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armorwallet
by armorwallet

get_top_trending_tokens

Retrieve the top trending tokens within a specified time frame for market cap or volume comparison, using a structured request format.

Instructions

Get the top trending tokens in a particular time frame. Great for comparing market cap or volume.

Expects a TopTrendingTokensRequest, returns a list of tokens with their details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_trending_tokens_requestsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the 'get_top_trending_tokens' tool, decorated with @mcp.tool() for registration in the MCP server. It checks if the armor_client is initialized, calls the client's top_trending_tokens method, and handles exceptions by returning error messages.
    @mcp.tool()
    async def get_top_trending_tokens(top_trending_tokens_requests: TopTrendingTokensRequest) -> List:
        """
        Get the top trending tokens in a particular time frame. Great for comparing market cap or volume.
        
        Expects a TopTrendingTokensRequest, returns a list of tokens with their details.
        """
        if not armor_client:
            return [{"error": "Not logged in"}]
        try:
            result: List = await armor_client.top_trending_tokens(top_trending_tokens_requests)
            return result
        except Exception as e:
            return [{"error": str(e)}]
  • Pydantic BaseModel defining the input schema (TopTrendingTokensRequest) for the tool, specifying the time_frame parameter with allowed values and default.
    class TopTrendingTokensRequest(BaseModel):
        time_frame: Literal["5m", "15m", "30m", "1h", "2h", "3h", "4h", "5h", "6h", "12h", "24h"] = Field(default="24h", description="Time frame to get the top trending tokens")
  • Supporting method in ArmorWalletAPIClient class that serializes the request data and makes the HTTP POST request to the '/tokens/trending/' API endpoint to fetch the top trending tokens.
    async def top_trending_tokens(self, data: TopTrendingTokensRequest) -> List:
        """Get the top trending tokens."""
        payload = data.model_dump(exclude_none=True)
        return await self._api_call("POST", f"tokens/trending/", 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 mentions returning 'a list of tokens with their details,' which hints at read-only behavior but doesn't clarify permissions, rate limits, or data freshness. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational traits.

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 and front-loaded, with three sentences that efficiently convey the tool's purpose, use case, and input/output expectations. No wasted words, though it could be slightly more structured for clarity.

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 moderate complexity (one parameter, no annotations, but with an output schema), the description is somewhat complete. It covers the basic purpose and parameter intent, and the output schema likely handles return values. However, it lacks details on behavioral aspects like rate limits or data sources, leaving room for improvement.

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%, but the description compensates by explaining the parameter's purpose: 'in a particular time frame.' However, it doesn't detail the specific time frame options or default values beyond what the schema provides. With one parameter and partial semantic addition, this meets the baseline for adequate but incomplete coverage.

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 top trending tokens in a particular time frame.' It specifies the verb ('Get') and resource ('top trending tokens'), and adds context about comparing market cap or volume. However, it doesn't explicitly differentiate from sibling tools like 'get_token_candle_data' or 'search_token_details', which prevents a perfect score.

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 minimal usage guidance. It mentions 'Great for comparing market cap or volume,' which implies a use case but doesn't specify when to use this tool versus alternatives like 'get_token_candle_data' for historical data or 'search_token_details' for specific token info. No explicit when/when-not rules or prerequisites 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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