low-hallucination-vision

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detect_elements

Detect objects in an image and return labels, bounding boxes, and confidence scores. Uses conservative detection to reduce false claims, prioritizing accuracy in image analysis.

Instructions

Detect objects in an image with mandatory bounding boxes.

Conservative by design: prefers false negatives over false positives.

Args:
    image_source: Local file path or http(s) URL of the image.
    prompt: Optional extra instructions (e.g. "only people and vehicles").
    temperature: Sampling temperature, default 0.2.

Returns:
    JSON: {"objects":[{"label","bbox","confidence"}], "overall_confidence"}.

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": {
      "image_source": "<image_source>"
    },
    "name": "detect_elements"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	No
`temperature`	No
`image_source`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 7, 2026

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: it explicitly notes the precision/recall tradeoff ('prefers false negatives over false positives') and the mandatory bounding box output. This provides meaningful insight beyond the basic input/output schema.

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 well-structured with a one-sentence summary, a key behavioral note, and clearly labeled Args and Returns sections. Every sentence provides useful information without redundancy.

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

Completeness5/5

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

Despite having an output schema, the description provides a concrete example of the return JSON, and covers input, output, and behavioral policy. For a tool with 3 parameters and moderate complexity, this is complete and sufficient.

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

Parameters5/5

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

The input schema has 0% description coverage, but the description fully compensates by explaining all three parameters: image_source as a local path or URL, prompt as optional instructions with an example, and temperature with a default. This adds significant semantic meaning beyond the schema.

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 clearly states 'Detect objects in an image with mandatory bounding boxes,' providing a specific verb and resource. However, it does not explicitly differentiate from sibling tools like analyze_image or ocr_extract, so the purpose is clear but lacks sibling differentiation.

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 implies usage for object detection with a 'conservative by design' behavior, giving clear context on when to use the tool. It does not explicitly state exclusions or alternative tools, so it falls short of the highest rating.

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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