font-metadata

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

There are no annotations, so the description carries the full burden of behavioral disclosure. It only states the tool's function without revealing the return format, potential errors, or that it is a read-only operation. The schema does mention 'If omitted, returns all supported languages' but the description itself lacks this transparency.

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

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

The description is a single, front-loaded sentence with no redundancy. It conveys both use cases (general check and specific verification) efficiently.

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

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

For a simple check tool with fully documented parameters and no output schema, the description is adequate but not comprehensive. It could mention what the response looks like (e.g., list of supported languages or boolean results) and edge cases, but it remains minimally viable.

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

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

Schema coverage is 100% with clear descriptions for both parameters (font_id and languages). The tool description adds no extra meaning 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 clearly states the tool's function with a specific verb ('check') and resource ('which languages/scripts a font supports'), and it distinguishes itself from sibling tools by focusing specifically on language support rather than font generation, metadata, or pairings.

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

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

The description provides clear context for when to use this tool: when you need to know supported languages or verify specific language codes. However, it doesn't explicitly mention alternatives or exclusion criteria, so it stops short of 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?

No annotations are provided, so the description carries the full burden. It adds useful behavioral context (metric-compatible and system fallbacks, cross-platform rendering) but does not explicitly state side effects, error behavior, or that it is a pure read-only operation. Since the tool is a generator, the lack of side effects is implied, but transparency is only adequate, not exceptional.

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

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

The description is a single, well-structured sentence that conveys the core purpose and key output characteristics without wasted words. It is front-loaded and efficient, earning a top score for 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 generator with only 3 parameters and no output schema, the description covers the main behavior and return value implicitly. It explains what the generated font stack includes (metric-compatible, system fallbacks) and the goal (cross-platform rendering). It does not provide examples or error handling, but these are not critical for an agent to use the tool correctly.

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

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

Schema description coverage is 100%, so the parameters are already well-documented. The description mentions metric-compatible and system fallbacks, which relate to the boolean parameters, but does not add new meaning beyond that. It meets the baseline for schema-covered parameters.

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

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

The description clearly states the tool generates a CSS font-stack with metric-compatible and system fallbacks, using a specific verb ('Generate') and resource ('CSS font-stack'). It does not explicitly distinguish from sibling tools like generate_css, but the specificity makes its 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 implies when to use the tool: when a robust cross-platform font stack is needed, including fallbacks. It does not explicitly exclude alternatives or name sibling tools, but the context is clear enough for an agent to select it appropriately.

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

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

With no annotations, the description carries full behavioral disclosure burden. It only states the output type (CSS @import snippet) and the two modes. It does not disclose validation behavior, error handling, whether the output includes full CSS or just an @import line, or any other side effects. The description is too minimal to provide meaningful transparency.

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

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

The description is a single, front-loaded sentence that clearly states the core action and scope. Every word earns its place, with no filler 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?

The tool has 7 parameters and no output schema, but the description gives a clear overview of the two modes and the output type. However, it does not explain how parameters interact per mode (e.g., which params apply to single vs pairing) or provide any usage details beyond the basic generation. The schema covers param descriptions, but the tool's overall behavior is only partially contextualized.

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

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

Schema coverage is 100%, so the input schema fully documents all 7 parameters. The description adds context by mentioning 'single font or heading+body font pairing', which maps to the mode and heading_font/body_font parameters, but it does not elaborate on size, weights, or line_height beyond what the schema 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 uses a specific verb 'Generate' with a clear resource ('ready-to-use CSS @import snippet') and defines the two modes (single font or heading+body pairing). This distinguishes it from sibling tools like suggest_pairings or get_font_info, 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 implies usage ('Generate ready-to-use CSS @import snippet') but does not explicitly state when to prefer this tool over alternatives. No exclusions or prerequisites are mentioned, so the usage context is only hinted at through the tool's name and 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?

No annotations are provided, so the description carries the burden. 'Get' clearly indicates a read-only operation, which is a positive behavioral signal. The description does not mention pagination, errors, or response format, but it does enumerate the metadata fields returned, which is helpful. It falls short of richer context like auth requirements or edge cases.

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 immediately states the action and purpose. It lists specific metadata items without unnecessary filler, making it highly efficient.

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

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

Despite lacking an output schema, the description enumerates the expected fields (variants, subsets, tags, designer, year, URL), which covers return value expectations. For a single-parameter tool, this is sufficient context to understand its purpose and output, though it could mention error handling or example response.

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

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

The schema covers 100% of the parameter, including a clear description and example. The tool description does not add additional parameter semantics beyond saying 'specific web font', so it meets the baseline without exceeding 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 uses a specific verb 'Get detailed metadata' and clearly names the resource ('a specific web font') and contents ('variants, subsets, tags, designer, year, and Google Fonts URL'). This distinguishes it from siblings like list_fonts_by_category, which lists fonts by category, and font_stack, which deals with font stacks.

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 a specific web font' implies the tool is used when you already know the font and want its details. However, it does not explicitly mention alternatives or when not to use it, such as 'use list_fonts_by_category to discover fonts'.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It adds that the tool 'Returns 100+ fonts across serif, sans-serif, monospace, display, and handwriting categories,' providing useful context about output scope and category coverage. It does not detail pagination or side effects, but these are either obvious or covered by 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 two sentences, front-loaded with the action, and every word earns its place. It is concise and well-structured, 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 description is largely complete for a listing tool: it names the filters, output breadth, and categories. The schema covers parameters, and while there is no output schema, the description hints at the return scope. It could mention pagination defaults, but this is not critical given 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 mentions filters (category, tag, search) which are already in the schema, but it does not add extra meaning such as defaults or parameter relationships. It adds no semantic 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 verb 'List' with the resource 'available web fonts' and specifies filtering by category, tag, or search query. This distinguishes it from siblings like get_font_info or suggest_pairings, 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 implies the tool is for listing/filtering fonts, but it does not explicitly mention when to prefer this over alternatives (e.g., get_font_info for specific font details). There are no exclusions or alternative recommendations, leaving usage context only implied.

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

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

With no annotations, the description carries the burden. It adds value by specifying the outputs (CSS snippets, compatibility scores, curated rationale) and the design-expert sourcing, giving insight into expected behavior. It implies a read-only operation, which is adequate for a recommendation tool, though it doesn't explicitly state side-effect-free or permission requirements.

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

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

The description is a single, well-structured sentence that front-loads the main action and enumerates key outputs. It is highly concise with no filler, maximizing information density.

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

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

Despite no output schema, the description enumerates the core return components (CSS snippets, compatibility scores, rationale), which is sufficient for a simple suggestion tool. The parameters are fully documented in the schema, so completeness is adequate, though a bit more detail on the response structure would be beneficial.

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 all four parameters with descriptions (100% coverage). The description itself does not add meaning beyond the schema, so the baseline score of 3 applies. No additional parameter context is provided.

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

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

The description clearly states the tool's function: get font pairing recommendations with specific output types (CSS snippets, compatibility scores, rationale). This distinguishes it from siblings like generate_css (which generates CSS for a single font) and get_font_info (which returns font metadata).

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

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

The description implies usage when font pairings are needed but provides no explicit guidance on when to use this tool versus alternatives. There is no mention of exclusions or alternative tools for related tasks, leaving the agent to infer the context.

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