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armorwallet
by armorwallet

get_token_candle_data

Retrieve token candle data for analysis by specifying token address, time interval, and range. Supports market cap or price data in predefined time frames for informed crypto decision-making.

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

  • MCP tool handler and registration for 'get_token_candle_data'. Wraps the Armor client call to fetch candle data for a token.
    @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)}]
  • Input schema (Pydantic model) for the get_token_candle_data tool: CandleStickRequest defining 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 performs the actual API call to retrieve token candle data, invoked by the tool handler.
    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?

With no annotations, the description carries full burden but provides minimal behavioral context. It states the tool expects a CandleStickRequest and returns a list of candle sticks, but doesn't disclose rate limits, authentication needs, error conditions, or data freshness. The description adds some value by hinting at analysis use, but lacks critical operational details for a data-fetching tool.

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 brief (two sentences) and front-loaded with the core purpose. Every sentence contributes: the first states what the tool does, the second clarifies input/output. There's no wasted text, though it could be more informative given the lack of schema descriptions.

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 complexity (fetching financial data with multiple parameters), no annotations, and an output schema (which reduces need to describe returns), the description is incomplete. It lacks guidance on usage, parameter details, and behavioral traits. However, the presence of an output schema slightly mitigates the gap, making it minimally adequate but with clear deficiencies.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate but doesn't. It mentions 'CandleStickRequest' but provides no details about its parameters. The baseline is 3 because the description at least names the request object, but it fails to add meaningful semantics beyond what's implied by the tool name, leaving parameters undocumented.

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 verb ('Get') and resource ('candle data about any token'), specifying it's for analysis. It distinguishes from siblings by focusing on token candle data rather than wallet management, orders, or swaps. However, it doesn't explicitly differentiate from potential similar tools like 'get_top_trending_tokens' beyond the candle data 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 explicit guidance on when to use this tool versus alternatives is provided. The description mentions analysis but doesn't specify scenarios or prerequisites. It references 'get_token_details' for obtaining addresses, but this is in the schema, not the description itself. No exclusions or comparison to siblings are stated.

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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