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elfatwitterintelligenceagent_search_account

Analyze Twitter accounts by retrieving engagement metrics, follower growth, and influential mentions. Identify topics and cryptocurrencies frequently discussed with data from ELFA API. Input a username and specify historical activity duration for actionable insights.

Instructions

Search for a Twitter account with both mention search and account statistics. This tool provides engagement metrics, follower growth, and mentions by smart users. It does not contain all tweets, but only those of influential users. It also identifies the topics and cryptocurrencies they frequently discuss. Data comes from ELFA API and can analyze several weeks of historical activity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
days_agoNoNumber of days to look back for mentions
limitNoMaximum number of mention results
usernameYesTwitter username to analyze (without @)

Implementation Reference

  • MCP handler that executes the tool 'elfatwitterintelligenceagent_search_account' by extracting agent_id='ElfaTwitterIntelligenceAgent' and tool_name='search_account' from the name and proxying to the Mesh API endpoint.
    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
  • Dynamically registers tools by fetching agent metadata, filtering by supported agents including 'ElfaTwitterIntelligenceAgent', and creating tool IDs like 'elfatwitterintelligenceagent_search_account' for tools named 'search_account'.
    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
  • Registers the tool schema (inputSchema from metadata) and description for listing, including for 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()
        ]
  • Helper function that performs the actual API call to execute the tool on the remote agent.
    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
  • Configuration list of supported agents that enables 'ElfaTwitterIntelligenceAgent', allowing its tools like 'search_account' to be registered as 'elfatwitterintelligenceagent_search_account'.
    # Default supported agents
    DEFAULT_AGENTS = [
        "CoinGeckoTokenInfoAgent",
        "DexScreenerTokenInfoAgent",
        "ElfaTwitterIntelligenceAgent",
        "ExaSearchAgent",
        "TwitterInfoAgent",
        "AIXBTProjectInfoAgent",
        "EtherscanAgent",
        "EvmTokenInfoAgent",
        "FundingRateAgent",
        "UnifaiTokenAnalysisAgent",
        "YahooFinanceAgent",
        "ZerionWalletAnalysisAgent"
    ]
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses key behavioral traits: data comes from ELFA API, analyzes several weeks of historical activity, and limits to influential users' tweets. However, it omits critical details like rate limits, authentication needs, error handling, or whether it's read-only/destructive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately concise but could be more front-loaded. It starts with core purpose but includes some redundancy (e.g., 'both mention search and account statistics' could be streamlined). Every sentence adds value, but structure could better highlight key constraints upfront.

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 no annotations and no output schema, the description partially compensates by detailing outputs (metrics, topics, crypto discussions) and data sources. However, it lacks information on return format, pagination, error cases, or completeness of results, leaving gaps for a tool with 3 parameters and complex functionality.

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 100%, providing clear parameter documentation. The description adds no additional parameter semantics beyond what's in the schema, such as explaining how 'days_ago' interacts with 'influential users' filtering or default value implications.

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 searches for a Twitter account and provides specific outputs: engagement metrics, follower growth, mentions by smart users, topics, and cryptocurrency discussions. It distinguishes from sibling 'elfatwitterintelligenceagent_search_mentions' by focusing on account analysis rather than mentions search, though the distinction could be more explicit.

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 like 'elfatwitterintelligenceagent_search_mentions' or 'elfatwitterintelligenceagent_get_trending_tokens'. The description mentions data sources and scope (influential users, historical weeks), but lacks clear when-to-use or when-not-to-use directives.

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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