Amazing Marvin MCP

by qemqemqem

Overview Schema Source Related Servers Discussions Admin

Python

Local

claim_reward_points

Claim reward points for completing a task by specifying points, task ID, and completion date.

Instructions

Claim reward points for completing a task

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": {
      "points": "<points>",
      "item_id": "<item_id>",
      "date": "<date>"
    },
    "name": "claim_reward_points"
  }
}

Input Schema

Name	Required	Description	Default
`points`	Yes
`item_id`	Yes
`date`	Yes
`debug`	No

Output Schema

Name	Required	Description	Default
`data`	Yes
`metadata`	Yes	Metadata about the data itself
`summary`	Yes	Human-readable insights
`debug`	Yes
`success`	Yes
`api_version`	No		current
`response_version`	No		1.0

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.2Jun 10, 2026

TDQS

C2.3/5.0

Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It only states the action, omitting details like idempotency, side effects, authorization requirements, or whether points are already earned. The description does not add beyond the name.

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

Conciseness2/5

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

The description is extremely brief (one sentence), which is concise but at the expense of completeness. It lacks structure and fails to convey necessary details, making it under-specified rather than efficiently informative.

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

Completeness1/5

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

Given 4 parameters with 0% schema coverage, no annotations, and an unseen output schema, the description is severely incomplete. It does not explain parameters, return values, or the tool's effect, leaving major gaps for an AI agent.

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

Parameters1/5

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

Schema description coverage is 0%, so the description should explain the parameters. It does not mention any of the four parameters (points, item_id, date, debug), leaving the agent to infer their meaning without guidance.

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 identifies the action ('Claim reward points') and the context ('for completing a task'), distinguishing it from sibling tools which focus on tasks, projects, or time tracking. However, it lacks specificity on what 'claim' entails (e.g., adding to balance vs redeeming) and the exact relationship to a task.

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?

No guidance on when to use this tool versus alternatives, nor any prerequisites or conditions. The description implies it follows task completion, but does not state this explicitly or provide exclusionary criteria.

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