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amazing-marvin-complete-mcp

amazing-marvin-complete-mcp

by andreasd083
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_labels

Read-only

Fetch all labels with their IDs, enabling accurate filtering and use of label_ids across tasks and projects.

Instructions

Get all labels with ids, for label_ids and filtering.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_labels"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.0

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the read-only nature is known. The description adds that it returns 'all' labels, implying full coverage, but does not discuss pagination, ordering, or result format. With the output schema present, this is minimally adequate but not rich.

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 a single focused sentence with no filler. It front-loads the core action and resource, then adds a compact note on how the labels are used.

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?

For a zero-parameter, read-only list operation with an output schema and supported by annotations, the description is sufficient. It names the resource, the returned data (ids), and the purpose (filtering), leaving no critical gap for an agent to call it correctly.

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 and schema coverage is 100%, so the baseline is 4. The description adds no parameter semantics, but none are needed here.

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 'Get all labels with ids', identifying the specific resource (labels) and the expected outcome (IDs for label_ids and filtering). This distinguishes it from sibling getters like get_goals and get_categories without 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 phrase 'for label_ids and filtering' implies when this tool is useful, but there is no explicit when-to-use guidance or mention of alternatives. It relies on inference rather than direct instruction.

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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