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X(Twitter) MCP Server

by vidhupv

create_draft_thread

Create draft tweet threads for X/Twitter by providing multiple tweet contents as an array, preparing content for review before publishing.

Instructions

Create a draft tweet thread

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentsYesAn array of tweet contents for the thread

Implementation Reference

  • The main handler function that executes the create_draft_thread tool. It validates the input arguments, creates a JSON file for the draft thread in the 'drafts' directory, and returns a TextContent response with the draft ID.
    async def handle_create_draft_thread(arguments: Any) -> Sequence[TextContent]:
        if not isinstance(arguments, dict) or "contents" not in arguments:
            raise ValueError("Invalid arguments for create_draft_thread")
        contents = arguments["contents"]
        if not isinstance(contents, list) or not all(isinstance(item, str) for item in contents):
            raise ValueError("Invalid contents for create_draft_thread")
        try:
            draft = {"contents": contents, "timestamp": datetime.now().isoformat()}
            # Ensure drafts directory exists
            os.makedirs("drafts", exist_ok=True)
            # Save the draft to a file
            draft_id = f"thread_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 thread created: {draft_id}")
            return [
                TextContent(
                    type="text",
                    text=f"Draft thread created with ID {draft_id}",
                )
            ]
        except Exception as e:
            logger.error(f"Error creating draft thread: {str(e)}")
            raise RuntimeError(f"Error creating draft thread: {str(e)}")
  • Tool registration in the list_tools() function, defining the name, description, and input schema for create_draft_thread.
    Tool(
        name="create_draft_thread",
        description="Create a draft tweet thread",
        inputSchema={
            "type": "object",
            "properties": {
                "contents": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "An array of tweet contents for the thread",
                },
            },
            "required": ["contents"],
        },
    ),
  • Input schema definition for the create_draft_thread tool, specifying an object with a required 'contents' array of strings.
        "type": "object",
        "properties": {
            "contents": {
                "type": "array",
                "items": {"type": "string"},
                "description": "An array of tweet contents for the thread",
            },
        },
        "required": ["contents"],
    },

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
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 action ('Create a draft tweet thread') but does not explain what 'draft' entails (e.g., saved locally vs. on a server), permissions required, or any side effects like rate limits or data persistence, leaving significant gaps.

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 a single, direct sentence with no wasted words, clearly stating the tool's purpose. It is appropriately sized and front-loaded, making it efficient and easy to understand at a glance.

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?

Given the lack of annotations and output schema, the description is incomplete for a tool that creates a draft. It does not cover what a 'draft' means in this context, how it interacts with other tools like 'publish_draft', or what the expected outcome is, leaving the agent with insufficient information for effective use.

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 schema description coverage is 100%, with the 'contents' parameter well-documented in the schema as an array of tweet contents. The description does not add any additional meaning or context beyond this, such as formatting rules or constraints, so it meets the baseline for adequate but not enhanced parameter explanation.

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 verb ('Create') and resource ('draft tweet thread'), making the purpose evident. However, it does not explicitly differentiate from siblings like 'create_draft_tweet' or 'create_draft_reply', which might involve similar actions but for different tweet types, leaving some ambiguity.

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?

No guidance is provided on when to use this tool versus alternatives such as 'create_draft_tweet' or 'create_draft_reply'. The description lacks context about prerequisites, exclusions, or specific scenarios for creating a draft thread, offering minimal usage direction.

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