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openSVM

DexScreener MCP Server

by openSVM

get_token_orders

Retrieve order data for a specific cryptocurrency token to analyze trading activity and market behavior on decentralized exchanges.

Instructions

Check orders paid for a specific token

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainIdYesChain ID (e.g., "solana")
tokenAddressYesToken address

Implementation Reference

  • The core handler function in DexScreenerService that fetches token orders from the DexScreener API endpoint /orders/v1/{chainId}/{tokenAddress} with rate limiting.
    async getTokenOrders({ chainId, tokenAddress }: OrderParams): Promise<TokenOrder[]> {
      return this.fetch<TokenOrder[]>(
        `/orders/v1/${chainId}/${tokenAddress}`,
        tokenRateLimiter
      );
    }
  • Input schema and description for the get_token_orders tool as declared in the MCP server capabilities.
    get_token_orders: {
      description: 'Check orders paid for a specific token',
      inputSchema: {
        type: 'object',
        properties: {
          chainId: {
            type: 'string',
            description: 'Chain ID (e.g., "solana")',
          },
          tokenAddress: {
            type: 'string',
            description: 'Token address',
          },
        },
        required: ['chainId', 'tokenAddress'],
      },
    },
  • src/index.ts:319-323 (registration)
    Registration and dispatching logic in the MCP callTool request handler that routes get_token_orders calls to the DexScreenerService method.
    case 'get_token_orders': {
      const args = request.params.arguments as { chainId: string; tokenAddress: string };
      result = await this.dexService.getTokenOrders(args);
      break;
    }
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 'Check orders paid' but doesn't clarify if this is a read-only operation, what data is returned, potential rate limits, authentication needs, or error conditions. This leaves significant gaps for an agent to understand how to use it effectively.

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 a single, direct sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core purpose without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'orders paid' entails, the format of returned data, or how results are structured, leaving the agent with insufficient context to handle responses or understand the tool's full behavior.

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?

The input schema has 100% description coverage, with clear documentation for chainId and tokenAddress parameters. The description adds no additional semantic context beyond what the schema provides, such as example values or usage notes, so it meets the baseline but doesn't enhance understanding.

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 action ('Check orders') and the target ('paid for a specific token'), making the purpose understandable. However, it doesn't differentiate this tool from its siblings (e.g., get_latest_boosted_tokens, search_pairs), which might also involve token-related queries, so it falls short of 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 no guidance on when to use this tool versus alternatives. With siblings like get_latest_boosted_tokens and search_pairs available, there's no indication of context, prerequisites, or exclusions for selecting this tool over others.

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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