CatchAll (by NewsCatcher)

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

Name	Required	Description
`file`	Yes	CSV content (required) — raw CSV text or standard base64-encoded CSV, capped at 10 MB after decoding. Server-side file paths are not accepted.
`name`	Yes	Human-readable dataset name (required).
`api_key`	No	CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
`project_id`	No	Optional project ID to associate this dataset with (new in 1.6.1).
`description`	No	Optional dataset description.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

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

Without annotations, the description discloses key behaviors: CSV content requirements, quality implications of missing columns, plan-dependent max file size, and mapping of additional columns to attributes. It does not detail return behavior or authentication, but an output schema exists.

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 (5 sentences) with no redundant information. It front-loads the main action and then provides necessary details in a logical order, making it easy for an AI agent to parse.

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 tool's complexity (CSV upload, enrichment, parameter mapping), the description covers prerequisites, quality tips, size limits, and alternatives. An output schema exists for return values. Missing details like error handling or enrichment specifics, but overall adequate.

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 baseline is 3. The description adds value by explaining the meaning of CSV columns beyond schema descriptions, such as the necessity of a name column and the benefit of domain/description for enrichment. This extra context justifies a 4.

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 creates a new dataset by uploading a CSV file, distinguishing it from the sibling `append_csv_to_dataset` which adds to an existing dataset. The verb and resource are specific and unambiguous.

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?

The description explicitly provides when to use (create new dataset from CSV) and when not to use (use `append_csv_to_dataset` for adding rows). It also gives guidance on CSV requirements (name column, recommended domain/description) and mentions plan-dependent file size limits.

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

Disambiguation5/5

Each tool is scoped to a specific resource type and action, with clear distinctions between similarly named operations (e.g., pull_results vs pull_job_csv, initialize_query vs validate_query). No two tools appear to perform the same function.

Naming Consistency4/5

Tools consistently use snake_case verb_noun patterns (create_X, get_X, list_X, update_X, delete_X), with domain-specific verbs like submit, pull, initialize, and validate adding semantic clarity. Minor deviations such as pull_* vs get_* and compound names like create_dataset_from_csv are still predictable.

Tool Count2/5

At 60 tools, the server is heavily overstuffed for a single MCP surface. While the broad domain (datasets, entities, jobs, monitors, projects, webhooks) justifies many operations, the sheer volume exceeds typical recommended limits and includes near-duplicates (pull_results vs pull_job_csv, get_dataset vs get_dataset_status), making agent tool selection unwieldy.

Completeness4/5

The tool set provides robust CRUD and lifecycle coverage for all major resources, including special operations like csv import, webhook mapping, and monitor enable/disable. Minor gaps such as the absence of a get_monitor (single monitor details) and no cancel_job can be worked around via list_monitors and waiting for job completion.

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