Skip to main content
Glama

dexscreenertokeninfoagent_get_specific_pair_info

Retrieve real-time data for a specific decentralized exchange trading pair, including price, volume, liquidity, and historical trades, by specifying the blockchain and pair contract address.

Instructions

Get detailed information about a specific trading pair on a decentralized exchange by chain and pair address. This tool provides comprehensive data about a DEX trading pair including current price, 24h volume, liquidity, price changes, and trading history. Data comes from DexScreener and is updated in real-time. You must specify both the blockchain and the exact pair contract address. The pair address is the LP contract address, not the quote token address.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainYesChain identifier (e.g., solana, bsc, ethereum, base)
pair_addressYesThe pair contract address to look up

Implementation Reference

  • The MCP handler function (@app.call_tool()) that implements the tool execution logic for all tools, including dexscreenertokeninfoagent_get_specific_pair_info. It looks up the tool in the registry and proxies the call to the Mesh API.
    @app.call_tool() async def call_tool(name: str, arguments: dict) -> List[types.TextContent]: """Call the specified tool with the given arguments.""" try: if name not in self.tool_registry: raise ValueError(f"Unknown tool: {name}") tool_info = self.tool_registry[name] result = await self.execute_tool( agent_id=tool_info["agent_id"], tool_name=tool_info["tool_name"], tool_arguments=arguments, ) # Convert result to TextContent return [types.TextContent(type="text", text=str(result))] except Exception as e: logger.error(f"Error calling tool {name}: {e}") raise ValueError(f"Failed to call tool {name}: {str(e)}") from e
  • The MCP list_tools() registration that exposes all tools from the registry, including the target tool.
    @app.list_tools() async def list_tools() -> List[types.Tool]: """List all available tools.""" return [ types.Tool( name=tool_id, description=tool_info["description"], inputSchema=tool_info["parameters"], ) for tool_id, tool_info in self.tool_registry.items() ]
  • Dynamically builds the tool_registry by fetching agent metadata and constructing tool names as '{agent_id.lower()}_{tool_name}', enabling 'dexscreenertokeninfoagent_get_specific_pair_info' from 'DexScreenerTokenInfoAgent' agent.
    async def process_tool_metadata(self) -> Dict[str, Dict[str, Any]]: """Process agent metadata and extract tool information. Returns: Dictionary mapping tool IDs to tool information """ agents_metadata = await self.fetch_agent_metadata() tool_registry = {} # Log filtering status if self.supported_agents is not None: logger.info( f"Filtering tools using supported agent list ({len(self.supported_agents)} agents specified)" ) else: logger.info("Loading tools from all available agents (no filter applied)") for agent_id, agent_data in agents_metadata.items(): # Skip agents not in our supported list (if a list is specified) if ( self.supported_agents is not None and agent_id not in self.supported_agents ): continue # Process tools for this agent for tool in agent_data.get("tools", []): if tool.get("type") == "function": function_data = tool.get("function", {}) tool_name = function_data.get("name") if not tool_name: continue # Create a unique tool ID tool_id = f"{agent_id.lower()}_{tool_name}" # Get parameters or create default schema parameters = function_data.get("parameters", {}) if not parameters: parameters = { "type": "object", "properties": {}, "required": [], } # Store tool info tool_registry[tool_id] = { "agent_id": agent_id, "tool_name": tool_name, "description": function_data.get("description", ""), "parameters": parameters, } # Log which agents contributed tools agents_with_tools = set(info["agent_id"] for info in tool_registry.values()) logger.info(f"Loaded tools from agents: {', '.join(sorted(agents_with_tools))}") logger.info(f"Successfully loaded {len(tool_registry)} tools") return tool_registry
  • Configuration listing supported agents, explicitly including 'DexScreenerTokenInfoAgent' which provides the 'get_specific_pair_info' tool.
    # Default supported agents DEFAULT_AGENTS = [ "CoinGeckoTokenInfoAgent", "DexScreenerTokenInfoAgent", "ElfaTwitterIntelligenceAgent", "ExaSearchAgent", "TwitterInfoAgent", "AIXBTProjectInfoAgent", "EtherscanAgent", "EvmTokenInfoAgent", "FundingRateAgent", "UnifaiTokenAnalysisAgent", "YahooFinanceAgent", "ZerionWalletAnalysisAgent" ]
  • Helper function that proxies the tool call to the Mesh API endpoint using the agent's tool.
    async def execute_tool( self, agent_id: str, tool_name: str, tool_arguments: Dict[str, Any] ) -> Dict[str, Any]: """Execute a tool on a mesh agent. Args: agent_id: ID of the agent to execute the tool on tool_name: Name of the tool to execute tool_arguments: Arguments to pass to the tool Returns: Tool execution result Raises: ToolExecutionError: If there's an error executing the tool """ request_data = { "agent_id": agent_id, "input": {"tool": tool_name, "tool_arguments": tool_arguments}, } # Add API key if available if Config.HEURIST_API_KEY: request_data["api_key"] = Config.HEURIST_API_KEY try: result = await call_mesh_api( "mesh_request", method="POST", json=request_data ) return result.get("data", result) # Prefer the 'data' field if it exists except MeshApiError as e: # Re-raise API errors with clearer context raise ToolExecutionError(str(e)) from e except Exception as e: logger.error(f"Error calling {agent_id} tool {tool_name}: {e}") raise ToolExecutionError( f"Failed to call {agent_id} tool {tool_name}: {str(e)}" ) from e

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/heurist-network/heurist-mesh-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server