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CatchAll

by com.newscatcherapi

append_csv_to_dataset

Append entities from a CSV file to an existing dataset.

Parses the CSV and appends its entities to the dataset. Each row must have a name column; include a domain or description column (or both) for meaningful enrichment. Duplicate rows (by name) are skipped. To create a new dataset from a CSV, use create_dataset_from_csv 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.
api_keyNoCatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
dataset_idYesThe dataset ID to append entities to (required).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: CSV parsing, required `name` column, optional `domain`/`description` columns, duplicate skipping by name, file size cap (10 MB after decoding), and rejection of server-side file paths. This is comprehensive.

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?

Highly concise: 3 sentences with front-loaded purpose. Every sentence adds essential information—behavior, column requirements, and alternative tool. No wasted words.

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 (3 params, output schema exists), the description covers purpose, when-to-use, behavioral constraints, column requirements, file format limits, and duplicates handling. It is fully sufficient for an agent to correctly invoke the tool.

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 covers all 3 parameters with detailed descriptions (100% coverage). The description adds value by specifying content requirements (columns) for the CSV file, which goes beyond the schema's format-only description.

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 the action: 'Append entities from a CSV file to an existing dataset.' It also explicitly distinguishes from the sibling `create_dataset_from_csv`, making its purpose and scope unambiguous.

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?

Explicitly tells when to use (appending to existing dataset) and when not to (creating new dataset), naming the alternative. However, it does not contrast with other nearby siblings like `add_dataset_entities`, which could be relevant for non-CSV data.

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.

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