Retrieve a bucket's policy JSON and anonymous-access summary to audit MinIO exposure and verify public access settings.
[READ] The bucket's policy JSON (verbatim) + anonymous-access summary.
Args: bucket_name: Bucket name (from bucket_ls). target: MinIO target name from config; omit for the default.
| Name | Required | Description | Default |
|---|---|---|---|
| target | No | ||
| bucket_name | Yes |
Changes observed during successful MCP inspections.
v0.7.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the '[READ]' prefix is largely redundant. The description does add value by disclosing the return content (raw policy JSON verbatim plus an anonymous-access summary), which tells the agent what it will actually receive. No mention of auth requirements or what happens when no policy exists.
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?
Front-loaded with the operation and its return shape, then a compact Args block. The Args entries are somewhat duplicative of the schema but earn their place by adding provenance and default behavior for target.
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?
No output schema exists, and the description covers the return value (verbatim policy JSON + anonymous-access summary), so the agent knows what to expect. For a two-parameter read tool this is close to complete; only edge behavior (no policy set, permission errors) is absent.
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?
Schema description coverage is 0%, so the description carries the parameter burden — and it does: bucket_name provenance (from bucket_ls) and target semantics (MinIO config target, omit for default) are both documented. That is meaningful compensation beyond the bare schema types.
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?
Specific verb+resource: reads the bucket's policy JSON (verbatim) plus an anonymous-access summary. '[READ]' immediately establishes the operation class. It does not explicitly name which sibling to prefer (e.g., bucket_info or bucket_exposure_audit), so it stops short of full differentiation.
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?
Usage is implied by the name and the '[READ]' tag, and it helpfully notes that bucket_name comes from bucket_ls, which is a real workflow hint. However, it never states when to use this versus bucket_info, bucket_exposure_audit, or bucket_lock_config. No exclusions or prerequisites are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.