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BerkantACUN

redis-guard-mcp

by BerkantACUN

redis_check_permissions

Verifies via ACL DRYRUN that dangerous Redis commands like EVAL, CONFIG SET, and FLUSHALL are blocked for the connected user, confirming the privilege layer is properly configured.

Instructions

Ask Redis's own ACL engine, via ACL DRYRUN, whether each of a curated list of dangerous commands (EVAL, CONFIG SET, FLUSHALL, SHUTDOWN, CLIENT KILL/PAUSE, ...) would actually succeed for the connected user right now. would_succeed should always be empty — the tool surface itself can't send any of these either way, but an empty list here means the privilege layer is also configured correctly, not just relied upon by omission.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations are absent, so the description carries the full burden. It transparently discloses that this is a read-only check ('Ask Redis's own ACL engine... ACL DRYRUN') and clarifies that the tool itself cannot execute those commands ('the tool surface itself can't send any of these either way'). This effectively communicates the non-destructive nature, which is essential given the risk of the commands listed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences and front-loads the core purpose and method. It wastes no words, though the second sentence could be slightly tighter. The expected outcome is clearly stated, making it efficient without being overly verbose.

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?

Given the tool's complexity (no params, no output schema), the description covers the key aspects: purpose, method, and expected result. It mentions the `would_succeed` field, providing a hint about the return structure. While it doesn't detail the full response format, it's sufficient for an agent to understand the tool's role and avoid misuse.

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

Parameters4/5

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

The tool has zero parameters, so schema coverage is complete by default. The baseline for zero parameters is 4, and the description adds no parameter-specific information (as there is none to add). It appropriately focuses on behavior rather than parameter details.

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 clearly states the action: using Redis's ACL DRYRUN to check if a curated list of dangerous commands would succeed. It explicitly names the method (ACL DRYRUN) and the resource (Redis's ACL engine). This is distinct from all sibling tools, which are data-access commands, so there's no ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains what the tool does and even includes an expected outcome ('would_succeed should always be empty'), but it doesn't explicitly state when to use this tool versus alternatives. Since siblings are all read operations, the usage context is implied but not directly articulated, leaving some room for the agent to infer the appropriate scenario.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.