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rosalinddb

@rosalinddb/mcp

by rosalinddb

ingest_vectors

Upsert vector records into a dataset by id, with support for metadata and synchronous or asynchronous indexing for eventual consistency.

Instructions

Ingest (upsert) vector records into a dataset (last-write-wins per id). Each record needs an id, a values array matching the dataset dimension, and optional flat metadata. The response reports accepted/rejected counts and per-record errors. Read-your-writes depends on the server's recall tier: if the result has NO job_id the write was synchronous and is immediately queryable; if it returns a job_id, indexing is asynchronous (eventually consistent) — poll get_dataset until status is 'indexed'. For dumps over ~10 MiB use the async import flow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations provided, so description carries full burden. It fully discloses synchronous vs asynchronous indexing, response structure (accepted/rejected counts, per-record errors), and polling mechanism via get_dataset.

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?

Concise with front-loaded main action, though slightly lengthy with detailed async behavior; each sentence adds value.

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

Completeness5/5

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

Comprehensively covers all necessary aspects for an ingestion tool with async behavior, including response details and integration with get_dataset for polling, despite lacking an output schema.

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?

Input schema has no defined properties (empty object with additionalProperties), but description adds essential parameter details like id, values array, and optional metadata, compensating for the empty schema.

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 the tool ingests (upserts) vector records into a dataset with last-write-wins per id, distinguishing it from siblings like create_dataset or delete_vector.

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?

Provides explicit guidance on when to use the async import flow for large payloads over 10 MiB and explains read-your-writes behavior based on recall tier, offering clear context without naming specific alternative tools.

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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