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Generate Outline

generate_outline

Create a structured blog outline from research sources and images. Specify the number of sections and writing style, then save the outline for content generation.

Instructions

Generate blog outline - Claude Desktop will create the outline based on research

This tool provides the research context and instructions. Claude (you) should:

Review the research sources and images
Create a comprehensive outline in JSON format
Call save_outline() with the generated outline

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": {
      "session_id": "<session_id>"
    },
    "name": "generate_outline"
  }
}

Input Schema

Name	Required	Description	Default
`style`	No	Writing style (technical, storytelling, how-to, listicle, comparative)	technical
`session_id`	Yes	Active session ID
`num_sections`	No	Number of main sections (3-7)

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 7, 2026

TDQS

B3.4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It clearly states that the agent (not the tool) must review research, create an outline, and call save_outline() – making the tool's role as a context provider transparent. It also hints at the need for research sources and images. However, it does not mention potential side effects beyond saving, nor does it address error cases or state changes, leaving some ambiguity.

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 well-structured with a clear list of steps, and the primary action is front-loaded. It is moderately concise, though the opening phrase 'Claude Desktop will create the outline' is somewhat redundant with 'Generate blog outline'. Overall, it is efficient and easy to parse.

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

Completeness3/5

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

Given the tool's role in a multi-step workflow with 23 siblings, the description is incomplete. It does not specify when in the process this should be called (e.g., after research approval), nor does it mention that the outline must be approved later. It also does not explain what the tool returns (though an output schema exists, which mitigates that). The description covers the agent's immediate task but lacks broader workflow context.

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 description coverage is 100%, so each parameter (style, session_id, num_sections) is already documented in the input schema. The description adds no extra meaning to these parameters – it does not explain their interplay or provide examples. According to the calibration, with high schema coverage, a baseline of 3 is appropriate, and the description does not exceed that.

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

Purpose4/5

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

The description states a clear purpose: 'Generate blog outline' with a verb and resource. It also clarifies that the tool provides context and instructions for the agent to create the outline, distinguishing it from a simple generator. However, it does not explicitly differentiate from sibling tools like modify_outline or generate_full_content, so it misses that opportunity for disambiguation.

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?

The description does not provide guidance on when to use this tool versus alternatives like modify_outline or approve_outline. It implies a workflow by instructing to call save_outline() afterwards, but does not state prerequisites (e.g., research must be approved) or conditions that would make this tool the right choice. This is a significant gap given the rich sibling set.

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