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rawtherapee-mcp-server

rawtherapee-mcp-server

by lucamarien
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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preview_exposure_bracket

Simulate exposure bracketing by rendering multiple EV previews to determine optimal exposure before committing to a full-resolution render.

Instructions

Simulate exposure bracketing by rendering multiple EV previews.

Generates preview images at different exposure compensation values. Useful for determining the optimal exposure before committing to a full-resolution render. Params: file_path, profile_path, stops, max_width

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": {
      "file_path": "<file_path>"
    },
    "name": "preview_exposure_bracket"
  }
}

Input Schema

NameRequiredDescriptionDefault
file_pathYes
profile_pathNo
stopsNo
max_widthNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.4

TDQS

A3.6/5.0
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 discloses that the tool generates preview images at different EV values and lists parameters. However, it lacks details on side effects (likely none), permissions, or what happens to existing data. The simulation behavior is clear but not exhaustive.

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 three sentences long and front-loaded with the main action. The parameter list at the end is somewhat redundant but not excessive. It is concise and structured well, though it could omit the parameter listing to avoid redundancy.

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?

Given no output schema or annotations, the description should explain return format and behavior. It mentions generating preview images but does not specify how they are returned (e.g., base64, URLs). It also omits details on how 'stops' affects output. This leaves gaps for an AI agent.

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?

The input schema has 0% description coverage, and the description only lists parameter names without explaining their semantics. For example, 'stops' as an array of numbers is ambiguous, and 'profile_path' and 'max_width' lack context. The description adds minimal value beyond the schema.

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 simulates exposure bracketing by rendering multiple EV previews, using specific verbs (simulate, render) and resource (EV previews). It effectively distinguishes itself from sibling preview tools like preview_raw or preview_white_balance by focusing on exposure bracketing.

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 explains it is useful for determining optimal exposure before a full-resolution render, providing clear context. While it does not explicitly list when not to use or alternatives, the purpose alone guides appropriate usage among siblings.

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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