Embed text with Forge

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

No annotations provided, so description carries full burden. It discloses embedding behavior, model options, dimension truncation (Matryoshka, re-normalized), and input_type prefix. Could mention rate limits or error handling, but adequate for typical use.

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

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

Description is a single paragraph, detailed but not overly long. Front-loaded with main action. Could be improved with bullet points for readability, but still concise.

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

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

Given presence of output schema (context indicates true), description adequately covers purpose, parameters, and usage. Might miss batch limits, but overall complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds extra meaning: model quality/cost ranking, Matryoshka truncation explanation, and retrieval prefix for input_type. Adds significant value beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Generate vector embeddings for one or more texts with Forge', specifies use cases (semantic search, RAG, clustering, similarity), and distinguishes from sibling 'list_models'.

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

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

Provides clear guidance on when to use (various NLP tasks) and specific instructions for input_type ('query' vs 'document') and model selection with quality/cost trade-offs. Missing explicit when-not-to-use.

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