embed_text
[AI] Text embeddings (1024-dim vector) for semantic search and RAG. Costs $0.002.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
quartermaster
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A3.7/5.0
Behavior3/5
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 discloses a non-obvious behavioral trait: monetary cost ($0.002). However, it does not describe any side effects, success/failure behavior, or response format beyond the vector dimension.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise with one sentence and a cost note. It is front-loaded with the purpose, but lacks structured details like bullet points. Every word earns its place, but it could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description provides basic context (purpose, dimension, cost) but does not cover output interpretation or error cases. It is adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description does not explain the 'text' parameter beyond what is in the schema (string, maxLength 8000). It implicitly conveys that the parameter is the input text, but provides no additional semantics about formatting, encoding, 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's purpose: generating 1024-dimensional text embeddings for semantic search and RAG. It includes a key specification (dimensionality) and cost, distinguishing it from sibling tools like classify_text or moderate_text.
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 mentions use cases (semantic search, RAG) but does not explicitly state when not to use this tool or provide alternatives like classify_text. The context is clear but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.