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pietrodileo

IRIS MCP Blueprint

by pietrodileo

export_table

Export rows from an IRIS table as JSON, CSV, or pipe-separated TXT. Narrow results using columns, where clause, and limit to manage response size.

Instructions

Export rows from an existing IRIS table as JSON, CSV, or TXT (pipe-separated).

The result is returned as a single string the caller can preview, copy to a file, or stream to a downstream client. For large tables narrow the result with columns, where, and/or limit to keep responses manageable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_nameYesBare table name (letters, digits, underscore; no schema, no dot).
table_schemaNoSchema of the table (default 'SQLUser').SQLUser
formatNoOutput format — 'json' (list of objects), 'csv' (RFC 4180, comma delimiter, CRLF line terminator), or 'txt' (pipe-separated columns + ruler, same look as other tools' output). Case-insensitive.json
columnsNoOptional list of column names to export. None or empty = all columns.
whereNoOptional SQL fragment placed after WHERE (do **not** include the 'WHERE' keyword). Example: "Age > 30 AND City = 'Rome'". Caller is responsible for escaping; consider parameterized fetch_data for untrusted input.
limitNoMaximum number of rows to return. Pass 0 or a negative value to disable the cap (use only for known-small tables).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so the description carries the full burden. It describes the output as a single string for preview or streaming, and recommends narrowing for large tables. It does not explicitly state that the operation is read-only, but that is implied by 'export'. Minor gap: no mention of idempotency or side effects.

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 two short paragraphs, front-loaded with the main action, and every sentence adds value. No redundancy or extraneous information.

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?

The tool has an output schema (not shown), and the description covers purpose, formats, and output type. It could mention how empty results or errors are handled, but overall it is complete for a simple export tool.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. The description reinforces the use of columns, where, and limit for narrowing, but does not add significant new semantics beyond the 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 action (export), the resource (IRIS table), and the output formats (JSON, CSV, TXT). It distinguishes from siblings like fetch_data by specifying the format options and return type.

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 advises narrowing results for large tables using columns, where, and limit, and warns against using limit 0 for unknown-large tables. However, it does not explicitly compare to sibling tools like fetch_data to guide selection.

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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