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tsmztech

Salesforce MCP Server

salesforce_dml_records

Perform data manipulation operations on Salesforce records: insert new records, update existing ones, delete records, or upsert using external IDs for Account, Case, and other object management.

Instructions

Perform data manipulation operations on Salesforce records:

  • insert: Create new records

  • update: Modify existing records (requires Id)

  • delete: Remove records (requires Id)

  • upsert: Insert or update based on external ID field Examples: Insert new Accounts, Update Case status, Delete old records, Upsert based on custom external ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
operationYesType of DML operation to perform
objectNameYesAPI name of the object
recordsYesArray of records to process
externalIdFieldNoExternal ID field name for upsert operations

Implementation Reference

  • Core handler function executing DML operations (insert, update, delete, upsert) on Salesforce records using jsforce connection, with detailed error formatting.
    export async function handleDMLRecords(conn: any, args: DMLArgs) {
      const { operation, objectName, records, externalIdField } = args;
    
      let result: DMLResult | DMLResult[];
      
      switch (operation) {
        case 'insert':
          result = await conn.sobject(objectName).create(records);
          break;
        case 'update':
          result = await conn.sobject(objectName).update(records);
          break;
        case 'delete':
          result = await conn.sobject(objectName).destroy(records.map(r => r.Id));
          break;
        case 'upsert':
          if (!externalIdField) {
            throw new Error('externalIdField is required for upsert operations');
          }
          result = await conn.sobject(objectName).upsert(records, externalIdField);
          break;
        default:
          throw new Error(`Unsupported operation: ${operation}`);
      }
    
      // Format DML results
      const results = Array.isArray(result) ? result : [result];
      const successCount = results.filter(r => r.success).length;
      const failureCount = results.length - successCount;
    
      let responseText = `${operation.toUpperCase()} operation completed.\n`;
      responseText += `Processed ${results.length} records:\n`;
      responseText += `- Successful: ${successCount}\n`;
      responseText += `- Failed: ${failureCount}\n\n`;
    
      if (failureCount > 0) {
        responseText += 'Errors:\n';
        results.forEach((r: DMLResult, idx: number) => {
          if (!r.success && r.errors) {
            responseText += `Record ${idx + 1}:\n`;
            if (Array.isArray(r.errors)) {
              r.errors.forEach((error) => {
                responseText += `  - ${error.message}`;
                if (error.statusCode) {
                  responseText += ` [${error.statusCode}]`;
                }
                if (error.fields && error.fields.length > 0) {
                  responseText += `\n    Fields: ${error.fields.join(', ')}`;
                }
                responseText += '\n';
              });
            } else {
              // Single error object
              const error = r.errors;
              responseText += `  - ${error.message}`;
              if (error.statusCode) {
                responseText += ` [${error.statusCode}]`;
              }
              if (error.fields) {
                const fields = Array.isArray(error.fields) ? error.fields.join(', ') : error.fields;
                responseText += `\n    Fields: ${fields}`;
              }
              responseText += '\n';
            }
          }
        });
      }
    
      return {
        content: [{
          type: "text",
          text: responseText
        }],
        isError: false,
      };
    }
  • Tool definition including name, description, and input schema for validating DML operation parameters.
    export const DML_RECORDS: Tool = {
      name: "salesforce_dml_records",
      description: `Perform data manipulation operations on Salesforce records:
      - insert: Create new records
      - update: Modify existing records (requires Id)
      - delete: Remove records (requires Id)
      - upsert: Insert or update based on external ID field
      Examples: Insert new Accounts, Update Case status, Delete old records, Upsert based on custom external ID`,
      inputSchema: {
        type: "object",
        properties: {
          operation: {
            type: "string",
            enum: ["insert", "update", "delete", "upsert"],
            description: "Type of DML operation to perform"
          },
          objectName: {
            type: "string",
            description: "API name of the object"
          },
          records: {
            type: "array",
            items: { type: "object" },
            description: "Array of records to process"
          },
          externalIdField: {
            type: "string",
            description: "External ID field name for upsert operations",
            optional: true
          }
        },
        required: ["operation", "objectName", "records"]
      }
    };
  • src/index.ts:45-63 (registration)
    Registers DML_RECORDS (salesforce_dml_records) in the tools list returned by ListToolsRequest.
    server.setRequestHandler(ListToolsRequestSchema, async () => ({
      tools: [
        SEARCH_OBJECTS, 
        DESCRIBE_OBJECT, 
        QUERY_RECORDS, 
        AGGREGATE_QUERY,
        DML_RECORDS,
        MANAGE_OBJECT,
        MANAGE_FIELD,
        MANAGE_FIELD_PERMISSIONS,
        SEARCH_ALL,
        READ_APEX,
        WRITE_APEX,
        READ_APEX_TRIGGER,
        WRITE_APEX_TRIGGER,
        EXECUTE_ANONYMOUS,
        MANAGE_DEBUG_LOGS
      ],
    }));
  • src/index.ts:119-131 (registration)
    Switch case in CallToolRequest handler that validates arguments and dispatches to handleDMLRecords for salesforce_dml_records.
    case "salesforce_dml_records": {
      const dmlArgs = args as Record<string, unknown>;
      if (!dmlArgs.operation || !dmlArgs.objectName || !Array.isArray(dmlArgs.records)) {
        throw new Error('operation, objectName, and records array are required for DML');
      }
      const validatedArgs: DMLArgs = {
        operation: dmlArgs.operation as 'insert' | 'update' | 'delete' | 'upsert',
        objectName: dmlArgs.objectName as string,
        records: dmlArgs.records as Record<string, any>[],
        externalIdField: dmlArgs.externalIdField as string | undefined
      };
      return await handleDMLRecords(conn, validatedArgs);
    }
Behavior3/5

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 does well by specifying requirements for each operation (Id for update/delete, external ID for upsert) and listing concrete examples. However, it doesn't mention important behavioral aspects like transaction boundaries, error handling, permission requirements, or rate limits that would be crucial for a DML tool.

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 efficiently structured with a clear opening statement followed by bullet points for each operation type and specific examples. Every sentence earns its place by providing essential information without redundancy. The bullet format makes it easy to scan and understand.

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?

For a DML tool with 4 parameters, no annotations, and no output schema, the description does a reasonable job but has gaps. It explains the operations well but doesn't cover important contextual information like response format, error conditions, transaction behavior, or Salesforce-specific constraints that would be needed for complete understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the baseline is 3. The description adds significant value by explaining what each operation type means in practical terms (e.g., 'upsert: Insert or update based on external ID field') and providing concrete examples that help understand how parameters like 'records' should be structured, elevating it above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs data manipulation operations on Salesforce records, specifying four distinct operations (insert, update, delete, upsert) with concrete examples. It distinguishes itself from sibling tools like query or describe tools by focusing on DML operations.

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

Usage Guidelines4/5

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

The description provides clear context for when to use each operation type (e.g., 'update: Modify existing records (requires Id)', 'upsert: Insert or update based on external ID field'), but doesn't explicitly state when NOT to use this tool versus alternatives like salesforce_query_records or salesforce_write_apex. The examples help illustrate appropriate use cases.

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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