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Convert up to 3,000 characters of input text into a numerical vector for semantic search, clustering, or retrieval; each paid call costs $0.01 USDC.

Instructions

Compute a text embedding vector. Costs $0.01 (USDC on Base) per call. Spends real money; subject to a per-payment and per-session cap. Pass the text in input (max 3000 chars).

Input Schema

NameRequiredDescriptionDefault
inputYesThe text to process, passed as "input": the text to embed or summarize, or for ask, the question. 1-3000 characters.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

The description adds real behavioral context beyond the annotations: monetary cost ($0.01 USDC on Base), that it spends real money, and that a per-payment and per-session cap applies. What it doesn't cover is what happens when a cap is hit or a payment fails, which leaves a meaningful edge case undisclosed for a paid write-flagged tool.

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?

Three short sentences with the purpose and the cost front-loaded ahead of the input note — efficient and easy to scan. The trailing "(max 3000 chars)" partially duplicates the schema's maxLength, which is the only wasted material.

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 one-parameter tool with annotations covering side-effect profile and with cost/payment constraints spelled out, the description covers what an agent needs to decide and call safely. The notable omission is the shape of the return value (e.g., vector dimensionality), which matters here since there is no output schema.

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%, so the single `input` parameter is already fully documented with its 1-3000 character bounds. The description restates the 3000-character limit but adds no new semantics, so the baseline 3 applies.

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?

"Compute a text embedding vector" is a specific verb plus resource that is clearly distinct from summarize, ask, health, and inspect. It doesn't explicitly name what differentiates it from the sibling summarize/ask tools, but the resource noun alone makes the distinction obvious.

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?

Usage is only implied: an agent infers it should call this when it needs an embedding, and the stated $0.01 price plus spending caps give a decision input about whether the call is worth it. There is no explicit when-to-use versus summarize/ask guidance and no stated preconditions or alternatives.

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

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