Skip to main content
Glama
dotlab-hq

@dotlab-hq/vector-store-mcp

by dotlab-hq

Create Vector Store

openai_create_vector_store

Create a new vector store to organize documents for semantic search and RAG. Attach files, set expiration, and configure chunking strategy.

Instructions

Create a new OpenAI vector store — a collection of processed files that can be used with the file_search tool.

Use this when you need a new vector store to hold uploaded documents for semantic search or retrieval-augmented generation (RAG).

You can optionally provide file_ids to attach already-uploaded files, a chunking strategy, and metadata (up to 16 key-value pairs).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name of the vector store.
file_idsNoA list of File IDs that the vector store should use. Useful for tools like `file_search`.
expires_afterNoThe expiration policy for the vector store.
metadataNoSet of 16 key-value pairs attached to the object. Keys max 64 chars, values max 512 chars.
chunking_strategyNoChunking strategy for files. Default is auto (800 tokens, 400 overlap).
Behavior4/5

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

Annotations already indicate readOnlyHint=false, destructiveHint=false, idempotentHint=false, and openWorldHint=true. The description adds that it creates a collection for file_search and lists optional parameters, which is consistent and adds minor behavioral context beyond the annotations.

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?

The description is extremely concise: three short sentences. The first defines the tool, the second gives direct usage guidance, the third lists key optional parameters. No wasted words or fluff.

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 (5 parameters, nested objects, no output schema), the description is adequately complete. It explains the resource's purpose, usage context, and key optional features. It could mention that creation returns a vector store object, but since the output schema is missing, the description suffices.

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

Parameters4/5

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

The input schema covers all 5 parameters with descriptions (100% coverage). The description adds value by summarizing optional parameters and noting the metadata limit of 16 key-value pairs, which aligns with the schema. This enhances understanding without redundancy.

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 states 'Create a new OpenAI vector store' with specific verb and resource, and defines it as a collection for file_search. It distinguishes from siblings (e.g., delete, update) by its creation purpose.

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

Usage Guidelines4/5

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

The description explicitly says 'Use this when you need a new vector store,' providing clear context for usage. It does not explicitly list when not to use or alternatives, but the context is sufficient given sibling tool names.

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/dotlab-hq/vector-store-mcp'

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