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desk3
by desk3

get_cycles

Identify cryptocurrency market cycle tops using Pi Cycle and Puell Multiple indicators to analyze Bitcoin's four-year cycle patterns.

Instructions

Does the Bitcoin Four-Year Cycle Exist? Discover the cryptocurrency market cycle indicator that helps you identify the top of the cryptocurrency bull market. This is a collection of publicly available signals including Pi Cycle and Puell Multiple data. Return fields: (puellMultiple Puell: multiple status / piCycleTop: Pi cycle top status / likelihood: cryptocurrency market cycle top indicator)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The primary handler function for the 'get_cycles' tool. It fetches cryptocurrency market cycle indicators (Puell Multiple, Pi Cycle Top, likelihood) from the external API endpoint.
    async def get_cycles() -> dict[str, Any]:
        """
        Get Simple indicators data including Puell Multiple Status, Pi Cycle Top Status, and Crypto Market Cycle Top Indicator.
        :return: Simple indicators data with puellMultiple, piCycleTop, and likelihood fields
        """
        url = 'https://mcp.desk3.io/v1/market/cycles'
        try:
            return request_api('get', url)
        except Exception as e:
            raise RuntimeError(f"Failed to fetch cycles data: {e}")
  • The JSON schema definition for the 'get_cycles' tool input, which requires no parameters.
    inputSchema={
        "type": "object",
        "properties": {},
        "required": [],
    },
  • Registration of the 'get_cycles' tool in the MCP server's list_tools handler, including name, description, and schema.
    types.Tool(
        name="get_cycles",
        description="Does the Bitcoin Four-Year Cycle Exist? Discover the cryptocurrency market cycle indicator that helps you identify the top of the cryptocurrency bull market. This is a collection of publicly available signals including Pi Cycle and Puell Multiple data. Return fields: (puellMultiple Puell: multiple status / piCycleTop: Pi cycle top status / likelihood: cryptocurrency market cycle top indicator) ",
        inputSchema={
            "type": "object",
            "properties": {},
            "required": [],
        },
    ),
  • The MCP tool call dispatcher case that invokes the get_cycles handler and formats the response as TextContent.
    case "get_cycles":
        try:
            data = await get_cycles()
            return [
                types.TextContent(
                    type="text",
                    text=json.dumps(data, indent=2),
                )
            ]
        except Exception as e:
            raise RuntimeError(f"Failed to fetch cycles data: {e}")
Behavior2/5

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

With no annotations, the description carries the full burden but lacks behavioral details. It mentions 'publicly available signals' and return fields, but doesn't disclose traits like data freshness, rate limits, authentication needs, or whether it's a read-only operation. The description adds some context about the indicator's purpose but misses key operational aspects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

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

The description is poorly structured and verbose. It starts with a rhetorical question and promotional language ('Discover...'), which doesn't efficiently convey the tool's function. The return fields are listed but buried in the text. It could be more front-loaded and concise, such as by stating the action first and removing fluff.

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 of financial indicators and no output schema, the description is incomplete. It lists return fields but doesn't explain their meanings (e.g., what 'likelihood' represents) or format. With no annotations and a vague purpose, it fails to provide enough context for an agent to use the tool effectively, especially compared to siblings with clearer functions.

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, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter semantics beyond the schema, but this is acceptable given the lack of parameters. A baseline of 4 is appropriate as it doesn't detract from the schema's completeness.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description is vague about the tool's actual function. While it mentions 'discover the cryptocurrency market cycle indicator' and references Pi Cycle and Puell Multiple data, it doesn't clearly state what the tool does (e.g., 'retrieves current cycle indicator data' or 'returns Bitcoin four-year cycle status'). It distinguishes from siblings like get_pi_cycle_top and get_puell_multiple by combining signals, but the purpose is more explanatory than action-oriented.

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?

No explicit guidance on when to use this tool versus alternatives is provided. It implies usage for identifying bull market tops with combined indicators, but doesn't compare to siblings like get_cycle_indicators or specify scenarios (e.g., 'use this for aggregated signals instead of individual ones'). The description is more promotional than instructional.

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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