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list_datasets

Retrieve a list of all available cryo datasets from the Cryo MCP Server, enabling users to query Ethereum blockchain data efficiently through MCP-compatible clients.

Instructions

Return a list of all available cryo datasets

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The list_datasets tool handler: runs 'cryo help datasets' subprocess, parses output to return list of available Cryo datasets. Registered via @mcp.tool() decorator.
    @mcp.tool()
    def list_datasets() -> List[str]:
        """Return a list of all available cryo datasets"""
        # Ensure we have the RPC URL
        rpc_url = os.environ.get("ETH_RPC_URL", DEFAULT_RPC_URL)
        
        result = subprocess.run(
            ["cryo", "help", "datasets", "-r", rpc_url],
            capture_output=True,
            text=True
        )
    
        # Parse the output to extract dataset names
        lines = result.stdout.split('\n')
        datasets = []
    
        for line in lines:
            if line.startswith('- ') and not line.startswith('- blocks_and_transactions:'):
                # Extract dataset name, removing any aliases
                dataset = line[2:].split(' (alias')[0].strip()
                datasets.append(dataset)
            if line == 'dataset group names':
                break
    
        return datasets
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 tool returns a list but doesn't specify format, pagination, rate limits, authentication needs, or whether it's read-only. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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 that directly states what the tool does. It's front-loaded with the core action and resource, with zero wasted words or redundant information, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate as a basic read operation. However, it lacks details about return format, data scope, or how it fits with sibling tools, which would help the agent use it more effectively in context.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter information, and it appropriately doesn't mention any. Baseline for 0 parameters is 4, as the description focuses on the tool's purpose without unnecessary parameter details.

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 ('Return a list') and resource ('all available cryo datasets'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'lookup_dataset' or 'list_available_sql_tables', which prevents a perfect score.

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 'lookup_dataset' or 'query_dataset'. It lacks any context about prerequisites, timing, or exclusions, leaving the agent to infer usage from the tool name alone.

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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