Salesforce Analytics & SOQL MCP Server for Pydantic AI 6 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Salesforce Analytics & SOQL through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Salesforce Analytics & SOQL "
"(6 tools)."
),
)
result = await agent.run(
"What tools are available in Salesforce Analytics & SOQL?"
)
print(result.data)
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 Analytics & SOQL MCP Server
The most powerful Salesforce integration — raw SOQL access to any data in your org.
Pydantic AI validates every Salesforce Analytics & SOQL tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- SOQL — Run any query against standard or custom objects
- Reports — List and execute Salesforce reports
- Dashboards — View dashboard components and data
- Counts — Get record counts for any object
The Salesforce Analytics & SOQL MCP Server exposes 6 tools through the Vinkius. Connect it to Pydantic AI 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 Analytics & SOQL to Pydantic AI via MCP
Follow these steps to integrate the Salesforce Analytics & SOQL MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 6 tools from Salesforce Analytics & SOQL with type-safe schemas
Why Use Pydantic AI with the Salesforce Analytics & SOQL MCP Server
Pydantic AI provides unique advantages when paired with Salesforce Analytics & SOQL through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Salesforce Analytics & SOQL integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Salesforce Analytics & SOQL connection logic from agent behavior for testable, maintainable code
Salesforce Analytics & SOQL + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Salesforce Analytics & SOQL MCP Server delivers measurable value.
Type-safe data pipelines: query Salesforce Analytics & SOQL with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Salesforce Analytics & SOQL tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Salesforce Analytics & SOQL and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Salesforce Analytics & SOQL responses and write comprehensive agent tests
Salesforce Analytics & SOQL MCP Tools for Pydantic AI (6)
These 6 tools become available when you connect Salesforce Analytics & SOQL to Pydantic AI via MCP:
sf_get_dashboard
Each component includes its type, header, data values, and the source report. Dashboards provide pre-built visual summaries. Get the dashboard ID from sf_list_dashboards. Use when the user asks "show me the sales dashboard" or wants a visual summary of specific metrics. Get all component data from a specific Salesforce dashboard — charts, metrics, and tables with their underlying data
sf_list_dashboards
Returns dashboard title, folder, last referenced date, and dashboard ID. Dashboards contain visual components (charts, tables, metrics) built from reports. Use to find dashboard IDs before fetching their component data with sf_get_dashboard. List available Salesforce dashboards with title, folder, and last referenced date to discover visualization assets
sf_list_reports
Returns report name, folder path, report format (Tabular/Summary/Matrix), last run date, and report ID. Use when the user asks about available reports, wants to find a specific one, or needs report IDs before running them with sf_run_report. List available Salesforce reports with name, folder, format, and last run date to discover reporting assets
sf_record_count
Returns the total number of records. Use for quick data volume checks: "how many leads do we have?", "total number of accounts", or capacity planning. Accepts any standard or custom object API name. Get the total record count for any Salesforce object — Account, Contact, Lead, Opportunity, Case, or any custom object
sf_run_report
Get the report ID from sf_list_reports first. This is the read-only equivalent of clicking "Run Report" in the Salesforce UI. Returns up to 2000 rows. Use when the user asks for specific report data or wants to extract insights from a saved report. Execute a specific Salesforce report by ID and return its full data — all rows, columns, and groupings
sf_run_soql
This is the most powerful and flexible tool — you can query any standard or custom object, apply filters, use aggregate functions, and join related objects. Example: SELECT Id, Name, Amount, StageName FROM Opportunity WHERE StageName = 'Closed Won' AND Amount > 10000 LIMIT 10. Use when no other tool covers the specific data need, or when the user requests a custom query. Execute a raw SOQL query against your Salesforce org to retrieve any data from standard or custom objects
Example Prompts for Salesforce Analytics & SOQL in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Salesforce Analytics & SOQL immediately.
"What was our total revenue closed this quarter?"
"Show me all reports in the Sales folder"
"How many accounts do we have by industry?"
Troubleshooting Salesforce Analytics & SOQL MCP Server with Pydantic AI
Common issues when connecting Salesforce Analytics & SOQL to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiSalesforce Analytics & SOQL + Pydantic AI FAQ
Common questions about integrating Salesforce Analytics & SOQL MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect Salesforce Analytics & SOQL with your favorite client
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Connect Salesforce Analytics & SOQL to Pydantic AI
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.