Skip to main content
Glama
jankowtf

MCP Server Template for Cursor IDE

by jankowtf

mood

Check the server's mood by asking questions like 'How are you?' to receive cheerful responses with hearts 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 function that implements the core logic of the 'mood' tool. It ignores the 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)]
  • Dispatch logic within the main @app.call_tool() handler that routes 'mood' tool calls to the check_mood function, with 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"])
  • Tool registration in @app.list_tools() defining the name, description, and input schema for the 'mood' tool, referencing mood_description.
    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,
                }
            },
        },
    ),
  • Helper variable providing the detailed description used in the 'mood' tool's input schema.
    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 ❤️"
    )
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the tool queries the server's mood, the server is 'always happy', and responses include 'a cheerful message and a heart ❤️'. This covers the interactive nature and predictable output style, though it lacks details like response format or error handling. No contradiction with annotations exists.

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' defines the action, and 'it's always happy!' adds essential behavioral context. There's zero redundancy or fluff, making it highly efficient for an agent to parse.

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 reasonably complete for its purpose. It explains what the tool does and the expected response behavior. However, it lacks output details (e.g., response structure) and doesn't address potential edge cases, leaving some gaps in full contextual understanding for an agent.

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?

The input schema has 100% description coverage, with the parameter 'question' fully documented in the schema itself (including examples like 'How are you?'). The description adds no additional parameter semantics beyond what the schema provides, such as formatting tips or constraints. According to rules, with high schema coverage (>80%), the baseline is 3 even without 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: to ask the server about its mood, with the specific behavioral outcome that it 'always responds with a cheerful message and a heart ❤️'. It distinguishes from sibling tools (all related to prompt application or documentation fetching) by focusing on a conversational interaction rather than functional operations. However, it doesn't explicitly contrast with specific alternatives for mood-checking, keeping it at 4 rather than 5.

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?

The description provides no guidance on when to use this tool versus alternatives. While it implies usage for checking server mood, it doesn't specify contexts (e.g., after errors, during idle time) or exclusions (e.g., not for functional queries). With sibling tools focused on practical tasks, the lack of when/when-not guidance leaves the agent guessing about appropriate use cases.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jankowtf/mcp-hitchcode'

If you have feedback or need assistance with the MCP directory API, please join our Discord server