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berlinbra

BlueSky MCP Server

bluesky_get_liked_posts

Retrieve posts liked by a user on BlueSky social network. Use this tool to access liked content with pagination support for managing large collections.

Instructions

Get a list of posts liked by the user

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of liked posts to return (default 50, max 100)
cursorNoPagination cursor for next page of results

Implementation Reference

  • Registers the 'bluesky_get_liked_posts' tool, including its description and input schema for limit and optional cursor parameters.
    types.Tool(
        name="bluesky_get_liked_posts",
        description="Get a list of posts liked by the user",
        inputSchema={
            "type": "object",
            "properties": {
                "limit": {
                    "type": "integer",
                    "description": "Maximum number of liked posts to return (default 50, max 100)",
                    "default": 50,
                },
                "cursor": {
                    "type": "string",
                    "description": "Pagination cursor for next page of results",
                },
            },
        },
    ),
  • Executes the 'bluesky_get_liked_posts' tool by calling the Bluesky API's get_likes method using the authenticated client's actor URI (IDENTIFIER), with provided limit and cursor.
    elif name == "bluesky_get_liked_posts":
        limit = arguments.get("limit", 50)
        cursor = arguments.get("cursor")
        response = await asyncio.to_thread(
            bluesky.client.app.bsky.feed.get_likes,
            {'uri': IDENTIFIER, 'limit': limit, 'cursor': cursor}
        )
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 what the tool does but lacks critical behavioral details: it doesn't mention authentication requirements (likely needed for user-specific data), rate limits, pagination behavior beyond the cursor parameter, or what the return format looks like (e.g., JSON structure).

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, efficient sentence that states the core purpose without any wasted words. It's appropriately sized for a simple tool and front-loaded with the essential information.

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 tool with no annotations and no output schema, the description is insufficient. It doesn't explain authentication needs, rate limits, return format, or error handling. Given the complexity of fetching user-specific data from a social platform, more contextual information is needed for the agent to use this tool effectively.

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 clear documentation for both parameters (limit and cursor). The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.

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 ('Get') and resource ('list of posts liked by the user'), making the purpose immediately understandable. It distinguishes from siblings like bluesky_get_posts (general posts) and bluesky_get_personal_feed (feed content), though it doesn't explicitly mention these distinctions in the description itself.

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 sibling tools like bluesky_get_posts or bluesky_search_posts, nor does it specify scenarios where retrieving liked posts is appropriate versus other post-fetching methods.

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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