Skip to main content
Glama

export_rows

Export filtered or projected dataset rows to a CSV or JSON file for spreadsheet-friendly data retrieval and sharing.

Instructions

Export rows (optionally filtered and projected) to a CSV or JSON file.

Use this when the user wants their data back in a spreadsheet-friendly form. Filters use the same conditions syntax as search_rows. The id column is always included. Refuses to overwrite an existing file unless overwrite=true.

Args: dataset: Exact dataset name, e.g. "Candidates". file_path: Destination path for the .csv or .json file. format: "auto" (default, infers from extension), or force "csv"/"json". conditions: Optional filter, e.g. [{"column": "stage", "op": "eq", "value": "Applied"}]. columns: Optional column projection, e.g. ["name", "phone"]. overwrite: Allow replacing an existing file (default false).

Returns: {"ok": true, "dataset", "file": "", "format", "rows_exported": }.

Example: export_rows(dataset="Candidates", file_path="~/Documents/applied_august.csv", conditions=[{"column": "applied_on", "op": "between", "value": ["2026-08-01", "2026-08-31"]}])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoauto
columnsNo
datasetYes
file_pathYes
overwriteNo
conditionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it reveals that the id column is always included, that existing files are not overwritten without overwrite=true, that format is inferred from the file extension unless forced, and what the return object looks like. This is meaningful behavior beyond the schema; only a few edge details like file-size limits or permission requirements are absent.

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 organized with a summary line, a usage note, a compact Args list, a Returns line, and a worked example. It is long only because there are six parameters to clarify, and every section adds useful information without repetition.

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?

For a six-parameter export tool with no annotations and an output schema, the description is comprehensive: it covers filters, projection, format inference, overwrite safety, return shape, and includes an example. There is no missing information an agent would need to call it correctly.

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 compensates fully by explaining every parameter, providing concrete examples for conditions and the overall call, and noting defaults for format and overwrite. An agent can construct a correct invocation from the description alone.

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 opens with a clear verb-resource-action statement: 'Export rows (optionally filtered and projected) to a CSV or JSON file.' This unambiguously distinguishes it from sibling read/search/write tools, and the 'spreadsheet-friendly form' phrasing reinforces the practical purpose.

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 explicitly says when to use the tool ('Use this when the user wants their data back in a spreadsheet-friendly form') and connects filtering to the search_rows syntax, which helps an agent reason about shared semantics. It does not explicitly contrast with search_rows or other alternatives, so it stops short of a 5.

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/kanishk393/mdm-mcp'

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