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CV Email MCP Server

CV Email MCP Server

by anmolvarshney77
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

ask_cv

Ask questions about your CV or resume content to retrieve specific details and clarify experiences, skills, or qualifications.

Instructions

Ask questions about your CV content

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "question": "<question>"
    },
    "name": "ask_cv"
  }
}

Input Schema

NameRequiredDescriptionDefault
questionYesQuestion about your CV/resume

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries full behavioral disclosure burden, but it only states 'Ask questions about your CV content.' It does not disclose what happens under the hood (e.g., whether it references a previously parsed CV, whether it has side effects, or what the response format is). This is minimal and leaves key behavior unspecified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence, front-loaded with the core purpose. It has no redundancy or wasted words, but it is so brief that it misses essential context, slightly reducing the conciseness score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with siblings and no annotations or output schema, the description is too incomplete. It does not explain how to use it in conjunction with parse_cv, what kind of answers it returns, or any prerequisites. The provided context signals (1 param, no output schema) indicate a simple tool, but the description still lacks necessary operational details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 100% of the parameter descriptions (the 'question' param is described). The description adds little beyond the schema, so the baseline of 3 applies for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Ask questions about your CV content' with a specific verb ('ask') and resource ('CV content'). It distinguishes itself from siblings parse_cv (parsing) and send_email (sending) by focusing on interactive Q&A.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool vs alternatives. It does not mention whether a CV must be parsed first, what types of questions are appropriate, or how it relates to parse_cv and send_email. The usage context is entirely implied.

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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