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MoralisWeb3

Moralis MCP Server

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by MoralisWeb3

evm_gettimeseriesvolume

Fetch time-series volume, liquidity, and FDV data for specific blockchains to analyze market trends and performance metrics.

Instructions

Fetch timeseries volume, liquidity and FDV for a specific blockchain.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNoThe chain to queryeth
timeframeYesThe timeframe to query7d

Implementation Reference

  • Generic handler function that provides the implementation for all extracted API tools. For 'evm_gettimeseriesvolume', it calls the corresponding Moralis EVM API endpoint based on the tool's pathTemplate, parameters, etc., from the OpenAPI spec.
    export async function executeApiTool(
      toolName: string,
      definition: McpToolDefinition,
      toolArgs: JsonObject,
      allSecuritySchemes: Record<string, any>,
      token?: string,
    ): Promise<CallToolResult> {
      try {
        // Validate arguments against the input schema
        let validatedArgs: JsonObject;
        try {
          const zodSchema = getZodSchemaFromJsonSchema(
            definition.inputSchema,
            toolName,
          );
          const argsToParse =
            typeof toolArgs === 'object' && toolArgs !== null ? toolArgs : {};
          validatedArgs = zodSchema.parse(argsToParse);
        } catch (error: unknown) {
          if (error instanceof ZodError) {
            const validationErrorMessage = `Invalid arguments for tool '${toolName}': ${error.errors.map((e) => `${e.path.join('.')} (${e.code}): ${e.message}`).join(', ')}`;
            return { content: [{ type: 'text', text: validationErrorMessage }] };
          } else {
            const errorMessage =
              error instanceof Error ? error.message : String(error);
            return {
              content: [
                {
                  type: 'text',
                  text: `Internal error during validation setup: ${errorMessage}`,
                },
              ],
            };
          }
        }
    
        // Prepare URL, query parameters, headers, and request body
        let urlPath = definition.pathTemplate;
        const queryParams: Record<string, any> = {};
        const headers: Record<string, string> = {
          Accept: 'application/json',
          'X-Moralis-Platform': 'MCP',
        };
        let requestBodyData: any = undefined;
    
        // Apply parameters to the URL path, query, or headers
        definition.executionParameters.forEach((param) => {
          const value = validatedArgs[param.name];
          if (typeof value !== 'undefined' && value !== null) {
            if (param.in === 'path') {
              urlPath = urlPath.replace(
                `{${param.name}}`,
                encodeURIComponent(String(value)),
              );
            } else if (param.in === 'query') {
              queryParams[param.name] = value;
            } else if (param.in === 'header') {
              headers[param.name.toLowerCase()] = String(value);
            }
          }
        });
    
        // Ensure all path parameters are resolved
        if (urlPath.includes('{')) {
          throw new Error(`Failed to resolve path parameters: ${urlPath}`);
        }
    
        // Construct the full URL
        const requestUrl = `${definition.baseUrl}${urlPath}`;
    
        // Handle request body if needed
        if (
          definition.requestBodyContentType &&
          typeof validatedArgs['requestBody'] !== 'undefined'
        ) {
          requestBodyData = validatedArgs['requestBody'];
          headers['content-type'] = definition.requestBodyContentType;
        }
    
        // Apply security requirements if available
        // Security requirements use OR between array items and AND within each object
        const appliedSecurity = definition.securityRequirements?.find((req) => {
          // Try each security requirement (combined with OR)
          return Object.entries(req).every(([schemeName, scopesArray]) => {
            const scheme = allSecuritySchemes[schemeName];
            if (!scheme) return false;
    
            // API Key security (header, query, cookie)
            if (scheme.type === 'apiKey') {
              return !!token || !!Config.MORALIS_API_KEY;
            }
    
            return false;
          });
        });
    
        // If we found matching security scheme(s), apply them
        if (appliedSecurity) {
          // Apply each security scheme from this requirement (combined with AND)
          for (const [schemeName, scopesArray] of Object.entries(appliedSecurity)) {
            const scheme = allSecuritySchemes[schemeName];
    
            // API Key security
            if (scheme?.type === 'apiKey') {
              const apiKey = token || Config.MORALIS_API_KEY;
              if (apiKey) {
                if (scheme.in === 'header') {
                  headers[scheme.name.toLowerCase()] = apiKey;
                  console.error(
                    `Applied API key '${schemeName}' in header '${scheme.name}'`,
                  );
                }
              }
            }
          }
        }
        // Log warning if security is required but not available
        else if (definition.securityRequirements?.length > 0) {
          // First generate a more readable representation of the security requirements
          const securityRequirementsString = definition.securityRequirements
            .map((req) => {
              const parts = Object.entries(req)
                .map(([name, scopesArray]) => {
                  const scopes = scopesArray as string[];
                  if (scopes.length === 0) return name;
                  return `${name} (scopes: ${scopes.join(', ')})`;
                })
                .join(' AND ');
              return `[${parts}]`;
            })
            .join(' OR ');
    
          console.warn(
            `Tool '${toolName}' requires security: ${securityRequirementsString}, but no suitable credentials found.`,
          );
        }
    
        // Prepare the axios request configuration
        const config: AxiosRequestConfig = {
          method: definition.method.toUpperCase(),
          url: requestUrl,
          params: queryParams,
          headers: headers,
          ...(requestBodyData !== undefined && { data: requestBodyData }),
        };
    
        // Log request info to stderr (doesn't affect MCP output)
        console.error(
          `Executing tool "${toolName}": ${config.method} ${config.url}`,
        );
    
        // Execute the request
        const response = await axios(config);
    
        // Process and format the response
        let responseText = '';
        const contentType = response.headers['content-type']?.toLowerCase() || '';
    
        // Handle JSON responses
        if (
          contentType.includes('application/json') &&
          typeof response.data === 'object' &&
          response.data !== null
        ) {
          try {
            responseText = JSON.stringify(response.data, null, 2);
          } catch (e) {
            responseText = '[Stringify Error]';
          }
        }
        // Handle string responses
        else if (typeof response.data === 'string') {
          responseText = response.data;
        }
        // Handle other response types
        else if (response.data !== undefined && response.data !== null) {
          responseText = String(response.data);
        }
        // Handle empty responses
        else {
          responseText = `(Status: ${response.status} - No body content)`;
        }
    
        // Return formatted response
        return {
          content: [
            {
              type: 'text',
              text: `API Response (Status: ${response.status}):\n${responseText}`,
            },
          ],
        };
      } catch (error: unknown) {
        // Handle errors during execution
        let errorMessage: string;
    
        // Format Axios errors specially
        if (axios.isAxiosError(error)) {
          errorMessage = formatApiError(error);
        }
        // Handle standard errors
        else if (error instanceof Error) {
          errorMessage = error.message;
        }
        // Handle unexpected error types
        else {
          errorMessage = `Unexpected error: ${String(error)}`;
        }
    
        // Log error to stderr
        console.error(
          `Error during execution of tool '${toolName}':`,
          errorMessage,
        );
    
        // Return error message to client
        return { content: [{ type: 'text', text: errorMessage }] };
      }
    }
  • src/server.ts:66-72 (registration)
    Builds the toolDefinitionMap by extracting tools from OpenAPI specs using configs including EVM_CONFIG (prefix 'evm_'), adding 'evm_gettimeseriesvolume' to the map used for ListTools and CallTool handlers.
    let toolDefinitionMap: Record<string, McpToolDefinition> = {};
    for (const config of configArray)
      toolDefinitionMap = {
        ...toolDefinitionMap,
        ...(await mapToolDefinitions(config)),
      };
  • src/server.ts:47-51 (registration)
    Default server setup registers EVM and Solana tool sets, including the EVM tools from Moralis spec.
      config: SchemaConfig | SchemaConfig[] = [
        Config.EVM_CONFIG,
        Config.SOL_CONFIG,
      ],
    ) {
  • Extracts tool definitions from OpenAPI document, generating MCP inputSchema for 'evm_gettimeseriesvolume' from the 'gettimeseriesvolume' operation's parameters and requestBody.
    export function extractToolsFromApi(
      api: OpenAPIV3DocumentX,
      prefix?: string
    ): McpToolDefinition[] {
      const tools: McpToolDefinition[] = [];
      const usedNames = new Set<string>();
      const globalSecurity = api.security || [];
    
      if (!api.paths) return tools;
    
      for (const [path, pathItem] of Object.entries(api.paths)) {
        if (!pathItem) continue;
    
        for (const method of Object.values(OpenAPIV3.HttpMethods)) {
          const operation = pathItem[method];
          if (!operation) continue;
    
          // Generate a unique name for the tool
          let baseName = operation.operationId || generateOperationId(method, path);
          if (!baseName) continue;
    
          // Sanitize the name to be MCP-compatible (only a-z, 0-9, _, -)
          baseName = baseName
            .replace(/\./g, '_')
            .replace(/[^a-z0-9_-]/gi, '_')
            .toLowerCase();
    
          let finalToolName = baseName;
          let counter = 1;
          while (usedNames.has(finalToolName)) {
            finalToolName = `${baseName}_${counter++}`;
          }
          usedNames.add(finalToolName);
    
          // Get or create a description
          const description =
            operation.description ||
            operation.summary ||
            `Executes ${method.toUpperCase()} ${path}`;
    
          const prompt = operation['x-mcp-prompt'];
    
          // Generate input schema and extract parameters
          const { inputSchema, parameters, requestBodyContentType } =
            generateInputSchemaAndDetails(operation);
    
          // Extract parameter details for execution
          const executionParameters = parameters.map((p) => ({
            name: p.name,
            in: p.in,
          }));
    
          // Determine security requirements
          const securityRequirements =
            operation.security === null
              ? globalSecurity
              : operation.security || globalSecurity;
    
          // Create the tool definition
          tools.push({
            name: prefix + finalToolName,
            description,
            inputSchema,
            method,
            pathTemplate: path,
            parameters,
            executionParameters,
            requestBodyContentType,
            securityRequirements,
            operationId: baseName,
            prompt,
          });
        }
      }
    
      return tools;
    }
  • EVM configuration defining the prefix 'evm_' and URLs for the Moralis API spec and base, enabling extraction and execution of 'evm_gettimeseriesvolume'.
    evm:  {
      prefix: 'evm_',
      baseUrl:
        process.env.API_BASE_URL || 'https://deep-index.moralis.io/api/v2.2',
      specUrl:
        process.env.API_SPEC_URL ||
        'https://deep-index.moralis.io/api-docs-2.2/v2.2/swagger.json',
    },
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions fetching data but fails to describe critical traits like rate limits, authentication needs, error handling, or the format of returned data (e.g., time intervals, units). This leaves significant gaps in understanding how the tool behaves in practice.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized, making it easy for an agent to parse quickly and understand the core function.

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 complexity of fetching timeseries financial data (volume, liquidity, FDV), no annotations, and no output schema, the description is incomplete. It lacks details on data format, potential limitations, or how results are structured, which is crucial for an agent to effectively use this tool in real-world scenarios.

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, clearly documenting both parameters ('chain' and 'timeframe') with enums and defaults. The description adds no additional semantic context beyond implying blockchain specificity, so it meets the baseline score of 3, as the schema adequately handles parameter documentation.

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 ('Fetch') and the specific data types ('timeseries volume, liquidity and FDV') for a blockchain, which distinguishes it from general data-fetching tools. However, it doesn't explicitly differentiate from similar sibling tools like 'evm_gettimeseriestokenanalytics' or 'evm_gettimeseriesvolumebycategory', which might handle related but different data queries.

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, such as other timeseries or volume-related tools in the sibling list. It lacks context about prerequisites, typical use cases, or comparisons to similar tools like 'evm_getvolumestatsbycategory', leaving the agent without clear usage direction.

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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