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Revise MCP

Revise MCP

by davidfstr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

outline_file

Retrieve a high-level outline of a file to understand its structure, showing only class and function definitions and section separators.

Instructions

Returns a high-level outline of a file, similar to VS Code's folded view.

Shows only structurally significant lines:

  • class and def definitions (at any indentation level)

  • Section separator comments (# === ... or # --- ... prefix, at any indentation)

  • Section name comments that immediately follow a pure separator line

Output format: "LINE_NUMBER:LINE_CONTENT" (1-indexed), one per line. Useful for understanding the structure of a large file before reading it in full.

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": {
      "filePath": "<filePath>"
    },
    "name": "outline_file"
  }
}

Input Schema

NameRequiredDescriptionDefault
filePathYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A4.1/5.0
Behavior4/5

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

No annotations provided, so the description carries full burden. It discloses the output format (LINE_NUMBER:LINE_CONTENT, 1-indexed) and the specific selection criteria for lines. It does not mention destructive actions or auth needs, but as a read-only tool, that's acceptable.

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 concise and well-structured. First sentence states purpose, then lists what is shown, then output format, then use case. No extraneous information.

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 annotations, the description covers the output comprehensively. It explains selection criteria and format. It does not mention limitations or error handling, but it's still fairly complete for its complexity.

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

Parameters2/5

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

Schema description coverage is 0%, and the description does not mention the 'filePath' parameter. While the parameter name is self-explanatory, the description adds no additional meaning or constraints (e.g., expected format, restrictions) beyond the schema's type declaration.

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 returns a high-level outline of a file, similar to VS Code's folded view. It specifies what structural lines are included (class/def, separator comments). The purpose is distinct from sibling tools (indent_dedent, move_string_in_file, rename_symbol).

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 says it's 'useful for understanding the structure of a large file before reading it in full,' which implies when to use. It doesn't explicitly say when not to use, but the purpose is clear enough that an AI agent can infer appropriate usage contexts.

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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