llmconveyors-mcp

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

No annotations provided, so description carries full burden. Discloses critical behavioral traits: return format ('HTML string directly (not a URL)'), authorization requirement ('Requires scope: resume:write'), and persistence model (no file generation). Minor gap on rate limits or size constraints.

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?

Five statements, each high-value: purpose, output format, ideal use case, alternative tool, and permission requirement. No redundancy with structured schema fields. Efficiently 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?

Excellent coverage given complexity: explains return value despite missing output schema, names required OAuth scope, and maps to sibling workflow. Sufficient for a 2-parameter transformation tool with complete schema coverage.

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%, establishing a baseline of 3. Description mentions 'JSON Resume object' and 'specified theme' which aligns with but does not substantially augment the schema's self-documentation of these two 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?

Specific verb+resource combination ('Preview a JSON Resume object as inline HTML') clearly states the transformation. Explicitly distinguishes from sibling 'resume-render' via contrast ('without generating a downloadable file' vs 'For producing a downloadable...').

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 when-to-use ('quick visual previews without generating a downloadable file') and names the exact alternative sibling tool ('use resume-render instead') for the contrasting use case.

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