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embed_text

Embed up to 64 texts into 768-dimensional vectors for machine learning tasks. Pay per call in Kaspa.

Instructions

Embed up to 64 texts (768-dim vectors, nomic-embed-text). Paid (~$0.0003 in KAS).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.8/5.0
Behavior3/5

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

No annotations exist, so description carries the burden. It discloses paid cost (~$0.0003 in KAS) and a limit of 64 texts. However, it does not mention that the operation is stateless, read-only, or any side effects, which is adequate but not thorough.

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?

Single sentence with no fluff, covering key aspects: action, limit, model, dimensions, cost. Every word adds value.

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

Completeness4/5

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

For a single-parameter tool with no output schema, the description covers purpose, limit, model, and cost. Missing return format (e.g., 2D array of floats) but otherwise complete enough.

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?

With 0% schema description coverage, the description adds meaning by specifying the limit (up to 64 texts) and the output characteristics (768-dim, nomic-embed-text). It does not describe input constraints like text length or format.

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 specifies the verb 'embed' and resource 'texts', with details about dimensionality (768-dim) and model (nomic-embed-text). This distinguishes it from sibling tools like classify or generate.

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?

Implies use for embedding text with a limit of 64 texts and a cost, but no explicit when-to-use or alternatives among siblings. The paid nature is a usage hint but not comparative.

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