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OpenRefine MCP Server

OpenRefine MCP Server

by FiquemSabendo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

apply_operations

Apply a set of data cleaning operations to an OpenRefine project to transform and refine your dataset.

Instructions

Apply operations to an OpenRefine project.

Args: project_id: ID of the project to apply operations to operations: Operations as JSON string

Returns: Summary with application status and last modified time

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": {
      "project_id": "<project_id>",
      "operations": "<operations>"
    },
    "name": "apply_operations"
  }
}

Input Schema

NameRequiredDescriptionDefault
project_idYes
operationsYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Without annotations, the description carries full burden. It states the return includes status and last modified time, indicating a mutating operation (not read-only). However, it does not disclose whether operations are reversible, idempotent, or what constitutes invalid input.

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 concise (two sentences for purpose, plus structured Args/Returns). No redundant or irrelevant information. Every sentence adds value.

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?

Given the lack of annotations, the description covers purpose, parameters (with some explanation), and return value. It could be more complete by noting operational nuances, but it meets basic needs for a simple 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 0%, so the description partly compensates by explicitly explaining 'project_id' as the project ID and 'operations' as a JSON string. However, it does not elaborate on the expected structure or constraints of the operations JSON, leaving ambiguity.

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's action: 'Apply operations to an OpenRefine project.' This distinguishes it from sibling tools like create_project, delete_project, and export_csv, which have different purposes.

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, nor does it mention prerequisites, limitations, or exceptions. It only describes the basic action without contextual usage advice.

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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