Skip to main content
Glama
bpamiri

u2-mcp

by bpamiri

export_to_csv

Export records from a Universe file to CSV format for spreadsheet import and data analysis. Supports expanding multivalues into separate rows or joining with a delimiter.

Instructions

Export records from a Universe file to CSV format.

Reads the specified records and returns them as a CSV string. Useful for spreadsheet import and data analysis.

Args: file_name: Name of the Universe file record_ids: List of record IDs to export expand_mv: If True, expand multivalues into separate rows. If False (default), join multivalues with delimiter. mv_delimiter: Delimiter for multivalues when expand_mv=False. Default is "|".

Returns: Dictionary containing: - status: success or error - format: "csv" - record_count: Number of records exported - row_count: Number of rows in CSV (may differ if expand_mv=True) - data: CSV string of the exported records

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
expand_mvNo
file_nameYes
record_idsYes
mv_delimiterNo|

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so the description carries full burden. It explains the tool reads records and returns CSV, details multivalue expansion behavior, and implies it is read-only. No destructive actions mentioned, which is appropriate for an export.

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 well-structured with a concise purpose statement, usage hint, then formatted Args and Returns sections. Every sentence adds value and is front-loaded.

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?

Given the tool's complexity (4 parameters, output schema exists), the description fully covers the behavior, including multivalue handling and return format. It explains output fields despite having an output schema.

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

Parameters5/5

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

Schema description coverage is 0%, but the description includes an explicit Args section that thoroughly explains each of the four parameters: file_name, record_ids, expand_mv, and mv_delimiter, including defaults and behavior.

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 exports records from a Universe file to CSV format, specifying the verb 'Export', the resource 'records from a Universe file', and the output format CSV. It distinguishes from sibling tools like export_to_json.

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 a usage hint: 'Useful for spreadsheet import and data analysis.' This gives context but does not explicitly state when not to use or compare with alternatives like export_to_json.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/bpamiri/u2-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server