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imageSearchText

Search for images in a dataset using text queries to find matching visual content based on natural language descriptions.

Instructions

根据输入的文本内容,从数据集中检索出与输入的文本内容相符的图片

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes检索的文本

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states this is a retrieval/search operation but doesn't disclose important behavioral traits: whether this is read-only or has side effects, what dataset is being searched, authentication requirements, rate limits, pagination behavior, or what happens when no matches are found. The description is minimal and lacks operational context.

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?

Extremely concise single sentence that directly states the tool's function. No wasted words or unnecessary elaboration. The description is appropriately sized for a simple search tool with one parameter.

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?

For a search tool with no annotations and no output schema, the description is insufficient. It doesn't explain what kind of dataset is being searched, the format of returned images, error conditions, or limitations. Given the complexity of image search and lack of structured metadata, more context is needed for effective agent use.

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?

Schema description coverage is 100% with one parameter 'text' described as '检索的文本' (search text). The description adds minimal value beyond the schema, only reinforcing that the input is '文本内容' (text content). With high schema coverage, the baseline is 3 even without additional parameter semantics in the description.

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 the tool's purpose: '从数据集中检索出与输入的文本内容相符的图片' (retrieve images matching input text from a dataset). It specifies the verb '检索' (retrieve/search) and resource '图片' (images), but doesn't distinguish it from sibling 'imageSearchPic' which appears to be a similar image search tool with different input type.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. The description doesn't mention when this tool is appropriate versus 'imageSearchPic' or other search/retrieval tools in the sibling list. It provides only the basic functionality without context about appropriate use cases.

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