Skip to main content
Glama
Sign In
mcp-vision

mcp-vision

by MikeyBeez
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

vision_analyze_image

Analyze image files using Ollama vision models. Provide a prompt to describe or ask questions about the image, with model selection.

Instructions

Analyze an image file with Ollama vision model

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": {
      "path": "<path>"
    },
    "name": "vision_analyze_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesPath to the image file
modelNoVision model to usellava
promptNoWhat to analyzeDescribe this image

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations provided, the description bears the full burden of disclosing behavioral traits. It only states the action without revealing what the tool returns, whether it requires a local Ollama service, or any potential side effects. This leaves significant ambiguity for the agent.

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, focused sentence that immediately conveys the tool's purpose without any redundant information. It is perfectly concise and well-structured.

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?

The tool has no output schema, yet the description does not explain what the analysis returns. It also omits prerequisites like the need for a running Ollama instance. Given the moderate complexity and lack of output documentation, the description is incomplete.

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?

The input schema already provides comprehensive descriptions for all three parameters (path, model, prompt), resulting in 100% schema coverage. The tool description adds no additional parameter context beyond what the schema states, but the schema itself sufficiently explains each parameter.

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's function: 'Analyze an image file with Ollama vision model'. It names the action (analyze), the resource (image file), and the specific model type, making it distinct from sibling tools like vision_screenshot or vision_camera which capture images.

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 description implies usage for analyzing existing image files but does not explicitly state when to use this tool versus the capture-focused siblings. There are no exclusions or alternative tool recommendations, leaving the guidance at an implied level.

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

Deploy Server

Other Tools