Salesforce AI Agent MCP Server
Provides tools for managing Jira Cloud stories, including fetching, updating status, posting comments, and searching via JQL.
Provides tools for triggering n8n workflows and checking execution status.
Provides tools for interacting with Salesforce Tooling and Metadata APIs, including managing custom fields, validation rules, deploying metadata, and querying with SOQL.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Salesforce AI Agent MCP Server Read Jira story ABC-123 and deploy custom field to Salesforce"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Salesforce AI Agent MCP Server
A production-grade Model Context Protocol (MCP) server that gives Claude direct, structured access to Jira Cloud, Salesforce (Tooling + Metadata APIs), and n8n workflows.
Built for the Salesforce Developer AI Agent project — an automation pipeline that reads Jira stories, interprets Salesforce configuration requirements, and deploys metadata (custom fields, validation rules, etc.) directly into Salesforce.
Architecture
Claude Desktop / Claude Code / VS Code
│ (stdio or SSE)
▼
salesforce-ai-agent-mcp
┌───────────────────────────┐
│ Tools (12 total) │
│ ├── Jira (4 tools) │──► Jira Cloud REST API v3
│ ├── Salesforce (6 tools) │──► Salesforce Tooling + Metadata APIs
│ └── n8n (2 tools) │──► n8n Webhook + REST API
└───────────────────────────┘Related MCP server: n8n MCP Server
Prerequisites
Node.js ≥ 18
npm ≥ 9
A Jira Cloud account with API token
A Salesforce org with a Connected App (OAuth 2.0)
An n8n instance (optional, for workflow orchestration)
Installation
cd salesforce-ai-agent-mcp
npm install
npm run buildConfiguration
Copy .env.example to .env and fill in your credentials:
cp .env.example .envJira
Variable | Description |
| e.g. |
| Your Atlassian account email |
| Generate at Atlassian API Tokens |
Salesforce
You need a Connected App with the Username-Password OAuth flow enabled.
In Salesforce Setup → App Manager → New Connected App
Enable OAuth settings
Add scope:
api,refresh_tokenCopy Consumer Key →
SF_CLIENT_IDCopy Consumer Secret →
SF_CLIENT_SECRET
Variable | Description |
|
|
| Connected App Consumer Key |
| Connected App Consumer Secret |
| Your Salesforce username |
| Your Salesforce password |
| Reset at Setup → Personal Information → Reset Security Token |
| API version, e.g. |
n8n
Variable | Description |
| Your n8n instance URL, e.g. |
| Generate at n8n Settings → API → Create API Key |
Usage
stdio mode (Claude Desktop / Claude Code / VS Code)
npm start
# or for development:
npm run devSSE mode (for remote/n8n integration)
npm run start:sse
# or for development:
npm run dev:sseEndpoints available in SSE mode:
GET http://localhost:3000/sse— clients connect herePOST http://localhost:3000/messages?sessionId=<id>— clients send messages hereGET http://localhost:3000/health— liveness probe
Override the port:
MCP_PORT=8080 npm run start:sseConnecting to Claude Desktop
Add the following to your claude_desktop_config.json:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"salesforce-ai-agent": {
"command": "node",
"args": ["/absolute/path/to/salesforce-ai-agent-mcp/dist/index.js"],
"env": {
"JIRA_BASE_URL": "https://your-org.atlassian.net",
"JIRA_EMAIL": "you@company.com",
"JIRA_API_TOKEN": "your_token",
"SF_LOGIN_URL": "https://login.salesforce.com",
"SF_CLIENT_ID": "your_client_id",
"SF_CLIENT_SECRET": "your_client_secret",
"SF_USERNAME": "you@yourorg.com",
"SF_PASSWORD": "yourpassword",
"SF_SECURITY_TOKEN": "yourSecurityToken",
"N8N_BASE_URL": "https://your-n8n.com",
"N8N_API_KEY": "your_n8n_api_key"
}
}
}
}Connecting to Claude Code (VS Code Extension)
Add to your VS Code settings.json (or via the MCP extension UI):
{
"mcp.servers": {
"salesforce-ai-agent": {
"command": "node",
"args": ["${workspaceFolder}/salesforce-ai-agent-mcp/dist/index.js"],
"env": {
"JIRA_BASE_URL": "https://your-org.atlassian.net",
"JIRA_EMAIL": "you@company.com",
"JIRA_API_TOKEN": "your_token",
"SF_LOGIN_URL": "https://login.salesforce.com",
"SF_CLIENT_ID": "your_client_id",
"SF_CLIENT_SECRET": "your_client_secret",
"SF_USERNAME": "you@yourorg.com",
"SF_PASSWORD": "yourpassword",
"SF_SECURITY_TOKEN": "yourSecurityToken",
"N8N_BASE_URL": "https://your-n8n.com",
"N8N_API_KEY": "your_n8n_api_key"
}
}
}
}Alternatively, the mcp.json file at the root of this project is compatible with VS Code's MCP extension and will be auto-detected if placed in your workspace root.
Available Tools (12 total)
Jira Tools
jira_get_story
Fetch a Jira story by issue key. Returns summary, description, acceptance criteria, status, labels, and all custom fields.
issueKey: "SFDC-123"jira_update_story_status
Transition a Jira story to a new workflow status.
issueKey: "SFDC-123"
targetStatus: "In Progress" # must match an available transitionjira_post_comment
Post a comment to a Jira story (used by the AI agent to report results or ask for clarification).
issueKey: "SFDC-123"
body: "Deployed Customer_Tier__c field to Account. Deployment ID: 0Af..."jira_search_stories
JQL-based story search.
jql: "label = \"sf-config\" AND status = \"To Do\" AND project = SFDC"
maxResults: 25Salesforce Tools
salesforce_get_object_fields
Describe all fields on a Salesforce object.
objectName: "Account"salesforce_create_custom_field
Create a custom field via the Tooling API.
objectName: "Account"
fieldLabel: "Customer Tier"
fieldApiName: "Customer_Tier" # __c added automatically
fieldType: "Picklist"
picklistValues: ["Platinum", "Gold", "Silver", "Bronze"]
description: "Tier classification for account segmentation"Lookup field example:
objectName: "Case"
fieldLabel: "Related Contract"
fieldApiName: "Related_Contract"
fieldType: "Lookup"
referenceTo: "Contract"salesforce_create_validation_rule
Create a validation rule via the Tooling API.
objectName: "Account"
ruleName: "Require_Phone_For_Hot_Leads"
errorConditionFormula: "AND(Rating = \"Hot\", ISBLANK(Phone))"
errorMessage: "Phone number is required for Hot-rated accounts"
errorDisplayField: "Phone"
active: truesalesforce_deploy_metadata
Trigger a metadata deployment from a base64-encoded ZIP.
zipFile: "<base64-encoded-zip>"
checkOnly: false
testLevel: "RunLocalTests"
rollbackOnError: truesalesforce_get_deployment_status
Poll a deployment's status.
deploymentId: "0AfXXXXXXXXXXXXX"salesforce_query
Execute SOQL for validation and verification.
soql: "SELECT Id, Name, Customer_Tier__c FROM Account WHERE Rating = 'Hot' LIMIT 10"n8n Tools
n8n_trigger_workflow
Trigger an n8n workflow via webhook URL.
webhookUrl: "https://your-n8n.com/webhook/abc123"
payload: {
"issueKey": "SFDC-123",
"environment": "sandbox",
"triggeredBy": "claude"
}n8n_get_execution_status
Check the status of an n8n execution.
executionId: "12345"Example AI Agent Workflow
You: "Process Jira story SFDC-456 and deploy the Salesforce configuration"
Claude:
1. jira_get_story(issueKey: "SFDC-456")
→ reads requirements: "Add Customer_Tier picklist to Account"
2. salesforce_get_object_fields(objectName: "Account")
→ confirms Customer_Tier__c doesn't exist yet
3. salesforce_create_custom_field(
objectName: "Account",
fieldLabel: "Customer Tier",
fieldApiName: "Customer_Tier",
fieldType: "Picklist",
picklistValues: ["Platinum", "Gold", "Silver"]
)
→ creates the field
4. salesforce_query(soql: "SELECT QualifiedApiName FROM FieldDefinition WHERE EntityDefinition.QualifiedApiName = 'Account' AND QualifiedApiName = 'Customer_Tier__c'")
→ verifies the field was created
5. jira_post_comment(
issueKey: "SFDC-456",
body: "✅ Customer_Tier__c picklist field created on Account object with values: Platinum, Gold, Silver."
)
6. jira_update_story_status(issueKey: "SFDC-456", targetStatus: "Done")Logging
All logs are written as structured JSON to stderr so they never interfere with the MCP stdio transport.
Control the log level via environment variable:
LOG_LEVEL=debug npm start # debug | info | warn | errorDevelopment
# Type-check only
npm run typecheck
# Build
npm run build
# Dev with hot reload (stdio)
npm run dev
# Dev with hot reload (SSE)
npm run dev:sseProject Structure
salesforce-ai-agent-mcp/
├── src/
│ ├── index.ts # Entry point — stdio & SSE transport setup
│ ├── server.ts # McpServer creation & tool registration
│ ├── tools/
│ │ ├── jira.ts # Jira tool definitions (4 tools)
│ │ ├── salesforce.ts # Salesforce tool definitions (6 tools)
│ │ └── n8n.ts # n8n tool definitions (2 tools)
│ ├── clients/
│ │ ├── jiraClient.ts # Jira REST API v3 client
│ │ ├── salesforceClient.ts # Salesforce Tooling/Metadata API + OAuth
│ │ ├── n8nClient.ts # n8n webhook + REST API client
│ │ └── logger.ts # Structured JSON logger (stderr)
│ └── types/
│ └── index.ts # Shared TypeScript types
├── .env.example # All required environment variables
├── mcp.json # VS Code MCP extension manifest
├── package.json
├── tsconfig.json
└── README.mdAvailable Tools
12 toolsjira_get_storyA
Fetch a Jira story by issue key. Returns summary, description, acceptance criteria, status, labels, assignee, and all custom fields.
Inputs: issueKey (required).
jira_post_commentA
Post a comment to a Jira story. Used by the AI agent to document deployment results, ask for clarification, or provide status updates. Supports plain text with paragraphs separated by blank lines.
Inputs: body (required), issueKey (required).
jira_search_storiesA
Search Jira stories using JQL (Jira Query Language). Returns matching issues with full details. Example JQL: 'label = "sf-config" AND status = "To Do" AND project = SFDC ORDER BY created DESC'
Inputs: jql (required), startAt, maxResults.
jira_update_story_statusA
Transition a Jira story to a new status. The available transitions depend on the current workflow configuration. Common statuses: 'To Do', 'In Progress', 'In Review', 'Done', 'Blocked'.
Inputs: issueKey (required), targetStatus (required).
n8n_get_execution_statusA
Retrieve the current status and output data of an n8n workflow execution by its execution ID. Use this to poll whether a triggered workflow has completed successfully.
Inputs: executionId (required).
n8n_trigger_workflowA
Trigger a specific n8n workflow by its webhook URL, passing a JSON payload. This enables Claude to kick off the full Salesforce automation pipeline — for example, by sending a Jira issue key to start the analysis and deployment workflow.
Inputs: payload (required), webhookUrl (required), waitForResponse.
salesforce_create_custom_fieldB
Create a custom field on a Salesforce object using the Tooling API. Supports all standard field types including Text, Number, Picklist, Checkbox, Date, Lookup, and more.
Inputs: fieldType (required), fieldLabel (required), objectName (required), fieldApiName (required), +13 more.
salesforce_create_validation_ruleB
Create a validation rule on a Salesforce object using the Tooling API. The rule evaluates an error condition formula and displays an error message when the formula returns true.
Inputs: ruleName (required), objectName (required), errorMessage (required), errorConditionFormula (required), +3 more.
salesforce_deploy_metadataB
Trigger a Salesforce metadata deployment using the Metadata API. The metadata must be provided as a base64-encoded ZIP file containing a standard Salesforce metadata package.
Inputs: zipFile (required), runTests, checkOnly, testLevel, +2 more.
salesforce_get_deployment_statusA
Poll the status of a Salesforce metadata deployment by its deployment ID. Returns component counts, errors, and test results.
Inputs: deploymentId (required).
salesforce_get_object_fieldsA
Query all existing fields on a Salesforce object using the REST API describe endpoint. Returns field names, labels, types, lengths, and picklist values.
Inputs: objectName (required).
salesforce_queryA
Execute a SOQL query against Salesforce and return the results. Use this to verify field values after deployment, check existing records, or validate configuration.
Inputs: soql (required), maxRecords.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 12 tools
Each tool targets a distinct action on a specific resource: Jira stories, Salesforce metadata/objects, and n8n workflows. There is no overlap between tools like get_deployment_status and get_execution_status because they operate on different systems.
All tool names follow a consistent pattern of domain_verb_noun (e.g., jira_get_story, salesforce_create_custom_field, n8n_trigger_workflow). Even salesforce_query fits, as 'query' functions as both verb and noun without breaking the convention.
The 12 tools are well-scoped for a cross-platform integration server covering Jira, Salesforce, and n8n. Each tool serves a clear purpose with no redundancy, so the count feels appropriate for the domain.
The tool surface covers the core workflow: fetching Jira stories, searching, commenting, modifying Salesforce metadata, deploying, and triggering/polling n8n workflows. Minor gaps exist, such as no update/delete for validation rules or custom fields, but the main lifecycle is adequately covered.
Maintenance
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