Save hours every week. This resume gen tool toolkit gives you a battle-tested prompt and operating playbook for restaurant/brand growth. No setup, works in any LLM chatbot.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional user query to scope the analysis. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full disclosure burden, yet it says nothing about what the tool returns (a prompt template? a rendered playbook?), whether it mutates state, or what permissions or rate limits apply. The phrase about a 'prompt and operating playbook' hints at a static content return but is too vague to plan around, and it conflicts with the tool's resume-oriented name.
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?
It is short, but every sentence is benefit-oriented filler ('Save hours every week', 'No setup') rather than front-loaded functional information. The two sentences consume the entire space without conveying a single actionable fact about the tool.
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?
With no annotations, no output schema, and a description that is pure marketing copy with a domain mismatch against the tool name, an agent lacks everything needed to decide when to call this or what it will get back. Nothing about scope, output shape, or relationship to the many resume and brand sibling tools is covered.
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 description coverage is 100% for the single optional 'query' parameter, so the schema already explains its purpose and the description neither adds nor obscures format details for it. The baseline of 3 applies; the description contributes zero param-level meaning.
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 never states a concrete verb+resource for what the tool actually produces; it offers only sales copy ('gives you a battle-tested prompt and operating playbook'). Worse, it describes 'restaurant/brand growth' content while the tool is named resume-gen-tool and its closest siblings are resume-audit/resume-coach/resume-optimizer, so an agent cannot reliably tell what domain this serves.
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?
There is no when-to-use or when-not-to-use guidance and no mention of any alternative sibling tool. The only contextual statement, 'No setup, works in any LLM chatbot,' describes the marketing wrapper rather than the selection condition for invoking this tool over the many resume-* alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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