Skip to main content
Glama
hedging8563

TokenLab MCP Server

by hedging8563

Create embeddings

create_embedding

Convert input text into an embedding vector using a specified model, with optional dimensions and encoding format.

Instructions

Create embeddings Creates an embedding vector representing the input text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
userNoEnd-user identifier
inputYesInput text(s) or token array(s) to embed.
modelYesID of the model to use
dimensionsNoOutput vector dimensions
encoding_formatNoOutput formatfloat
Behavior3/5

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

Annotations already indicate this is a non-read-only, non-destructive, non-idempotent operation. Description confirms it creates an embedding but adds no additional behavioral context like cost, storage, or side effects. Adequate but not enhanced.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is one sentence but begins with a redundant repetition of the title ('Create embeddings Creates...'). This wastes words without adding value. More concise phrasing would improve.

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?

No output schema is provided, and the description does not explain what is returned (e.g., vector, dimensions). Given the tool has 5 parameters and no return value documentation, the description is incomplete for an agent to fully understand behavior.

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 covers 100% of parameters with descriptions. Tool description adds no extra meaning beyond what's in the schema (e.g., just mentions 'input text'). Baseline score of 3 is appropriate.

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?

Description explicitly states 'Creates an embedding vector representing the input text,' clearly identifying the verb (creates) and resource (embedding vector). It distinguishes from sibling tools like create_multimodal_embedding by specifying 'text' input.

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?

Description implies usage for text embedding but provides no explicit guidance on when to use this tool versus alternatives such as create_multimodal_embedding or rerank_documents. No 'when to use' or 'when not to use' notes.

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/hedging8563/tokenlab-mcp-server'

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