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zvec-ai

zvec-mcp-server

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by zvec-ai

embedding_write

Converts plain text documents into dense vector embeddings and upserts them into a Zvec collection for storage.

Instructions

Embed text documents and upsert them into a Zvec collection.

Converts each document's text field to a dense vector using OpenAIDenseEmbedding, then upserts all documents into the specified collection. This is the high-level write interface: supply plain text, get vectors stored automatically.

OpenAI connection is read from environment variables: OPENAI_API_KEY, OPENAI_BASE_URL (optional), OPENAI_EMBEDDING_MODEL (optional). The embedding dimension is inferred from the collection schema automatically.

Args: params (EmbeddingWriteInput): - collection_name: Target collection - field_name: Vector field to populate - documents: List of {id, text, fields} — text is auto-embedded

Returns: str: Success message with upsert count, or error

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description discloses the use of OpenAI for embedding, environment variables, and automatic dimension inference, adding context beyond the annotations. Annotations already indicate non-destructive (destructiveHint=false) and readOnlyHint=false, and the description does not contradict them. It could further mention potential overwrite on upsert.

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 concise (4 sentences), front-loaded with the main action, and includes an Args/Returns section. However, the Args section largely duplicates schema information, slightly reducing efficiency.

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?

Given the tool's complexity, the description covers core aspects: action, embedding mechanism, environment configuration, and return message. An output schema exists, reducing need for detailed return documentation. Leaves out error handling and performance considerations.

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?

The input schema's properties already have descriptions covering all parameters (collection_name, field_name, documents, and their sub-properties). The description adds marginal value by summarizing the OpenAI connection and dimension inference, but the schema provides the bulk of parameter semantics. Baseline 3 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?

The description uses specific verbs ('embed' and 'upsert') and identifies the resource ('Zvec collection'), clearly distinguishing it from sibling tools like 'generate_dense_embedding' which only creates embeddings, and 'upsert_documents' which likely does not auto-embed.

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

Usage Guidelines2/5

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

The description implies it is the high-level write interface but does not explicitly state when to use this tool versus alternatives like 'generate_dense_embedding' or 'insert_documents'. No when-not-to-use or exclusion criteria are provided.

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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