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MoralisWeb3

Moralis MCP Server

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by MoralisWeb3

evm_gettimeseriesvolumebycategory

Fetch timeseries volume data including buy, sell, liquidity, and FDV for specific token categories across multiple blockchains. Filter by chain and timeframe to analyze market trends.

Instructions

Fetch timeseries buy volume, sell volume, liquidity and FDV for a specific category. Optionally filter by chain.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNoThe chain to queryeth
timeframeYesThe timeframe to query1d
categoryIdYesThe category id

Implementation Reference

  • The generic handler function `executeApiTool` that implements the core logic for all API-based tools, including `evm_gettimeseriesvolumebycategory`. It validates input, constructs HTTP request to Moralis API using tool definition (path, method, params, security), executes with axios, and formats response.
    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:96-120 (registration)
    MCP server registration of the CallTool handler, which looks up the tool definition by name (e.g., 'evm_gettimeseriesvolumebycategory') and delegates to the generic executeApiTool handler.
    server.setRequestHandler(
      CallToolRequestSchema,
      async (request: CallToolRequest, c): Promise<CallToolResult> => {
        const { name: toolName, arguments: toolArgs } = request.params;
        const toolDefinition = toolDefinitionMap[toolName];
        if (!toolDefinition) {
          console.error(`Error: Unknown tool requested: ${toolName}`);
          return {
            content: [
              {
                type: 'text',
                text: `Error: Unknown tool requested: ${toolName}`,
              },
            ],
          };
        }
        return executeApiTool(
          toolName,
          toolDefinition,
          toolArgs ?? {},
          securitySchemes,
          c.authInfo?.token,
        );
      },
    );
  • src/server.ts:85-94 (registration)
    MCP server registration of ListTools handler, which provides the dynamically generated schema and description for all tools including `evm_gettimeseriesvolumebycategory`.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      const toolsForClient: Tool[] = Object.values(toolDefinitionMap).map(
        (def) => ({
          name: def.name,
          description: def.description,
          inputSchema: def.inputSchema,
        }),
      );
      return { tools: toolsForClient };
    });
  • Loads OpenAPI spec for EVM/Solana, extracts tool definitions with prefix (e.g., 'evm_'), applies blacklist, stores in toolDefinitionMap used for registration and execution. This is where `evm_gettimeseriesvolumebycategory` gets registered dynamically.
    async function mapToolDefinitions(config: SchemaConfig) {
      const spec = (await getSpec(config.specUrl)) as OpenAPIV3DocumentX;
      const api = (await SwaggerParser.dereference(spec)) as OpenAPIV3DocumentX;
    
      const tools = extractToolsFromApi(api, config.prefix);
      const blacklist = Array.isArray(api['x-mcp-blacklist'])
        ? api['x-mcp-blacklist'].map((e) => `${config.prefix}${e.toLowerCase()}`)
        : [];
    
      const toolDefinitionMap: Record<string, McpToolDefinition> = {};
      for (const tool of tools) {
        if (blacklist.includes(tool.name)) continue;
        toolDefinitionMap[tool.name] = {
          ...tool,
          baseUrl: config.baseUrl,
        };
      }
      return toolDefinitionMap;
    }
  • Configuration for EVM tools, defining the 'evm_' prefix matching the tool name, base API URL, and OpenAPI spec URL from which the tool schemas and definitions are extracted.
    const configs: Record<string, SchemaConfig> = {
      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 states the tool fetches data (implying read-only), but doesn't describe response format, pagination, rate limits, authentication needs, or error conditions. For a timeseries query tool with zero annotation coverage, this leaves significant behavioral gaps.

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 front-loads the core purpose and includes the optional parameter note. Every word contributes directly to understanding the tool's function with zero wasted text.

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?

For a timeseries data fetching tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the returned data structure looks like, time granularity, how 'FDV' is calculated, or any limitations. The high schema coverage helps with inputs, but the overall context for proper tool invocation remains incomplete.

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%, with all parameters well-documented in the schema itself. The description adds minimal value by mentioning the optional 'chain' filter, but doesn't provide additional context about parameter interactions, default behaviors, or the meaning of 'categoryId' beyond what's in the schema.

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 returned ('timeseries buy volume, sell volume, liquidity and FDV'), with the target resource being 'a specific category'. It distinguishes from siblings like 'evm_gettimeseriesvolume' (which appears to be general volume) by specifying 'by category', but doesn't explicitly contrast with other category-related tools like 'evm_getvolumestatsbycategory'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when timeseries metrics for a category are needed, and mentions optional chain filtering. However, it doesn't provide explicit guidance on when to choose this tool over alternatives like 'evm_getvolumestatsbycategory' or 'evm_gettimeseriestokenanalytics', nor does it mention prerequisites or exclusions.

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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