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get_post_thread

Retrieve complete conversation threads from Bluesky Social by fetching posts, replies, and parent content for comprehensive context.

Instructions

Get a full conversation thread.

Args:
    ctx: MCP context
    uri: URI of the post to get thread for
    depth: How many levels of replies to include
    parent_height: How many parent posts to include

Returns:
    Thread with the post and its replies/parents

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uriYes
depthNo
parent_heightNo

Implementation Reference

  • The main handler function for the 'get_post_thread' tool. It authenticates the Bluesky client, calls the underlying atproto client's get_post_thread method, converts the response to a dict, and returns it wrapped in a success/error structure.
    @mcp.tool()
    def get_post_thread(
        ctx: Context,
        uri: str,
        depth: Optional[int] = None,
        parent_height: Optional[int] = None,
    ) -> Dict:
        """Get a full conversation thread.
    
        Args:
            ctx: MCP context
            uri: URI of the post to get thread for
            depth: How many levels of replies to include
            parent_height: How many parent posts to include
    
        Returns:
            Thread with the post and its replies/parents
        """
        try:
            bluesky_client = get_authenticated_client(ctx)
    
            thread_response = bluesky_client.get_post_thread(uri, depth, parent_height)
    
            # Convert the response to a dictionary
            if hasattr(thread_response, "model_dump"):
                thread_data = thread_response.model_dump()
            else:
                thread_data = thread_response
    
            return {"status": "success", "thread": thread_data}
        except Exception as e:
            error_msg = f"Failed to get post thread: {str(e)}"
            return {"status": "error", "message": error_msg}
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves a thread but doesn't describe what 'full' means (e.g., pagination, rate limits, error handling, or authentication requirements). For a read operation with no annotation coverage, this is a significant gap in transparency about how the tool behaves in practice.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by structured sections for Args and Returns. Each sentence earns its place by defining parameters and output, though the 'ctx' parameter is unexplained and could be trimmed for clarity.

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 complexity (3 parameters, no annotations, no output schema), the description is incomplete. It lacks details on authentication, error cases, return format (beyond 'Thread'), and how depth/parent_height interact. For a tool that retrieves nested data, more context is needed to use it effectively without trial and error.

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 0%, so the description must compensate. It lists all parameters (uri, depth, parent_height) and provides brief explanations (e.g., 'How many levels of replies to include' for depth), adding meaning beyond the bare schema. However, it doesn't clarify units (e.g., depth in levels vs. count), default behaviors, or constraints, leaving some ambiguity.

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: 'Get a full conversation thread.' This specifies the verb ('Get') and resource ('conversation thread'), distinguishing it from siblings like get_post (single post) or get_timeline (timeline feed). However, it doesn't explicitly differentiate from get_author_feed or get_posts which might also return threads, making it slightly less specific than ideal.

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. It doesn't mention when to choose get_post_thread over get_post (for a single post) or get_author_feed (for a user's posts), nor does it specify prerequisites like authentication or context for thread retrieval. This leaves the agent without clear usage direction.

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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