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feuerdev
by feuerdev

create_note

Generate and manage notes with a title and text using JSON output, facilitated by the Keep MCP server for Google Keep integration and organization.

Instructions

Create a new note with title and text.

Args:
    title (str, optional): The title of the note
    text (str, optional): The content of the note
    
Returns:
    str: JSON string containing the created note's data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNo
titleNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • This is the core handler function for the MCP 'create_note' tool. Decorated with @mcp.tool(), it handles the tool execution: authenticates the Keep client, creates a new note, adds a specific label, syncs changes, and returns JSON-serialized note data.
    @mcp.tool()
    def create_note(title: str = None, text: str = None) -> str:
        """
        Create a new note with title and text.
        
        Args:
            title (str, optional): The title of the note
            text (str, optional): The content of the note
            
        Returns:
            str: JSON string containing the created note's data
        """
        keep = get_client()
        note = keep.createNote(title=title, text=text)
        
        # Get or create the keep-mcp label
        label = keep.findLabel('keep-mcp')
        if not label:
            label = keep.createLabel('keep-mcp')
        
        # Add the label to the note
        note.labels.add(label)
        keep.sync()  # Ensure the note is created and labeled on the server
        
        return json.dumps(serialize_note(note))
Behavior2/5

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 states the tool creates a note but fails to mention critical traits such as permissions required, whether the creation is idempotent, error handling, or rate limits. The return format is mentioned ('JSON string'), but details like structure or potential errors are omitted, leaving significant gaps in transparency.

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?

The description is structured with clear sections (Args, Returns) and uses brief, direct sentences. It avoids unnecessary fluff and front-loads the core purpose. However, the 'Args' and 'Returns' sections could be more integrated into the flow, and some redundancy exists (e.g., repeating 'note' in the return statement), slightly affecting efficiency.

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 moderate complexity (2 parameters, no annotations, but has an output schema), the description is partially complete. It covers the basic purpose and return format, but lacks details on behavioral aspects, error cases, and usage context. The presence of an output schema means the description doesn't need to explain return values deeply, but other gaps keep it from being fully adequate.

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 description adds minimal semantics beyond the input schema, which has 0% description coverage. It lists the parameters ('title' and 'text') and notes they are optional, but doesn't explain their meaning, constraints (e.g., length limits), or default behaviors. Since schema coverage is low, the description partially compensates by naming parameters, but it doesn't fully address the gap, resulting in a baseline score.

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 action ('Create a new note') and the resources involved ('with title and text'), making the purpose immediately understandable. It distinguishes from siblings like 'delete_note' and 'update_note' by specifying creation rather than modification or deletion. However, it doesn't explicitly differentiate from 'find' (which likely searches notes), leaving minor room for improvement.

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 like 'update_note' or 'find'. It lacks context about prerequisites (e.g., whether authentication is needed) or scenarios where this is appropriate (e.g., initial note creation vs. editing existing ones). This absence of usage instructions reduces its effectiveness for an AI agent.

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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