CatchAll (by NewsCatcher)

pull_monitor_csv

Download the latest monitor run's results as a CSV file.

Use when:

You want the most recent monitor run output as a CSV for offline analysis or export.
Prefer this over pull_monitor_results when the consumer needs spreadsheet/CSV format.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`api_key`	No	CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
`monitor_id`	Yes	The monitor ID to download results for.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.5/5.0

Behavior4/5

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

No annotations are provided, so the description must carry the full behavioral load. It discloses that the tool downloads the latest run's results as CSV, which implies a read-only operation. However, it does not explicitly state the absence of side effects, permissions required, or what happens if no runs exist. The intent is clear but could be slightly more transparent.

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 very concise: a single sentence stating the main action, followed by a bulleted 'Use when' section that covers usage guidelines and alternatives. Every sentence adds value without redundancy.

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 simple download tool with an output schema and well-known sibling tools, the description is complete. It specifies the input (monitor_id), the output format (CSV), and the scope (latest run). No critical details are missing for an agent to decide whether to invoke this 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 100%, so the baseline is 3. The description adds no extra meaning beyond what the schema already provides for the parameters (`api_key` and `monitor_id`). It does not explain how to get the `monitor_id` or the role of the optional API key.

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 tool description clearly states that it downloads the latest monitor run's results as a CSV file. It uses specific verbs and resources ('Download ... results as a CSV file') and distinguishes from the sibling `pull_monitor_results` by explicitly mentioning CSV format for offline analysis or export.

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 includes a 'Use when' section with explicit guidelines: want the most recent run output as CSV, and prefer over `pull_monitor_results` for spreadsheet/CSV needs. It names an alternative tool, providing clear context for when to choose this tool.

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.

Resources

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