access_list_agentsReturn the AI crawler tokens this index reads robots.txt rules for — only tokens their operators document — plus the widely-written tokens that no operator sends, which make a robots rule unenforceable. Useful when writing or auditing a robots.txt.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint=false and idempotentHint, so the safety profile is covered; the description goes beyond them by disclosing the composition of the result set (operator-documented tokens plus widely-written tokens no operator sends, which make rules unenforceable). It does not state whether the list is exhaustive/static or how results are ordered, which would be useful for a snapshot-style tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler: the return scope leads, the practical use case follows. The em-dash aside about unenforceable rules is dense but load-bearing, not padding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description carries the burden of explaining what comes back, and it does describe the semantic content of the set well. It does not describe the shape of each entry (bare token string vs. token plus operator/provenance), which is the one thing an agent would still have to discover by calling it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so per the rubric the baseline is 4 and there is nothing for the description to disambiguate. It correctly avoids inventing parameter talk, though it also cannot add value here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Return') and a tightly scoped resource ('the AI crawler tokens this index reads robots.txt rules for'), and it further specifies the two categories returned, which is unusually precise. It does not name or contrast with the siblings access_check_url or access_explain_verdicts, but the list-vs-check-vs-explain distinction is self-evident from the resource itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'Useful when writing or auditing a robots.txt' gives a concrete, actionable context of use rather than vague hand-waving. It stops short of stating when not to use it or pointing to a sibling (e.g. 'for a single URL's verdict, use access_check_url'), so it is clear-but-unbounded rather than exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.