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MarlBurroW

TeamSpeak MCP

by MarlBurroW

manage_ban_rules

Control access to TeamSpeak servers by creating, removing, or managing IP, name, and UID-based ban rules to restrict unwanted users.

Instructions

Create, delete or manage ban rules

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform
ban_idNoBan ID (required for delete action)
ipNoIP address pattern to ban (optional for add action)
nameNoName pattern to ban (optional for add action)
uidNoClient unique identifier to ban (optional for add action)
timeNoBan duration in seconds (0 = permanent, default: 0)
reasonNoBan reason (optional)Banned by AI
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions actions ('create, delete or manage') but fails to explain critical details such as required permissions, whether changes are reversible, rate limits, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is a significant gap.

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 extremely concise—a single phrase with zero waste. It's front-loaded with the core functionality, making it easy to parse quickly. Every word earns its place by directly stating the tool's purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (managing ban rules with 7 parameters), no annotations, and no output schema, the description is incomplete. It lacks essential context like error handling, return values, or behavioral nuances, leaving significant gaps for an AI agent to understand how to use it effectively.

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

Parameters3/5

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

The schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or providing examples. This meets the baseline for high schema coverage.

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?

The description clearly states the tool's purpose with specific verbs ('create, delete or manage') and the resource ('ban rules'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'ban_client' or 'list_bans', which would require explicit comparison to achieve a perfect score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'ban_client' or 'list_bans', nor does it mention prerequisites or context for usage. It merely restates the tool's functionality without operational context.

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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