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list_webhooks

List all your webhooks.

Use when:

  • You want to see all webhook endpoints configured in your account.

  • You need to find a webhook_id to pass to monitors (via webhook_ids) or jobs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number for pagination (default: 1).
api_keyNoCatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
page_sizeNoNumber of results per page (default: 100, max: 1000).
project_idNoOptional filter to webhooks belonging to a specific project.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A3.8/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action and use cases; it does not mention that it is a read-only operation (though 'list' implies it), discuss pagination behavior, response structure, or any side effects. For a tool with zero annotation coverage, this is a notable gap.

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 two sentences with a clear lead statement, followed by a bulleted 'Use when' list. There is zero filler, and the primary purpose is front-loaded. Every word earns its place, making it concise and well-structured.

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?

The tool is simple (list with pagination) and the output schema is provided, so return format is covered. The description gives clear use cases and the schema documents parameters. No critical usage element is missing for an agent to call it correctly. Minor omissions like explicit read-only confirmation and edge-case pagination notes are not essential given the schema.

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?

All four parameters (page, api_key, page_size, project_id) are fully described in the input schema, so the schema does the heavy lifting. The description adds no additional parameter-level meaning beyond what the schema provides—it only mentions webhook_id usage, which is not a parameter. With 100% schema coverage, the baseline of 3 is appropriate.

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 opens with a clear action and resource: 'List all your webhooks.' It explicitly states the scope ('all your webhooks') and differentiates from a single-webhook fetch by emphasizing 'all' and by the use cases. This distinguishes it from get_webhook without needing to name it.

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?

The 'Use when' section gives two explicit scenarios: viewing all endpoints and finding a webhook_id for monitors/jobs. This is clear context for when to call the tool. However, it does not explicitly state when not to use it (e.g., to fetch a single webhook) or mention the alternative get_webhook, so it stops short of full exclusion guidance.

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.