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create_dataset_from_csv

Create a new dataset by uploading a CSV file.

The CSV must have at least a name column. For meaningful entity enrichment each row should also include a domain column or a description column (or both) — a row with only a name is accepted but produces lower-quality enrichment. Additional columns are mapped to entity attributes. Max file size is plan-dependent. To add CSV rows to an existing dataset, use append_csv_to_dataset instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileYesCSV content (required) — raw CSV text or standard base64-encoded CSV, capped at 10 MB after decoding. Server-side file paths are not accepted.
nameYesHuman-readable dataset name (required).
api_keyNoCatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
project_idNoOptional project ID to associate this dataset with (new in 1.6.1).
descriptionNoOptional dataset description.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.6/5.0
Behavior4/5

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

No annotations provided, so description carries the burden. It explains input constraints and enrichment logic, but does not state whether the operation is synchronous or asynchronous, any side effects, or error handling. However, it does mention file size limits and required columns, which adds transparency.

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?

Description is concise with two paragraphs: first sentence states purpose, then follows with requirements, recommendations, file size note, and alternative tool. No fluff or redundant 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?

Given output schema exists, return values are covered. The description provides enough context for input and differentiation from siblings. It lacks explicit mention of authorization prerequisites or whether creation is immediate, but overall it is complete for typical usage scenarios.

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

Parameters4/5

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

Schema coverage is 100%, so each parameter is described. The tool description adds context beyond schema by specifying that the 'file' parameter should be CSV content (not a path), that the CSV must have a 'name' column, and that additional columns map to attributes. This enhances understanding.

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?

Description clearly states 'Create a new dataset by uploading a CSV file.' It specifies the action, resource, and method. Also explicitly distinguishes from sibling 'append_csv_to_dataset' by stating to use that tool for adding rows to an existing dataset.

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

Usage Guidelines5/5

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

Description provides explicit guidance: when to use (creating new dataset), when not to use (use append_csv_to_dataset for appending), and tips on CSV content (require 'name' column, recommend 'domain'/'description' columns for better enrichment, and max file size is plan-dependent).

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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TDQS

A3.6/5.0
Disambiguation4/5

Most tools have distinct purposes, but some pairs like create_dataset vs create_dataset_from_csv or pull_results vs pull_job_csv could cause confusion. However, descriptions clarify differences.

Naming Consistency4/5

Tools follow a consistent verb_noun pattern (e.g., create_dataset, list_datasets) with minor exceptions like append_csv_to_dataset and pull_job_csv. Overall predictable.

Tool Count3/5

60 tools is high for an MCP server, but the domain (web research, job processing, multiple resource types) justifies the count. Still borders on excessive.

Completeness5/5

The server offers full CRUD for datasets, entities, monitors, projects, webhooks, plus job submission, status polling, result retrieval (JSON/CSV), webhook management, and health endpoints. No obvious gaps.