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desk3
by desk3

get_eth_trend

Analyze Ethereum's price and network activity trends over the past three months with historical data on active and new addresses.

Instructions

Get the ETH trend chart for the past three months. Format: [[date, price, active addresses, new addresses]]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function that fetches and returns the ETH trend data from the Desk3 API endpoint.
    async def get_eth_trend() -> list[list]:
        """
        Get the ETH trend chart for the past three months.
        :return: List of [date, price, active addresses, new addresses]
        """
        url = 'https://mcp.desk3.io/v1/market/eth/trend'
        try:
            return request_api('get', url)
        except Exception as e:
            raise RuntimeError(f"Failed to fetch ETH trend data: {e}")
  • JSON schema definition for the get_eth_trend tool in the list_tools handler, specifying no required input parameters.
    types.Tool(
        name="get_eth_trend",
        description="Get the ETH trend chart for the past three months. Format: [[date, price, active addresses, new addresses]]",
        inputSchema={
            "type": "object",
            "properties": {},
            "required": [],
        },
    ),
  • Registration and execution logic for the get_eth_trend tool in the handle_call_tool function.
    case "get_eth_trend":
        try:
            data = await get_eth_trend()
            return [
                types.TextContent(
                    type="text",
                    text=json.dumps(data, indent=2),
                )
            ]
        except Exception as e:
            raise RuntimeError(f"Failed to fetch ETH trend data: {e}")
  • Helper function used by get_eth_trend to make authenticated API requests to the Desk3 service.
    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. It discloses the output format ('Format: [[date, price, active addresses, new addresses]]'), which is useful behavioral context. However, it lacks details on data sources, update frequency, or potential limitations like rate limits or authentication needs.

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 highly concise and front-loaded, consisting of two clear sentences: one stating the purpose and timeframe, and another specifying the output format. Every sentence adds essential information without redundancy.

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 annotations, no output schema), the description is adequate but has gaps. It explains what the tool returns but lacks context on data freshness, sources, or error handling. For a data-fetching tool, more behavioral transparency 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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the output format, which aligns with the baseline expectation 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's purpose: 'Get the ETH trend chart for the past three months.' It specifies the resource (ETH trend chart) and timeframe (past three months), but does not explicitly differentiate from sibling tools like 'get_btc_trend' or 'get_cycle_indicators' beyond the ETH focus.

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 guidance is provided on when to use this tool versus alternatives. The description does not mention any prerequisites, exclusions, or comparisons to sibling tools such as 'get_btc_trend' for Bitcoin data or 'get_cycle_indicators' for broader market analysis.

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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