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vidhupv

X(Twitter) MCP Server

by vidhupv

create_draft_tweet

Create draft tweets for X/Twitter by providing content, enabling users to prepare posts for review and scheduling through the chat interface.

Instructions

Create a draft tweet

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesThe content of the tweet

Implementation Reference

  • The handler function that executes the create_draft_tweet tool. It validates the input arguments, creates a draft by saving the tweet content to a local JSON file with a unique ID, logs the action, and returns a success message with the draft ID.
    async def handle_create_draft_tweet(arguments: Any) -> Sequence[TextContent]:
        if not isinstance(arguments, dict) or "content" not in arguments:
            raise ValueError("Invalid arguments for create_draft_tweet")
        content = arguments["content"]
        try:
            # Simulate creating a draft by storing it locally
            draft = {"content": content, "timestamp": datetime.now().isoformat()}
            # Ensure drafts directory exists
            os.makedirs("drafts", exist_ok=True)
            # Save the draft to a file
            draft_id = f"draft_{int(datetime.now().timestamp())}.json"
            with open(os.path.join("drafts", draft_id), "w") as f:
                json.dump(draft, f, indent=2)
            logger.info(f"Draft tweet created: {draft_id}")
            return [
                TextContent(
                    type="text",
                    text=f"Draft tweet created with ID {draft_id}",
                )
            ]
        except Exception as e:
            logger.error(f"Error creating draft tweet: {str(e)}")
            raise RuntimeError(f"Error creating draft tweet: {str(e)}")
  • Registers the 'create_draft_tweet' tool in the list_tools() function, including its name, description, and input schema.
    Tool(
        name="create_draft_tweet",
        description="Create a draft tweet",
        inputSchema={
            "type": "object",
            "properties": {
                "content": {
                    "type": "string",
                    "description": "The content of the tweet",
                },
            },
            "required": ["content"],
        },
    ),
  • Defines the JSON schema for the tool's input, requiring a 'content' property of type string.
    inputSchema={
        "type": "object",
        "properties": {
            "content": {
                "type": "string",
                "description": "The content of the tweet",
            },
        },
        "required": ["content"],
    },
  • Dispatches the tool call to the specific handler function in the general call_tool handler.
    if name == "create_draft_tweet":
        return await handle_create_draft_tweet(arguments)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this creates a draft but doesn't mention whether this requires authentication, what happens on success/failure, if drafts are saved persistently, or any rate limits. The description is minimal and lacks essential behavioral context for a creation tool.

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 at just three words, with zero wasted language. It's front-loaded with the essential action and resource, making it efficient for quick understanding.

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?

For a creation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what happens after creation, what the draft object contains, how to reference it later, or any error conditions. The minimal description leaves too many questions unanswered.

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 'content' parameter clearly documented in the schema. The description doesn't add any additional parameter information beyond what's already in the schema, so it meets the baseline score of 3 when schema coverage is high.

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 'Create a draft tweet' clearly states the action (create) and resource (draft tweet), but it doesn't distinguish this tool from its sibling 'create_draft_thread' which creates a different type of draft. The purpose is understandable but lacks sibling differentiation.

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 'create_draft_thread' for threads or 'publish_draft' to publish existing drafts. There's no mention of prerequisites, constraints, or typical use cases.

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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