Salesforce Service Cloud MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Salesforce Service Cloud as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Salesforce Service Cloud. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Salesforce Service Cloud?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Salesforce Service Cloud MCP Server
Connect Salesforce Service Cloud to any AI agent.
LlamaIndex agents combine Salesforce Service Cloud tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Cases — Search, create, update, and filter by status or priority
- Comments — Read and add internal/public case comments
- Knowledge — Search published knowledge articles for instant answers
- Metrics — Aggregate case counts by status and priority
The Salesforce Service Cloud MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Salesforce Service Cloud to LlamaIndex via MCP
Follow these steps to integrate the Salesforce Service Cloud MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from Salesforce Service Cloud
Why Use LlamaIndex with the Salesforce Service Cloud MCP Server
LlamaIndex provides unique advantages when paired with Salesforce Service Cloud through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Salesforce Service Cloud tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Salesforce Service Cloud tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Salesforce Service Cloud, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Salesforce Service Cloud tools were called, what data was returned, and how it influenced the final answer
Salesforce Service Cloud + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Salesforce Service Cloud MCP Server delivers measurable value.
Hybrid search: combine Salesforce Service Cloud real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Salesforce Service Cloud to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Salesforce Service Cloud for fresh data
Analytical workflows: chain Salesforce Service Cloud queries with LlamaIndex's data connectors to build multi-source analytical reports
Salesforce Service Cloud MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Salesforce Service Cloud to LlamaIndex via MCP:
sf_add_case_comment
Set isPublished to true if the comment should be visible to the customer (e.g., in a customer portal). Default is internal-only. Use to log agent responses, internal notes, or resolution steps on a support case. Add a comment to a Salesforce case — internal note or customer-visible response
sf_case_comments
Returns comment body, whether it is published (customer-visible), creator name, and creation date. Comments provide the full conversation history of a support case. Use to review case discussions or get context before responding. Get all comments (internal and customer-visible) on a specific Salesforce case for case history review
sf_case_metrics
Returns summary data: how many cases at each status × priority intersection. Perfect for support team dashboards, capacity planning, and identifying volume trends. Use when the user asks "how many open cases do we have?" or "what is the case breakdown by priority?" Get aggregate support case metrics — case counts grouped by status and priority for a team dashboard view
sf_cases_by_status
Returns cases sorted by priority then creation date. Use for support queue management: "how many new cases are there?", "show escalated cases", or for case workload analysis by status. Get all Salesforce cases at a specific status for queue analysis — New, Working, Escalated, or Closed
sf_create_case
Subject is required. Status defaults to "New". Priority: High, Medium, Low. Origin: Web, Phone, Email. Link to a customer via contactId and their company via accountId (both use 18-char Salesforce IDs). Cases track the complete lifecycle of a customer support issue. Create a new support case in Salesforce Service Cloud with subject, description, priority, origin, and linked contact/account
sf_search_cases
Returns case number, subject, status (New/Working/Escalated/Closed), priority (High/Medium/Low), origin channel (Web/Phone/Email), case owner, and description. Use when the user wants to find a specific support case, look up a case number, or review customer issues. Search Salesforce Service Cloud cases by subject or case number to find customer support issues
sf_search_knowledge
Returns article title, summary, URL, and article type. Salesforce Knowledge is the built-in KB for self-service and agent-assist. Use when the user asks for help articles, documented solutions, or wants to check if an issue has been addressed in the knowledge base. Search the Salesforce Knowledge Base for published articles to find documented solutions and answers
sf_update_case
Common operations: advance Status from "New" to "Working" to "Closed", escalate Priority to "High", or append to Description. Only specified fields change. Update a Salesforce case — change status, escalate priority, or add description to reflect case progress
Example Prompts for Salesforce Service Cloud in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Salesforce Service Cloud immediately.
"How many open P1 cases do we have?"
"Find a knowledge article about password reset"
"Create a high-priority case: Login page returning 500 error"
Troubleshooting Salesforce Service Cloud MCP Server with LlamaIndex
Common issues when connecting Salesforce Service Cloud to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSalesforce Service Cloud + LlamaIndex FAQ
Common questions about integrating Salesforce Service Cloud MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Salesforce Service Cloud with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Salesforce Service Cloud to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.