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desk3
by desk3

get_altcoin_season_index

Determine if the cryptocurrency market is in Altcoin Season by analyzing top 100 altcoins' performance against Bitcoin over 90 days, with charts and metrics for tracking market trends.

Instructions

Altcoin Season Index page provides real-time insights into whether the cryptocurrency market is currently in Altcoin Season, based on the performance of the top 100 altcoins relative to Bitcoin over the past 90 days, with detailed charts and metrics for tracking market trends and altcoin dominance

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Core handler function that executes the tool logic by making an API request to retrieve the Altcoin Season Index data.
    async def get_altcoin_season_index() -> dict:
        """
        Get the Altcoin Season Index.
        :return: Altcoin season index data
        """
        url = 'https://mcp.desk3.io/v1/market/altcoin/season'
        try:
            return request_api('get', url)
        except Exception as e:
            raise RuntimeError(f"Failed to fetch Altcoin Season Index data: {e}")
  • Registration of the 'get_altcoin_season_index' tool in the @server.list_tools() handler, including name, description, and input schema.
    types.Tool(
        name="get_altcoin_season_index",
        description="Altcoin Season Index page provides real-time insights into whether the cryptocurrency market is currently in Altcoin Season, based on the performance of the top 100 altcoins relative to Bitcoin over the past 90 days, with detailed charts and metrics for tracking market trends and altcoin dominance",
        inputSchema={
            "type": "object",
            "properties": {},
            "required": [],
        },
    ),
  • JSON Schema for the tool input (no parameters required).
    inputSchema={
        "type": "object",
        "properties": {},
        "required": [],
    },
  • Handler case in @server.call_tool() that invokes the core function and formats the response for MCP.
    case "get_altcoin_season_index":
        try:
            data = await get_altcoin_season_index()
            return [
                types.TextContent(
                    type="text",
                    text=json.dumps(data, indent=2),
                )
            ]
        except Exception as e:
            raise RuntimeError(f"Failed to fetch Altcoin Season Index data: {e}")
  • Shared helper function used by the tool handler to make authenticated API requests to the Desk3 API.
    def request_api(method: str, url: str, params: dict = None, data: dict = None) -> any:
        headers = {
            'Accepts': 'application/json',
            'X-DESK3_PRO_API_KEY': API_KEY,
        }
        try:
            logging.info(f"Requesting {method.upper()} {url} params={params} data={data}")
            if method.lower() == 'get':
                response = requests.get(url, headers=headers, params=params)
            elif method.lower() == 'post':
                response = requests.post(url, headers=headers, json=data)
            else:
                raise ValueError(f"Unsupported HTTP method: {method}")
            response.raise_for_status()
            logging.info(f"Response {response.status_code} for {url}")
            return json.loads(response.text)
        except Exception as e:
            logging.error(f"Error during {method.upper()} {url}: {e}")
            raise
Behavior3/5

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 describes the tool's function (providing real-time insights with charts and metrics) and data sources (top 100 altcoins vs Bitcoin over 90 days), but lacks details on rate limits, authentication needs, or potential data freshness issues. It doesn't contradict annotations, but could be more comprehensive for a tool with no annotation coverage.

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

Conciseness4/5

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose, data basis, and additional features (charts and metrics). It's front-loaded with the main function and avoids unnecessary details, though it could be slightly more concise by trimming phrases like 'for tracking market trends and altcoin dominance' which are somewhat redundant.

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 no annotations, no output schema, and 0 parameters, the description adequately covers the tool's purpose and data sources. However, it lacks information on return format (e.g., what the 'insights' or 'metrics' look like), which is important since there's no output schema. For a tool with no structured data support, more detail on behavioral aspects would improve completeness.

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 appropriately focuses on the tool's purpose without redundant parameter info, earning a baseline score of 4 for zero-parameter tools.

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 tool provides real-time insights into whether the market is in Altcoin Season based on specific metrics (top 100 altcoins vs Bitcoin over 90 days). It specifies the resource (Altcoin Season Index page) and includes details about charts and metrics. However, it doesn't explicitly differentiate from siblings like 'get_bitcoin_dominance' or 'get_cycle_indicators' that might overlap in market analysis.

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. The description mentions tracking market trends and altcoin dominance, but doesn't specify scenarios where this tool is preferred over siblings like 'get_bitcoin_dominance' for dominance metrics or 'get_cycles' for cycle analysis. Usage is implied rather than clearly defined.

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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