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resolve_detect_in_frame

Detect objects in the current DaVinci Resolve frame by sending an object description to Moondream, returning bounding box coordinates.

Instructions

Send the current frame to Moondream to detect described objects and return bounding boxes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
object_descriptionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.2.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that the frame is sent to an external service (Moondream) and that the output is bounding boxes, which is useful. Yet it does not mention side effects, latency, failure modes, or whether the operation is strictly read-only, so transparency is only partially adequate.

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 a single front-loaded sentence that packs the action, target, method, and output without any filler. Every word contributes value, making it highly concise and easy to parse.

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?

The tool is simple (one parameter) and has an output schema, so the description does not need to explain return values. It conveys the core invocation context—acting on the current frame and using a description of objects—sufficiently for an agent to call it correctly. Minor gaps like prerequisite state or error handling are acceptable given the schema richness.

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 provides only a type and title for object_description with 0% description coverage. The tool description compensates by stating that the tool detects 'described objects', clearly linking the parameter to the natural-language description of what to detect. This gives the agent the essential meaning, though it lacks examples or input-format details.

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 specific action ('Send the current frame to Moondream') and a specific result ('return bounding boxes') for detecting described objects. It is clear about the tool's purpose but does not explicitly contrast it with siblings like resolve_describe_frame or resolve_ask_about_frame, so it falls short of full differentiation.

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?

The wording implies usage: call this when you want to detect objects in the current frame based on a description. However, there is no explicit guidance on when to prefer this over other vision tools or any exclusions, leaving the agent to infer the appropriate context.

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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