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get_token_candle_data

Retrieve historical price candle data for cryptocurrency tokens to perform technical analysis and market research.

Instructions

Get candle data about any token for analysis.

Expects a CandleStickRequest, returns a list of candle sticks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
candle_stick_requestsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the 'get_token_candle_data' tool, decorated with @mcp.tool() for registration in FastMCP. It handles authentication check, calls the armor_client helper, and returns candle data or error.
    @mcp.tool()
    async def get_token_candle_data(candle_stick_requests: CandleStickRequest) -> List:
        """
        Get candle data about any token for analysis.
    
        Expects a CandleStickRequest, returns a list of candle sticks.
        """
        if not armor_client:
            return [{"error": "Not logged in"}]
        try:
            result: List = await armor_client.get_market_candle_data(candle_stick_requests)
            return result
        except Exception as e:
            return [{"error": str(e)}]
  • Pydantic model defining the input schema for the candle data request, including token address, time interval, time range, and market cap option.
    class CandleStickRequest(BaseModel):
        token_address: str = Field(description="Public mint address of the token. To get the address from a token symbol use `get_token_details`")
        time_interval: Literal["1s", "5s", "15s", "1m", "3m", "5m", "15m", "30m", "1h", "2h", "4h", "6h", "8h", "12h", "1d", "3d", "1w", "1mn"] = Field(default="1h", description="Time frame to get the candle sticks. Use larger candle time frames over larger time windows to keep returned candles minimal")
        time_from: str = Field(description="The time from which to start the candle data in ISO 8601 format. Attempt to change this to keep number of candles returned under 64.")
        time_to: Optional[str] = Field(default=None, description="The time to end the candle data in ISO 8601 format. Use only for historic analysis.")
        market_cap: Optional[bool] = Field(default=False, description="Whether to return the marketcap of the token instead of the price")
  • Helper method in ArmorWalletAPIClient that serializes the request and makes a POST API call to '/tokens/candles/' to retrieve the candle data from the backend service.
    async def get_market_candle_data(self, data: CandleStickRequest) -> Dict:
        """Get the candle sticks."""
        payload = data.model_dump(exclude_none=True)
        return await self._api_call("POST", f"tokens/candles/", payload)
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 expects a CandleStickRequest and returns a list of candle sticks, but doesn't disclose critical traits like whether it's read-only, requires authentication, has rate limits, or what 'candle sticks' represent (e.g., time-series data). The description adds minimal context beyond basic input-output, leaving significant gaps.

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 concise with two sentences that are front-loaded: the first states the purpose, and the second covers input-output. There's no wasted text, but it could be more structured by elaborating on key details. It's appropriately sized for a simple tool but under-specified.

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 has 1 parameter with 0% schema coverage, no annotations, but an output schema exists (which reduces the need to explain return values), the description is incomplete. It covers basic input-output but lacks details on parameter semantics, behavioral traits, and usage context. It's minimally adequate but has clear gaps for a data retrieval tool.

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 schema description coverage is 0%, and there is 1 parameter ('candle_stick_requests') with a nested reference to CandleStickRequest. The description mentions 'Expects a CandleStickRequest' but doesn't explain what this request entails (e.g., token identifiers, time ranges, intervals). It adds minimal meaning beyond the schema, failing to compensate for the lack of schema descriptions.

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 'Get candle data about any token for analysis' which provides a clear verb ('Get') and resource ('candle data about any token'), but it's vague about what 'candle data' specifically entails and doesn't distinguish from siblings like 'get_top_trending_tokens' or 'search_token_details' that might also provide token-related data. The purpose is understandable but lacks specificity.

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 doesn't mention any prerequisites, exclusions, or compare to sibling tools such as 'get_all_orders' or 'search_token_details' that might serve similar analytical purposes. Usage is implied only by the name and description, with no explicit context.

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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