get_fontGet full metadata for one font family by slug: styles, weights, axes, license, subsets and download/CSS URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Family slug, e.g. 'inter', 'playfair-display'. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly and idempotent; the description adds value by enumerating the returned metadata fields (styles, weights, axes, license, subsets, download/CSS URLs), giving agents concrete expectations of the response. It does not cover error behavior, but the annotations satisfy 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?
One concise sentence that leads with the verb and resource, then enumerates contents efficiently. 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?
Given the tool's simplicity (one param), strong annotations, and a description that explicitly lists the return value categories, the description is complete for an agent to successfully invoke and interpret results. No output schema exists, but the description compensates adequately.
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 'slug' parameter is fully described in the schema with an example. The description reinforces 'by slug' but adds no additional syntax, constraints, or formatting details beyond what the schema already 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 retrieves full metadata for a single font family by slug, listing specific content areas (styles, weights, axes, license, subsets, URLs). This distinguishes it from sibling tools like get_font_files or get_font_download_url, which target narrower subsets.
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: you need a font slug and want comprehensive metadata. However, it does not explicitly mention when to prefer this over search_fonts or when to use the more specific sibling tools, leaving some ambiguity.
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 targets a distinct operation within the color, font, or code utility domains. Descriptions clearly differentiate retrieval, search, and action tools, so an agent can reliably select the right one without ambiguity.
All names use snake_case and are readable, but there's a mix of direct verb_noun patterns (check_contrast, extract_colors) and a get_ prefix for retrieval tools (get_font, get_palette). The inconsistency is minor and the pattern remains predictable overall.
15 tools is at the upper end of the well-scoped range, but each tool serves a clear, distinct purpose across three focused domains (fonts, colors, code). No tool feels redundant or unnecessary for the server's mission.
The server provides a comprehensive lifecycle for its domains: font search, metadata, file access, CSS generation, and image recognition; color extraction, naming, shading, contrast checking, and palette search; and code detection, conversion, and minification. No obvious gaps exist.