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Access Check

access_check
Read-only

Loss-leader URL access, login, and CAPTCHA check. Price: $0.05 USDC. Example: GET /v1/access-check?url=https://example.com

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already establish this as a read-only, non-destructive operation, and the description adds context about the tool's cost ($0.05 USDC) and the HTTP method (GET). It does not contradict the annotations and provides additional behavioral detail about the check's scope (access, login, CAPTCHA).

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 a single concise sentence followed by an example, front-loading the key purpose and cost. Every word earns its place with no redundancy.

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?

For a simple single-parameter check tool with good annotations, the description covers the core purpose, cost, and invocation example. It does not explain response format, but the absence of an output schema and the tool's simplicity make this acceptable.

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

Parameters2/5

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

The only parameter, `url`, is documented in the schema with a URI format, but the description's example is the sole mention and adds no new semantics. With 0% schema description coverage, the description fails to compensate by explaining parameter constraints or behavior, making this a weak area.

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 explicitly states 'URL access, login, and CAPTCHA check' with a concrete example, clearly identifying the tool's function. It distinguishes itself from sibling tools like preflight and premium_preflight by focusing on access and login verification.

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 description provides a clear example of how to invoke the tool (GET /v1/access-check?url=...), implying usage for checking URL accessibility. However, it does not explicitly compare with sibling tools or state when not to use it, so it lacks explicit exclusions.

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

B3.3/5.0
Disambiguation2/5

Several tools overlap significantly: preflight, premium_preflight, and batch_preflight all perform URL preflight with varying price/features, and access_check appears to be a subset of preflight. This creates ambiguity in selecting the right tool for a task.

Naming Consistency3/5

Most names use snake_case, but the pattern varies: some end in _check, _preflight, _discovery, or _intel, and preflight is a single word without a verb. The naming is readable but not uniformly consistent.

Tool Count5/5

With 7 tools, the count is well within the ideal 3-15 range and appropriate for a URL preflight service that covers different access levels, batch processing, and specialized checks.

Completeness4/5

The tool set covers core preflight, access, commerce, API discovery, change detection, and batch operations. Minor gaps exist (e.g., no dedicated raw fetch or history endpoint), but they are not critical and can be worked around.

Resources