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love_task

Routes LÖVE development requests to docs, live game inspection, or code execution based on your goal, so an AI assistant can query or act without extra context.

Instructions

Intelligent router for LÖVE development: routes to docs, live inspection, or code execution.

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": {
      "goal": "<goal>"
    },
    "name": "love_task"
  }
}

Input Schema

NameRequiredDescriptionDefault
goalYesWhat you want to accomplish or query
contextNoOptional background or current game state info

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

C2.6/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden. It does not disclose side effects, permission requirements, or whether 'code execution' mutates user files, and it does not say whether a call returns a routing decision or an executed result — a significant gap for a router that can run code.

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

Conciseness4/5

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

A single front-loaded sentence with no filler or repetition. It is efficient, though its brevity is part of why several behavioral questions go unanswered.

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?

There is no output schema, so the description must explain what a call returns and roughly how routing is decided; it does neither. For a dispatcher that can touch documentation, a live game instance, and code execution, this leaves the agent guessing about outcomes and side effects.

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?

Schema description coverage is 100% and both parameters ('goal' and 'context') are documented in the schema itself, so the baseline is 3. The description adds no meaning about how to phrase a goal or what context is useful for routing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific domain (LÖVE development) and three routing destinations (docs, live inspection, code execution), which gives an agent some sense of scope. However, it never says whether the tool performs the work itself or merely dispatches, nor does it name the underlying tools it routes to, so the actual behavior stays vague.

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?

There is no explicit when-to-use or when-not-to-use guidance, and no comparison against the listed siblings (discover_tools, load_tool_schema, call_tool, report_usage). The agent has to infer that this is the entry point for LÖVE work rather than an alternative to the generic meta-tools.

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