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ai-workers-mcp

ai-workers-mcp

by basttran
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

ai_email_draft

Converts bullet points into a polished email body using a free language model. Allows selecting tone such as professional, friendly, or assertive.

Instructions

Draft an email body from bullet points using a configured free LLM. Plain prose only — no code, secrets, or file paths.

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": {
      "bullets": "<bullets>"
    },
    "name": "ai_email_draft"
  }
}

Input Schema

NameRequiredDescriptionDefault
bulletsYesBullet points as a string or array of strings
toneNoEmail tone, e.g. 'professional', 'friendly', 'assertive'. Defaults to 'professional'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations provided. Description adds context about using a 'configured free LLM' and output constraints ('plain prose'), but does not disclose behavior like rate limits, auth needs, or error handling.

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

Conciseness5/5

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

Two sentences with no redundancy. Front-loaded with the core action and resource.

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

Completeness4/5

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

For a simple tool with 2 params and no output schema, the description covers the purpose, input, and key constraint. Might miss return format (e.g., whether it returns plain body or includes subject), but is generally adequate.

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?

Schema coverage is 100% and both parameters are described in the schema. The description does not add significant meaning beyond what the schema provides, just reiterates the prose constraint.

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?

Description clearly states the tool drafts an email body from bullet points, with a specific verb ('draft') and resource ('email body'). It distinguishes from sibling tools by focusing on email drafting.

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

Usage Guidelines3/5

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

Provides a constraint ('Plain prose only — no code, secrets, or file paths') but lacks explicit when-to-use or when-not-to-use compared to alternatives like ai_generate or ai_summarize.

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

Deploy Server

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