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KalebJS

groupme-mcp

by KalebJS

remove_member

Remove a member from a GroupMe group by specifying the group ID and membership ID to manage group membership effectively.

Instructions

Remove a member from a group.

Args:
    group_id: The ID of the group.
    membership_id: The membership ID of the member to remove.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
group_idYes
membership_idYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Remove' implies a destructive mutation, the description doesn't specify whether this action is reversible, what permissions are required, or what happens upon success/failure (e.g., error if membership_id is invalid). It mentions the parameters but adds no behavioral context beyond the basic operation, leaving critical gaps for safe invocation.

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 highly concise and well-structured: a clear purpose statement followed by a bullet-point list of parameters with brief explanations. Every sentence earns its place, with no redundant or verbose language, making it easy to scan and understand quickly.

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 (a destructive mutation with no annotations and no output schema), the description is incomplete. It lacks information on behavioral traits (e.g., idempotency, error handling), output expectations, and usage guidelines. While it covers parameter semantics adequately, the overall context is insufficient for safe and effective use by an AI agent without additional assumptions.

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 description adds meaningful semantics for both parameters: 'group_id' identifies the group, and 'membership_id' specifies which member to remove. Since schema description coverage is 0% (titles only provide basic labels), this compensates well by clarifying what each parameter represents in the context of the operation. However, it doesn't detail format constraints (e.g., ID structure) or examples, keeping it from a perfect score.

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 a specific verb ('Remove') and resource ('a member from a group'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'update_membership' or 'destroy_group', which might also involve membership changes, leaving some ambiguity about when this specific removal operation is appropriate versus alternatives.

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. It doesn't mention prerequisites (e.g., needing admin permissions), exclusions (e.g., cannot remove the last admin), or compare it to related tools like 'update_membership' for role changes or 'destroy_group' for group deletion. This lack of context could lead to misuse in complex scenarios.

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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