mcp-cloudflare-crawl

crawl_list

List stored Cloudflare crawl jobs, filter by status, and paginate results to track progress and manage submitted, running, or completed crawls.

Instructions

List all crawl jobs stored in the local database.

Jobs are recorded automatically when crawl_start or crawl_and_wait is called. Status is updated whenever crawl_status is polled.

Args: status_filter: Filter by job status — one of: "submitted", "running", "completed", "errored", "cancelled_due_to_timeout", "cancelled_due_to_limits", "cancelled_by_user". limit: Maximum number of jobs to return (default: 50). offset: Number of jobs to skip for pagination (default: 0).

Returns: { "jobs": [ { "job_id": "...", "url": "https://...", "status": "completed", "created_at": "2026-03-25T00:00:00+00:00", "updated_at": "2026-03-25T00:01:00+00:00" }, ... ], "count": }

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`offset`	No
`status_filter`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 13, 2026

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does disclose meaningful behavior: the data source (local database), the lifecycle that populates it, and pagination defaults. It stops short of stating read-only semantics explicitly or any concurrency/rate behavior, but the listing nature makes the safety profile inferable.

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?

Front-loaded with the one-line purpose, then lifecycle context, then parameters. The Args section is dense and every line adds information not present in the schema's bare titles.

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?

Parameters and lifecycle are completely covered, and for a simple list tool that is nearly everything an agent needs. The Returns block duplicates an existing output schema, which is redundant rather than harmful, and no filtering-by-URL or ordering behavior is mentioned.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate, and it does fully: it enumerates every valid status_filter value (which the schema does not), and documents limit/offset defaults and pagination purpose. Nothing about the parameters is left undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("List all crawl jobs stored in the local database"), which clearly separates it from action siblings like crawl_start or crawl_cancel. It doesn't explicitly name a sibling it overlaps with (e.g., crawl_status), but the purpose is 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?

Explains the data model context that tells an agent when this is useful: jobs exist only after crawl_start/crawl_and_wait, and status is refreshed by polling crawl_status. It gives clear context but no explicit when-not or alternative-selection 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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