly mention when not to use it or name alternatives, the unique purpose among siblings makes the usage unambiguous. There's no confusion with other 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 annotation readOnlyHint: true already establishes that this is a safe, read-only operation. The description adds a modest amount of context by stating the return value ('Returns the original plaintext string') and specifying UTF-8 as the output encoding. It doesn't disclose edge-case behavior (e.g., invalid base64 handling), but given the annotation coverage, this is acceptable.

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

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

The description is two sentences long and front-loaded with the core action. It immediately states what the tool does, then provides a use case and return value, with zero wasted words. Every sentence serves a purpose.

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

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

For a simple, read-only utility with only two parameters, the description is complete enough. It covers the action, use cases, and return value. There is no output schema, so the stated return value is useful. It doesn't address error conditions, but that's not critical for this tool's simplicity and the annotation already signals safety.

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%, meaning both parameters ('encoded' and 'urlSafe') are already fully described in the input schema. The description adds no additional parameter-level detail, so the baseline score of 3 is appropriate. The schema carries the load here.

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

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

The description clearly states the tool's action: 'Decode a Base64 string back to UTF-8 text.' This is a specific verb+resource combination that accurately distinguishes it from the sibling tool text_base64_encode. The purpose is unambiguous and immediately understandable.

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

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

The description provides explicit use cases: 'Use when extracting data from Base64-encoded API responses, tokens, or email headers.' This gives clear context for when the tool is appropriate. However, it doesn't explicitly mention when not to use it or name alternatives like text_base64_encode as a contrast, so it falls short of a 5.

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 by specifying the input encoding as UTF-8, which is beyond the readOnlyHint annotation. It also explains the intended purpose, but does not detail edge cases or output formatting, keeping it from a 5.

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

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

The description is two sentences long, with the core action in the first sentence and the use case in the second. No wasted words, and it is front-loaded with the key information.

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

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

For a simple encoding tool, the description covers the essential purpose, usage, and input encoding. The schema provides full parameter details and the annotation declares read-only behavior, so no additional context is needed.

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

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

Schema description coverage is 100%, so the schema fully documents both parameters. The description does not add extra parameter information, matching the baseline of 3 for high 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 function: 'Encode a UTF-8 string to Base64.' It uses a specific verb and resource, and the second sentence adds a use case, distinguishing it from sibling tools like text_base64_decode and dev_url_encode.

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 a clear usage context: 'Use for embedding data in URLs or APIs that require Base64.' However, it does not explicitly mention when not to use the tool or name alternatives, so it falls short of the highest bar.

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 include readOnlyHint: true, which aligns with the non-mutating 'Parse' action. The description adds the output format ('JSON array of objects'), but does not disclose edge-case behaviors like error handling or encoding assumptions. It provides some value beyond the annotation but not rich context.

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

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

Two concise sentences: the first states the core function, the second gives the use case. Every word earns its place, with no fluff or repetition of schema details.

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

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

The tool is simple with 3 well-documented parameters and no output schema. The description covers the output format ('JSON array of objects') and the typical use case, making it complete enough for an AI agent to invoke correctly. It doesn't need to repeat parameter details already in the schema.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-level details beyond what the schema already provides (csv, delimiter, has_header). It only reinforces the general CSV-to-JSON concept.

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 CSV text') and the output ('a JSON array of objects'), distinguishing it from sibling tools like text_xml_to_json and text_yaml_to_json. The verb 'Parse' plus resource 'CSV' makes the purpose unambiguous.

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

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

The description provides a clear use case: 'Use when converting spreadsheet or tabular data into structured JSON for processing or storage.' It lacks explicit when-not-to-use or named alternatives, but the context is sufficiently clear for selecting this tool over similar ones.

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 readOnlyHint annotation already indicates a safe non-destructive operation, so the bar is lower. The description adds 'clean Markdown' and 'extracting readable content,' which implies output quality, but does not disclose details about handling malformed HTML, lossy conversion, or preservation of specific elements. It provides some context beyond annotations but not rich behavioral detail.

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 action and then usage guidance. There is no fluff or repetition, 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?

For a simple one-parameter tool with no output schema, the description covers the purpose, usage scenarios, and implied output format ('Markdown'). It does not mention edge cases or limitations, but the simplicity of the tool and the readOnlyHint annotation make the description adequately complete for an agent to select and invoke it.

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

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

Schema coverage is 100% for the only parameter 'html' with a clear description. The tool description does not add further parameter-specific meaning beyond restating the conversion, so it meets the baseline for high schema coverage but provides no additional value.

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

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

The description uses a specific verb and resource: 'Convert HTML to clean Markdown.' It clearly states the conversion direction, distinguishing it from the sibling tool text_markdown_to_html, and adds context with 'extracting readable content from web pages.'

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

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

The description explicitly says 'Use when extracting readable content from web pages or migrating HTML docs to Markdown format.' This gives clear context and use cases, though it does not mention when not to use it or explicitly name alternatives like text_markdown_to_html.

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 readOnlyHint annotation covers safety. The description adds meaningful behavioral context (configurable indentation, purpose of readability) beyond annotations. It does not address invalid JSON handling or return format, but the annotation lowers the burden for this simple tool.

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

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

The description is two sentences, front-loaded with the action, and contains zero filler. It efficiently communicates purpose and usage.

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

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

For a simple tool, this description is largely complete: schema covers params, annotations cover safety, and description covers purpose. There is no output schema, but the output (formatted string) is implied. Minor gap: no discussion of error handling or large inputs, but not critical.

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

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

Schema coverage is 100% with descriptions for both parameters (json, indent). The description's mention of 'configurable indentation' aligns with the indent param but adds no extra semantics beyond what the schema already provides. Baseline 3 applies.

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

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

The description clearly states the tool's function with a specific verb (format/pretty-print), resource (JSON string), and configurable indentation. It distinguishes itself from sibling tools 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?

It provides explicit usage context: 'Use when making minified or compact JSON readable for debugging or documentation.' However, it does not explicitly name alternatives or when-not-to-use cases, so it stops short of a 5.

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, so the safety profile is covered. The description adds the use case of human-readable config files but does not disclose additional behavioral traits such as return format, handling of edge cases, or limitations. With annotations providing the safety context, the description adds only modest value.

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 action and followed by a use case. Every word earns its place with no fluff or redundancy.

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

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

For a simple two-parameter converter with readOnlyHint annotation and no output schema, the description is largely sufficient. It states the purpose and usage context. However, it could be slightly more complete by mentioning that the output is a YAML string, though this is implied by the tool name and conversion nature.

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

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

Schema description coverage is 100%, meaning both `data` and `indent` parameters are already described in the input schema. The description does not add any parameter-specific semantics beyond what the schema provides, so it meets the baseline but does not exceed it.

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

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

The description clearly states the tool's function: 'Convert JSON data to YAML format.' This specific verb+resource combination distinguishes it from sibling tools like text_yaml_to_json and other text_* converters. The additional context about generating human-readable config files reinforces its 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 a clear use case: 'Use when generating human-readable config files from JSON data structures.' This gives context for when to use the tool, though it does not explicitly mention alternatives or when not to use it, missing the full guidance of an explicit exclusion.

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

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

Annotations already mark this as read-only (readOnlyHint=true). The description adds that it 'report[s] parsing errors', giving insight into its behavior (output includes error details). This is useful context beyond the annotation, though it does not specify the exact return structure.

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?

A single, front-loaded sentence that clearly states the purpose and behavior. No filler or redundancy. It wastes no words and achieves exactly what the description needs.

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 one-parameter tool with full schema coverage, strong purpose clarity, and a read-only annotation, the description is reasonably complete. It mentions error reporting, providing some insight into the return value, though without an output schema it could further clarify the success/error response format. Overall, it is sufficient for the tool's simplicity.

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

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

The input schema covers 100% of the parameter with description 'JSON string to validate'. The tool description essentially repeats this ('Validate whether a string is valid JSON') without adding new meaning or clarifying edge cases (e.g., error format, empty strings). With full schema coverage, the baseline is 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 states a clear, specific action: 'Validate whether a string is valid JSON and report parsing errors.' It uses the verb 'validate', identifies the resource (JSON string), and distinguishes itself from sibling tools like text_json_format or text_json_to_yaml by focusing on 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 usage context is implied: use this when you need to check if a string is valid JSON. However, no explicit alternatives or 'when not to use' guidance is provided, even though sibling tools like text_json_format exist. It is not misleading, but it does not actively steer the agent away from other 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 claims 'Returns sanitized HTML,' but the input schema shows sanitize defaults to false, meaning the output is not sanitized by default. This misleading statement is a behavioral inaccuracy. The readOnlyHint annotation is positive, but the description doesn't disclose the gfm default or the effect of the sanitize parameter 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 three short sentences and front-loads the core purpose. Each sentence serves a function: purpose, usage, and return value. However, the return-value sentence is inaccurate, but that is a matter of correctness rather than conciseness.

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

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

For a simple read-only conversion tool, the description covers purpose, usage, and return type, but it fails to accurately describe the sanitization behavior and omits the gfm option's effect. The schema covers parameter details, so the description is mostly adequate but not fully complete due to the misleading sanitized statement.

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

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

The schema provides complete descriptions for all three parameters (markdown, gfm, sanitize) with 100% coverage, so the baseline is 3. The description adds no additional parameter-level semantics; it only mentions the overall conversion and the misleading sanitization claim.

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

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

The description opens with 'Convert Markdown text to HTML,' which is a specific verb and resource. It also provides a clear use case ('rendering Markdown content for web display or email templates') and inherently distinguishes itself from sibling tools like text_html_to_markdown by its stated direction of 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 states 'Use when rendering Markdown content for web display or email templates,' providing a clear context for when to invoke the tool. It does not list exclusions or alternative tools, so it stops short of a full when/when-not comparison.

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 the operation is non-mutating. The description adds no extra behavioral context beyond restating the conversion purpose; it does not disclose how invalid XML is handled, potential data loss, or output structure. This meets the baseline but lacks additional value.

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 the first sentence front-loading the core function and the second adding usage context. It contains no redundant phrasing or unnecessary detail, 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?

For a simple single-parameter conversion tool with a read-only annotation and no output schema, the description provides essential purpose and usage guidance. It does not specify the exact JSON output format or error behaviors, but these are not critical for a straightforward conversion tool, so it is almost complete.

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

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

The input schema covers the only parameter 'xml' with the description 'XML string to convert', achieving 100% coverage. The tool's description adds no further parameter-level semantics beyond what the schema already provides, so the baseline score of 3 applies.

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

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

The description clearly states the tool's function with a specific verb ('Convert') and resource ('XML documents to JSON'). It distinguishes itself from sibling converters like text_csv_to_json and text_yaml_to_json by naming the input format, and adds concrete use cases ('XML API responses, feeds, or config files').

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 specifies when to use the tool ('Use when parsing XML API responses, feeds, or config files'), providing clear context. However, it does not mention when not to use it or name alternative tools, so it slightly falls short of the highest tier.

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

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

With readOnlyHint=true, the safety profile is already clear. The description adds the return behavior ('Returns a JSON object'), which is valuable because there is no output schema. It does not disclose error handling or invalid-input behavior, but for a straightforward conversion tool this is sufficient.

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

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

The description is concise, with three short sentences each serving a purpose: what it does, when to use it, and what it returns. No redundant filler or unnecessary detail.

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 conversion tool with no output schema and read-only annotation, the description covers all essential aspects: purpose, usage context, and return type. It is complete for an agent to select and invoke the tool correctly.

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

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

The schema already provides 100% parameter coverage, describing the 'yaml' parameter as 'YAML string to convert'. The description only restates this as 'YAML configuration or data', adding no new semantic detail. Therefore, the baseline score of 3 applies.

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

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

The description states a specific action 'Convert YAML configuration or data to JSON' with a clear resource scope. It effectively distinguishes from sibling tools like text_csv_to_json, text_xml_to_json, and text_json_to_yaml by specifying the exact conversion direction and input type.

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 a clear usage context: 'Use when parsing YAML config files for programmatic access or API consumption.' This tells the agent when to invoke this tool, though it does not explicitly mention alternatives or exclusions, which is acceptable given the simplicity.

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, so the agent knows this is a safe read operation. The description adds that formatting is language-aware and focuses on style rules, which is useful but does not disclose other behavioral details like output format or error handling. No contradiction exists between description and 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 only three sentences, but the second sentence about supported languages is both inaccurate and redundant given the schema. It wastes space and introduces misinformation. A concise, accurate description would focus on the actual supported languages and clearly differentiate from sibling formatting tools.

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

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

The description is incomplete for a tool with no output schema and multiple siblings. It fails to mention the actual language enum or how it differs from transform_sql_format and minify tools. It also claims unsupported languages, creating a significant completeness gap. Annotations cover read-only behavior, but the description omits essential return-value and supported-language context.

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

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

The schema already documents both parameters fully (100% coverage), so the baseline is 3. However, the description's language list (JS, TS, Python, Go, Rust) directly contradicts the schema enum (javascript, json, html, css, sql, xml). This actively misleads the agent into passing unsupported language values, making the description worse than if it had no parameter info.

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

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

The description clearly states the tool formats source code with style rules, but the claimed language support (JS, TS, Python, Go, Rust) contradicts the schema enum, which only includes javascript, json, html, css, sql, xml. This makes the purpose misleading. It also fails to distinguish from the sibling tool transform_sql_format, which would be a more specific alternative for SQL.

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 tells the agent when to invoke the tool. However, it does not mention any exclusions or alternatives, such as transform_sql_format for SQL-only formatting, so it lacks full 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 already declare readOnlyHint: true, covering the safety profile. The description adds some context about purpose but does not disclose additional behavioral traits such as handling of nested objects, output format, or limitations. It is not contradictory, but it does not go beyond what annotations and the schema already imply.

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

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

The description is two sentences long, front-loads the core purpose, and contains no fluff or redundant information. Every word contributes to understanding the tool's function and usage.

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 two parameters, no output schema, and no complex side effects, the description is nearly complete. It explains what and when, and the schema fills in the parameter details. A minor gap is not explicitly describing the output format, but that is largely inferable from 'TypeScript interfaces.'

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

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

Schema description coverage is 100% for both parameters (json and name), so the schema carries the full semantic weight. The description does not add parameter-level details, but per the rubric, baseline 3 is appropriate when the schema is sufficiently descriptive.

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

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

The description clearly states a specific verb and resource: 'Generate TypeScript interfaces from a JSON sample.' This distinguishes it from sibling tools like text_json_format or transform_sql_format, making the tool's purpose unambiguous.

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

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

The description provides clear context with 'Use when building type-safe API clients or converting API responses into TypeScript types.' It gives specific use cases but does not explicitly mention when not to use it or name alternative tools, so it lacks the full exclusionary guidance for 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 readOnlyHint annotation establishes safety, and the description adds specific behavioral details about what gets removed (whitespace, comments, redundant rules). This goes beyond the annotation and helps the agent understand the transformation's effects.

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

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

The description is two sentences long, front-loaded with the core action, and contains no fluff or redundant information. 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?

For a simple tool with one parameter, a read-only annotation, and no output schema, the description covers the purpose and usage adequately. It does not explicitly state the return value (minified CSS), but this is strongly implied by 'minify' and the input schema, making the description complete enough for the agent.

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

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

The schema already provides 100% coverage for the single 'code' parameter with the description 'CSS code to minify'. The tool description does not add significant parameter-level meaning beyond what the schema already states, so the baseline of 3 applies.

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

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

The description clearly states the tool's function: minifying CSS by removing whitespace, comments, and redundant rules. It distinguishes itself from sibling tools like transform_minify_js by explicitly naming CSS as the target resource.

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

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

The description provides an explicit use case ('Use when reducing stylesheet size for faster page loads') that tells the agent when to select this tool. It does not mention alternatives or exclusions, but the context is clear enough.

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

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

The description discloses the specific transformations performed (whitespace/comments removal, variable name shortening), going beyond the readOnlyHint annotation. It does not mention any caveats, but the annotation already covers non-destructiveness, so the bar is lower.

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 that front-load the verb and purpose, followed by a clear use case. No redundant words and every sentence adds value.

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

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

For a simple single-parameter tool with no output schema, the description adequately covers what it does and when to use it. It could mention the return value explicitly, but the tool's name and purpose make it obvious that it returns the minified code.

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

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

The input schema already fully describes the single parameter ('code' with description 'JavaScript code to minify'). The description reiterates 'JavaScript code' but adds no additional meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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

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

The description uses a specific verb ('Minify') with a clear resource ('JavaScript code') and explains what it does (removing whitespace, comments, shortening variable names). This clearly distinguishes it from sibling tools like transform_minify_css and transform_code_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?

Provides clear context for when to use the tool ('optimizing bundle size for production deployment'), giving a concrete scenario. It does not explicitly mention alternatives or exclusions, but the use case is specific enough to guide selection.

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

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

Annotations already convey readOnlyHint=true, and the description adds that it performs indentation and keyword casing. This is consistent and adds minor behavioral detail, but it does not disclose potential limitations (e.g., handling of invalid SQL, dialect-specific behavior) beyond what is expected for a formatting tool.

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

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

The description is two sentences, front-loaded with the core purpose, and every word adds value. It wastes no space and is easy to scan.

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

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

For a simple formatting tool with two well-documented parameters, no output schema, and readOnlyHint annotations, the description provides sufficient context for an agent to understand what it does and when to use it. No major information gaps remain.

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

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

Schema coverage is 100% with both 'sql' and 'indent' parameters documented in the input schema. The description does not add extra parameter details beyond the schema, so the baseline of 3 is appropriate.

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

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

The description uses a specific verb ('Format and beautify') with a clear resource ('SQL queries') and outlines the exact transformations (indentation, keyword casing). It distinguishes itself from sibling tools like transform_code_format or text_json_format by explicitly targeting SQL.

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

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

The description clearly states when to use the tool ('cleaning up inline SQL for code reviews, documentation, or debugging'), providing actionable context. It does not explicitly mention when not to use it or list alternatives, but the usage scenarios are specific enough to guide selection.

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