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like_post

Like posts on Bluesky Social by providing the post URI and CID. This tool enables users to engage with content through likes within the Bluesky Social platform.

Instructions

Like a post.

Args:
    ctx: MCP context
    uri: URI of the post to like
    cid: CID of the post to like

Returns:
    Status of the like operation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uriYes
cidYes

Implementation Reference

  • The main handler function for the 'like_post' tool. It authenticates via get_authenticated_client, calls bluesky_client.like(uri, cid), and returns success or error details.
    @mcp.tool()
    def like_post(
        ctx: Context,
        uri: str,
        cid: str,
    ) -> Dict:
        """Like a post.
    
        Args:
            ctx: MCP context
            uri: URI of the post to like
            cid: CID of the post to like
    
        Returns:
            Status of the like operation
        """
        try:
            bluesky_client = get_authenticated_client(ctx)
            like_response = bluesky_client.like(uri, cid)
            return {
                "status": "success",
                "message": "Post liked successfully",
                "like_uri": like_response.uri,
                "like_cid": like_response.cid,
            }
        except Exception as e:
            error_msg = f"Failed to like post: {str(e)}"
            return {"status": "error", "message": error_msg}
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 the action but doesn't mention required permissions, whether the operation is idempotent, rate limits, or what the 'Status of the like operation' entails. For a mutation tool with zero annotation coverage, this is a significant gap 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.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, but includes redundant sections ('Args:', 'Returns:') that add little value without elaboration. While concise, it under-specifies critical details, making it less helpful than a slightly longer but informative description would be.

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 mutation tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It lacks details on authentication needs, error conditions, return values, and parameter semantics, leaving significant gaps for the agent to operate effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 parameters (uri, cid) but provides no semantic context—what these identifiers represent, their format, or how they relate to the post. Without this, the agent cannot understand parameter meaning beyond the schema's basic types.

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 ('Like a post') with a specific verb and resource, making the purpose immediately understandable. However, it doesn't explicitly differentiate from the sibling 'unlike_post' tool, which would require mentioning the opposite action for full distinction.

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 'unlike_post' or 'get_likes'. There's no mention of prerequisites (e.g., authentication status), context for usage, or exclusions, leaving the agent without operational context.

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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