CatchAll (by NewsCatcher)

get_webhook_history

Get webhook delivery history, either for a resource or for a webhook.

Query in exactly one of two modes:

By resource: pass resource_type + resource_id to see deliveries made for a specific job/monitor/monitor_group.
By webhook: pass webhook_id to see every delivery made through one webhook — including manual test deliveries (from test_webhook), which are not tied to a job or monitor and only appear in this mode.

Input Schema

TableJSON Schema

Name	Required	Description
`page`	No	Page number for pagination (default: 1).
`api_key`	No	CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
`page_size`	No	Number of results per page (default: 50, max: 500).
`webhook_id`	No	Webhook ID to fetch history for. Mutually exclusive with `resource_type`/`resource_id`.
`resource_id`	No	The ID of the job/monitor/monitor_group.
`resource_type`	No	Resource type: 'job', 'monitor', or 'monitor_group'. Required together with `resource_id` when `webhook_id` is not given.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.7/5.0

Behavior4/5

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

With no annotations, the description carries the transparency burden. It reveals the mutually exclusive query modes and the subtle behavior that test deliveries are only visible via the webhook mode. However, it does not explicitly state what happens if conflicting parameters are passed or describe error/edge-case behavior, leaving some room for ambiguity.

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 compact and well-structured with a bulleted list for the two modes. Every sentence adds relevant information, with no wasted words or 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?

The tool has moderate complexity (two modes, 6 parameters, mutual exclusivity), and the description fully addresses how to use it. An output schema exists, so return format is covered elsewhere. The inclusion of the test delivery nuance completes the picture for this 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 coverage is 100%, so the baseline is 3. The description adds value beyond the schema by clarifying the relationship between resource_type/resource_id and webhook_id, and by explaining why test deliveries only appear in webhook mode. This enriches the parameter semantics.

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 function: fetching webhook delivery history. It distinguishes between two query modes (by resource or by webhook) and differentiates from sibling tools like get_webhook (webhook configuration) and test_webhook (manual delivery).

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?

Explicitly explains exactly when to use each of the two modes, including the specific parameters required for each. It also notes that manual test deliveries only appear in webhook mode, guiding users on when to choose that mode over resource querying.

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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