Skip to main content
Glama

Analyze Image (VLM)

sdnext_analyze

Analyze images using a vision-language model to generate descriptions, answer questions, and extract visual details via the SD.Next /analyze endpoint.

Instructions

Analyze an image with the VLM "analyze" task (SD.Next /sdapi/v1/analyze). Same request shape as sdnext_vqa.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYesBase64-encoded image (raw base64, data: URL, or "upload:<id>" ref).
modelNo
top_kNo
top_pNo
promptNo
systemNo
prefillNo
questionNo
do_sampleNo
num_beamsNo
max_tokensNo
temperatureNo
keep_prefillNo
keep_thinkingNo
thinking_modeNo
include_annotatedNo
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It only mentions the endpoint and request shape but does not disclose safety, side effects, or output behavior. For an image analysis tool, it doesn't clarify whether it's read-only or if there are any limitations.

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 extremely concise at two sentences, front-loaded with the action and endpoint. Every word earns its place, with no fluff or repetition.

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

Completeness1/5

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

Given 16 parameters, no output schema, and minimal schema coverage, the description is inadequate. It does not explain the meaning of most parameters, expected return values, or any constraints. The reference to sdnext_vqa is insufficient for an agent to correctly invoke this tool.

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

Parameters1/5

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

Schema description coverage is only 6% (only 'image' is described). The description adds no parameter explanations and only references that the request shape is same as sdnext_vqa, which is indirect. With 16 parameters and zero enum guidance, the description fails to compensate for the lack of schema documentation.

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 analyzes an image using the VLM 'analyze' task, names the specific endpoint (/sdapi/v1/analyze), and distinguishes it from siblings by noting the request shape matches sdnext_vqa. This is a specific verb+resource+task statement.

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?

It mentions 'Same request shape as sdnext_vqa,' which hints at similarity and implies that sdnext_vqa might be used for VQA tasks while this is for analysis. However, there is no explicit statement of when to use this vs alternatives or when not to use it.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Congenital/sdnext_mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server