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Bring records in from a file

import_records

Create records from attached CSV, vCard, or mbox files, matching columns to fields and linking or creating people by email, phone, or name. Use to turn file contents into records; returns count.

Instructions

Make records from a file the person added: a CSV with a header row, a vCard (.vcf) of contacts, or a mailbox (.mbox) of mail. Each column is matched to a field by name; a column for an email, phone or name links each row to its person, made when new. Use it when the person attaches such a file and wants its contents as records, rather than creating them one by one. Returns how many were made.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileYesThe id of the file record, from the message it came with or from find_records on file.
typeYesA content type, as the prompt lists it.
mappingNoOptional: which column feeds which field, as {column: field}. Leave out to match by name.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare the mutation profile (readOnlyHint=false, idempotentHint=false, destructiveHint=false). The description adds real side-effect context beyond that: persons are created when new during linking, column-to-field matching happens by name, and it returns a count. It does not warn about duplicate rows or error handling, which idempotentHint=false would make relevant.

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?

Three tight sentences, front-loaded with the action and file types, then matching/linking behavior, then the usage trigger. No filler.

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 mutation import tool with a nested mapping param and no output schema, the description covers sources, matching, person side effects, and return value. It leaves out failure/duplicate-row behavior, but is otherwise complete against the annotations.

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 baseline is 3, but the description adds meaning by explaining the column-to-field 'match by name' behavior and the linking semantics of email/phone/name columns, which the schema's terse mapping note does not fully convey.

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?

States a specific verb and resource ('make records from a file') and enumerates the supported sources (CSV, vCard, mbox). It distinguishes itself from create_record by framing the operation as bulk import rather than one-by-one creation.

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

Usage Guidelines5/5

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

Explicitly says when to use it ('when the person attaches such a file and wants its contents as records') and names the alternative it replaces ('rather than creating them one by one'), which routes the agent away from create_record.

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