ai-workers-mcp

ai_outline

Generate a hierarchical markdown outline from plain text using a free language model. Accepts plain prose and returns a structured outline.

Instructions

Create a hierarchical markdown outline from plain text 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": {
      "text": "<text>"
    },
    "name": "ai_outline"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	Plain text to outline

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Jun 14, 2026

TDQS

A4.2/5.0

Behavior3/5

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

No annotations are provided, so the description carries full burden. It mentions the use of a 'configured free LLM', which implies variability but does not detail potential costs, latency, or error handling. It sets input constraints but does not disclose behavior on invalid inputs.

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?

The description is two sentences with no fluff. The first sentence states the core purpose and method; the second sentence adds critical constraints. Every word earns its place, and the key information is front-loaded.

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 one parameter and no output schema, the description covers the purpose, input type, and constraints. It does not explicitly describe the output format (hierarchical markdown) but this is implied by the purpose. Slight gap in not mentioning that the outline is returned as markdown.

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

Parameters4/5

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

The schema has one parameter 'text' with a minimal description. The tool description adds value by specifying acceptable input ('Plain prose only') and prohibiting certain content, which is not in the schema. Baseline is 3 due to 100% schema coverage, but the extra guidance merits a 4.

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 specific action ('Create a hierarchical markdown outline'), the input type ('plain text'), and the method ('using a configured free LLM'). It differentiates from siblings like ai_bullet or ai_summarize by specifying the output format and input constraints.

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

Usage Guidelines4/5

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

The description explicitly says when to use this tool (for hierarchical outlines from plain text) and provides input restrictions ('Plain prose only — no code, secrets, or file paths'). However, it does not directly compare to sibling tools, leaving some ambiguity about when to choose this over alternatives.

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

Deploy Server