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infino-ai

Infino MCP server

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Add documents to an Infino table

infino_add_documents

Add document rows to a table in one commit; missing vectors are automatically generated from text via a local embedding model.

Instructions

Append documents (rows, as JSON objects keyed by column name) to a table; one call is one commit. If the table has a vector index and a document omits the vector, the server embeds its text column (a local model, no API key). Send tens of rows per call; for a whole corpus use the infino CLI or an SDK.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYesTable to append to.
documentsYesRows to append, as JSON objects keyed by column name.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.14.0

TDQS

A4.4/5.0
Behavior4/5

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

The description adds useful behavioral context beyond the annotations: 'one call is one commit' and the vector-index embedding fallback with a local model and no API key. It does not cover response format or failure behavior, but the most important behavioral traits are disclosed.

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 three sentences with no filler. The main action and data format are front-loaded, followed by the commit/embedding behavior and batching alternative. Every sentence earns its place.

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?

For a two-parameter write tool, the description covers the key invocation details: required parameters, document format, batch sizing, commit semantics, and vector embedding behavior. It omits output/response expectations, but since there is no output schema and this is an append operation, that is a minor gap.

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 coverage is 100%, so the baseline is 3. The description adds meaningful extra semantics by explaining that omitting the vector field triggers server-side embedding from the text column, and by advising row counts per call. This goes beyond the schema's simple 'Rows to append' phrasing.

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 states a specific action ('Append documents'), a clear resource ('to a table'), and the data shape ('rows, as JSON objects keyed by column name'). This clearly distinguishes the tool from the update/delete siblings by framing the operation as append-only.

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?

It gives explicit batching guidance ('Send tens of rows per call') and an explicit alternative for larger volumes ('for a whole corpus use the infino CLI or an SDK'). It does not explicitly contrast with infino_update_documents or infino_delete_documents, but 'append' makes that distinction reasonably clear.

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