GMA2 MCP

get_group_annotation

Retrieve the user-added annotation text for a specific group in grandMA2. Returns the annotation or an empty response if none is set.

Instructions

read the user-added annotation text on a group.

in grandMA2, the Info keyword reads or writes user-added descriptive text
annotations on objects. this tool reads the annotation. if no annotation has
been set on the group, the response will be empty.

note: this does NOT return group composition or fixture details.
to see group details, use list_groups with a specific group_id instead.

Args:
    group_id: group ID to read annotation from

Returns:
    str: the user annotation text, or empty response message if none set

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "group_id": "<group_id>"
    },
    "name": "get_group_annotation"
  }
}

Input Schema

Name	Required	Description	Default
`group_id`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jun 21, 2026

TDQS

A4.7/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses read-only behavior, empty response if no annotation, and contextual background about grandMA2 Info keyword. Could mention error cases like non-existent group, but overall transparent.

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?

Description is concise: two short paragraphs plus a clear Args/Returns section. Every sentence adds value, well-structured and front-loaded with core purpose.

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

Completeness5/5

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

Given one parameter and no annotations, the description is complete. It explains output (string or empty), addresses potential confusion with sibling tool list_groups, and provides sufficient context for correct usage.

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?

Schema coverage is 0%, so description must compensate. It adds a brief description for group_id: 'group ID to read annotation from', which adds meaning beyond schema's title and type. However, it lacks constraints like valid range or format.

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 tool reads annotation text on a group, specifies it does not return group composition or fixture details, and distinguishes from list_groups, making the purpose unambiguous.

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

Usage Guidelines5/5

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

Explicitly tells when to use this tool (read annotation) and when not to (for group details, use list_groups instead), providing clear guidance on alternatives.

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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