llmconveyors-mcp

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

Discloses input encoding (base64), hints at output structure (contact info, work experience, etc.), and auth requirements. However, lacks disclosure on side effects (does it persist the parsed resume or just return transient data?) and error handling given no annotations are present.

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 well-structured sentences with zero waste: core purpose first, technical details second, usage guidance third, alternative reference fourth, and auth requirement last. Each earns its place.

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?

Strong coverage given no output schema (describes return fields) and no annotations (mentions scope). Minor gap: doesn't clarify if parsing creates persistent storage or transient extraction only, which matters given the 'write' scope and mutation-sounding siblings.

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 has 100% description coverage (fileBase64, filename, contentType, mode all well-documented). Description reinforces base64 requirement and file types but adds minimal semantic value beyond the schema, which carries the full burden for parameter documentation.

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?

Excellent specificity with verb 'Parse', resource 'resume file', explicit formats (PDF/DOCX/TXT), output format (JSON Resume), and clear differentiation from sibling 'upload-resume' which handles combined upload+parse.

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?

Explicitly states when to use ('extract structured data from an existing resume file'), when to use alternative ('For uploading and parsing in one step, use upload-resume instead'), and prerequisite scope ('Requires scope: resume:write').

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