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MCP Server Replicate

get_collection_details

Retrieve detailed information about a specific collection using its slug to access metadata and contents.

Instructions

Get detailed information about a specific collection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
collection_slugYes

Implementation Reference

  • The main handler function that executes the tool: fetches collection details from Replicate API using ReplicateClient and returns a parsed Collection object.
    async def get_collection_details(collection_slug: str) -> Collection:
        """Get detailed information about a specific collection."""
        async with ReplicateClient(api_token=os.getenv("REPLICATE_API_TOKEN")) as client:
            result = await client.get_collection(collection_slug)
            return Collection(**result)
  • Registers the get_collection_details tool on the FastMCP server instance using the @mcp.tool() decorator.
    @mcp.tool()
  • Pydantic BaseModel used for input/output typing and validation of the Collection object returned by the tool.
    class Collection(BaseModel):
        """A collection of related models on Replicate."""
        name: str = Field(..., description="Name of the collection")
        slug: str = Field(..., description="URL-friendly identifier for the collection")
        description: Optional[str] = Field(None, description="Description of the collection's purpose")
        models: List[Model] = Field(default_factory=list, description="Models in this collection")
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 information, implying a read-only operation, but does not specify authentication requirements, rate limits, error handling, or what 'detailed information' includes. This leaves significant gaps in understanding the tool's behavior.

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 with no wasted words. It is appropriately sized for a simple tool and front-loaded with the core purpose, making it easy to scan and understand quickly.

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. It does not explain what 'detailed information' includes, such as fields or structure of the returned data, nor does it cover behavioral aspects like error cases. For a tool with no structured documentation, this leaves too many unknowns for effective use.

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?

The input schema has 1 parameter with 0% description coverage, and the tool description does not add any semantic information about the parameter. It does not explain what a 'collection_slug' is, its format, or how to obtain it. This fails to compensate for the low schema coverage, leaving the parameter poorly documented.

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 states the tool's purpose ('Get detailed information about a specific collection'), which is clear but vague. It specifies the verb ('Get') and resource ('collection'), but lacks detail on what 'detailed information' entails. It does not distinguish from sibling tools like 'list_collections', which likely lists collections rather than providing details for one.

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 does not mention prerequisites, such as needing a collection slug, or compare it to sibling tools like 'list_collections' or 'get_model_details'. There is no explicit or implied context for usage.

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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