Skip to main content
Glama
ozwei

LM Studio MCP Bridge

by ozwei

analyze_local_image_async

Start asynchronous analysis of a local image using a prompt, returning a Task ID for progress tracking.

Instructions

Async Vision: Start a background image analysis task. Returns a Task ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
promptYes
image_pathYes
Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It discloses that the operation is asynchronous and returns a Task ID, but it lacks details about task progression, result retrieval, or prerequisites. This is insufficient for an async tool.

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, conveying the essential purpose and return value in a single sentence. It is front-loaded and contains no redundant information.

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 is an async operation with no output schema, and the description only mentions the Task ID. It fails to explain how to retrieve the result or tie into the available `get_bridge_task_status` tool, leaving a significant contextual gap.

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?

The schema has 0% description coverage, so the description must compensate. However, it does not explain any of the parameters (`image_path`, `prompt`, `model`). The property names are self-explanatory, but the description adds no additional meaning or constraints.

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 starts a background image analysis task and returns a Task ID. The 'Async' in the name and 'background' in the description distinguish it from the synchronous sibling `analyze_local_image`. It uses a specific verb ('start') and resource ('image analysis').

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 non-blocking image analysis, but it does not explicitly state when to use this over the synchronous `analyze_local_image` or how to check task status. No alternatives or exclusions are mentioned.

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/ozwei/lmstudio-mcp-bridge'

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