Get font pairing recommendations with CSS snippets, compatibility scores, and curated rationale from design experts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of pairing suggestions (1-10, default 5) | |
| style | No | Filter by style: modern, elegant, bold, minimal, classic, etc. | |
| font_id | Yes | Font slug or family name to find pairings for | |
| use_case | No | Filter by use case: editorial, blog, saas, tech, luxury, corporate, etc. |
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.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: listing fonts, fetching metadata, checking language support, generating CSS, creating font stacks, and suggesting pairings. There is no overlap in their core functionalities.
Most tools follow a verb_noun pattern (check_language_support, generate_css, get_font_info, list_fonts_by_category, suggest_pairings), but 'font_stack' is a noun phrase and slightly deviates from the pattern. Overall, names are consistent in style and readable.
With 6 tools, the server is well-scoped for font metadata and CSS generation. Each tool earns its place and the count is within the ideal 3-15 range.
The tool surface covers font discovery, detailed metadata, language support verification, CSS generation, and pairing recommendations. There are no obvious dead ends; the set is comprehensive for its stated purpose.