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simonl77

Salesforce MCP Server

by simonl77

salesforce_dml_records

Insert, update, delete, or upsert Salesforce records using DML operations to manage data across objects like Accounts and Cases.

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, formats results with success counts and detailed error messages.
    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 specification including name, description, and input schema defining parameters for DML operations.
    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 the salesforce_dml_records tool (via DML_RECORDS) in the listTools endpoint response.
    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 dispatcher for salesforce_dml_records that validates input arguments and invokes the handleDMLRecords handler.
    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);
    }
  • TypeScript interface defining the expected arguments for the DML handler, used for validation.
    export interface DMLArgs {
      operation: 'insert' | 'update' | 'delete' | 'upsert';
      objectName: string;
      records: Record<string, any>[];
      externalIdField?: string;
    }
Behavior3/5

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

With no annotations provided, the description carries full burden. It clearly indicates this is a write/mutation tool (insert, update, delete, upsert) and specifies some requirements (Id for update/delete, external ID for upsert). However, it doesn't mention permission requirements, transaction behavior, error handling, or rate limits that would be important 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.

Conciseness4/5

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

The description is well-structured with a clear opening statement followed by bullet points and examples. It's appropriately sized for a multi-operation tool, though the bullet format could be slightly more concise.

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 no annotations and no output schema, the description provides good operational clarity but lacks important context about permissions, transactional behavior, error responses, and what happens on partial failures. The examples help but don't fully compensate for missing behavioral details.

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%, so the schema already documents all parameters well. The description adds some context by explaining what each operation type does and providing examples, but doesn't add significant semantic value beyond what's in the schema descriptions.

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' and lists specific operations (insert, update, delete, upsert) with concrete examples. It distinguishes itself from sibling tools like query or describe tools by focusing on write 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 implicit guidance by listing operation types and their requirements (e.g., 'update requires Id', 'delete requires Id', 'upsert based on external ID field'). However, it doesn't explicitly state when to use this tool versus alternatives like salesforce_write_apex or provide exclusion criteria.

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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