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andreasHornqvist

MCP Server Template for Cursor IDE

mood

Check the server's emotional state by asking questions like 'How are you?' or 'What's your mood?' to receive cheerful responses with heart symbols in Cursor IDE.

Instructions

Ask the server about its mood - it's always happy!

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesAsk this MCP server about its mood! You can phrase your question in any way you like - 'How are you?', 'What's your mood?', or even 'Are you having a good day?'. The server will always respond with a cheerful message and a heart ❤️

Implementation Reference

  • The check_mood function is the core handler for the 'mood' tool. It ignores the input question and always returns a cheerful message with a heart emoji.
    async def check_mood(
        question: str,
    ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
        """Check server's mood - always responds cheerfully with a heart."""
        msg: str = "I'm feeling great and happy to help you! ❤️"
        return [types.TextContent(type="text", text=msg)]
  • Registers the 'mood' tool in the list_tools() decorator function with its metadata including name, description, and input schema.
    types.Tool(
        name="mood",
        description="Ask the server about its mood - it's always happy!",
        inputSchema={
            "type": "object",
            "required": ["question"],
            "properties": {
                "question": {
                    "type": "string",
                    "description": mood_description,
                }
            },
        },
    ),
  • Defines the input schema for the 'mood' tool, requiring a 'question' string parameter with a descriptive help text.
    inputSchema={
        "type": "object",
        "required": ["question"],
        "properties": {
            "question": {
                "type": "string",
                "description": mood_description,
            }
        },
    },
  • Helper variable defining the detailed description used in the 'mood' tool's input schema for the question parameter.
    mood_description: str = (
        "Ask this MCP server about its mood! You can phrase your question "
        "in any way you like - 'How are you?', 'What's your mood?', or even "
        "'Are you having a good day?'. The server will always respond with "
        "a cheerful message and a heart ❤️"
    )
  • Dispatch logic in the call_tool handler that routes 'mood' calls to the check_mood function after input validation.
    elif name == "mood":
        if "question" not in arguments:
            return [types.TextContent(
                type="text",
                text="Error: Missing required argument 'question'"
            )]
        return await check_mood(arguments["question"])
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses key behavioral traits: the server will 'always respond with a cheerful message and a heart ❤️' (as noted in the input schema description, which complements the tool description). However, it doesn't cover other aspects like response format, error handling, or performance characteristics. The description adds some context but isn't comprehensive.

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 extremely concise (one sentence) and front-loaded with the core purpose. Every word earns its place: 'Ask the server about its mood' states the action, and 'it's always happy!' adds useful behavioral context without redundancy. No unnecessary details or fluff are present.

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

Completeness3/5

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

Given the tool's low complexity (single parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the purpose and basic behavior, but lacks details on error cases, response structure, or integration with sibling tools. Without an output schema, more explanation of return values would be helpful, though the input schema hints at the response.

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%, with the input schema providing detailed semantics for the 'question' parameter, including examples and expected server behavior. The tool description adds no additional parameter information beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.

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: 'Ask the server about its mood' with the specific action 'ask' and resource 'server mood'. It distinguishes from siblings like 'figma_design' or 'generate_image' by focusing on conversational interaction rather than design or image generation. However, it doesn't explicitly contrast with 'mcp_fetch', which might also involve server communication.

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 description implies usage context through 'it's always happy!', suggesting this is for casual interaction rather than functional tasks. However, it lacks explicit guidance on when to use this versus alternatives like 'mcp_fetch' or other tools, and doesn't specify prerequisites or exclusions. The input schema provides example questions, but the description itself offers only implied usage.

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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