Skip to main content
Glama
dotlab-hq

@dotlab-hq/vector-store-mcp

by dotlab-hq

Upload File

openai_upload_file

Upload a local file to OpenAI for assistants, batch, fine-tune, vision, user_data, or evals, and get a file ID to attach to vector stores.

Instructions

Upload a local file to OpenAI.

The file is uploaded using multipart/form-data. Once uploaded, you can attach the returned file ID to a vector store using openai_attach_file_to_vector_store.

Supported purposes:

  • assistants: Used in the Assistants API

  • batch: Used in the Batch API

  • fine-tune: Used for fine-tuning

  • vision: Images for vision fine-tuning

  • user_data: Flexible file type for any purpose

  • evals: Used for eval data sets

Individual files can be up to 512 MB.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYesAbsolute or relative path to the local file to upload.
purposeYesThe intended purpose of the uploaded file.
Behavior4/5

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

Annotations provide basic hints (readOnlyHint=false, etc.), but the description adds behavioral context: multipart/form-data upload, 512 MB size limit, and the six supported purposes. No contradictions with 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 concise and well-structured with bullet points for purposes. Every sentence adds information without redundancy.

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

Completeness3/5

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

The description mentions 'returned file ID' but does not describe the full response format or return object. Given no output schema, more detail on the response would improve completeness.

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?

Schema already has 100% coverage with descriptions and enums. The description adds value by elaborating on each purpose (e.g., 'assistants: Used in the Assistants API') and clarifying that file_path is a local path.

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 'Upload a local file to OpenAI' with a specific verb and resource. It lists supported purposes and size limits, distinguishing it from sibling tools like 'openai_attach_file_to_vector_store' which attaches the returned file ID.

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 explains that after upload, the file ID can be attached to a vector store using a specific sibling tool. It does not explicitly state when not to use, but the context of purposes and the sibling list imply appropriate use cases.

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