detect_codeDetect the language/format of a code snippet. Static analysis only — the snippet is never executed and never stored.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Snippet to inspect. Max 200,000 chars. |
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 readOnlyHint=true and idempotentHint=true, but the description adds critical context beyond that: 'Static analysis only — the snippet is never executed and never stored.' This discloses privacy and execution guarantees, which is valuable behavioral transparency not present in the structured data.
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 that are front-loaded with the tool's purpose, followed by a crucial safety note. Every word earns its place, with no padding 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 tool, the description covers purpose, input (code snippet), size limit via schema, and behavioral constraints. It omits explicit mention of the return format (the detected language/format), but that is implied by the name and description. Given no output schema, a brief note on return could improve completeness, but current info is largely 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 already provides a description for the single 'code' parameter, and schema coverage is 100%. The tool description does not add additional parameter semantics beyond what's in 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 'Detect' with a clear resource 'the language/format of a code snippet,' making the tool's function immediately obvious. It also distinguishes itself from sibling tools like convert_code and minify_code, which focus on transformation rather than detection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies the primary use case: identifying the language/format of a code snippet. It does not explicitly mention when not to use it or name alternatives, but the distinction from siblings (e.g., convert_code) is implicit. Context is clear without formal exclusions.
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.