IRIS MCP Blueprint

import_csv_to_iris

Import CSV data into IRIS database by creating a table with column names from the CSV header, all stored as VARCHAR(255).

Instructions

Creates a table and imports data from a CSV string. The first row of the CSV must be the header (column names). All columns will be created as VARCHAR(255) for this blueprint. Args: ctx: The context of the tool call. table_name: The name of the table to import the data into. csv_content: The content of the CSV file to import. table_schema: The schema of the table to import the data into.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "table_name": "<table_name>",
      "csv_content": "<csv_content>"
    },
    "name": "import_csv_to_iris"
  }
}

Input Schema

Name	Required	Default
`table_name`	Yes
`csv_content`	Yes
`table_schema`	No	SQLUser

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.4Jun 12, 2026

TDQS

B3.3/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the column type limitation and header requirement, but does not mention error handling, behavior when table already exists, or concurrency issues.

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?

The description is concise, front-loaded with the main purpose, and avoids unnecessary words. The Args section is slightly redundant but acceptable.

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?

While an output schema exists (reducing need for return value description), the description lacks details on failure modes, table existence handling, and encoding. It provides basic but not comprehensive context.

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

Parameters2/5

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

Schema description coverage is 0%. The description merely repeats parameter names with vague definitions (e.g., 'The name of the table'). No format details for csv_content or clarification of table_schema default are provided.

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 creates a table and imports data from a CSV string. It distinguishes itself from siblings by specifying CSV import and column type limitation (VARCHAR(255)).

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like insert_data or export_table. No explicit context or exclusion criteria are given.

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