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Architecture Overview

An Army of Agents.
One Interface.

KineticMCP exposes Salesforce as a standardized Model Context Protocol (MCP) server. Any AI client can now "speak Salesforce" fluently.

01

The Agent Decides

You give a natural language goal to your agent (Claude, OpenAI, or local LLM). It understands the context and decides it needs to fetch data.

USER PROMPT
> Find all accounts in New York untouched for 30 days.
// Agent Analysis...
Need tool: kineticmcp
Function: semantic_search
Parameters: { "query": "accounts New York inactive 30 days" }
SERVER LOGS
[10:42:01] INFO Received intent from Agent.
[10:42:02] DEBUG Mapping "New York" -> BillingCity
[10:42:02] DEBUG Mapping "30 days" -> LastActivityDate < N_DAYS:30
GENERATED SOQL: SELECT Id, Name FROM Account WHERE BillingCity = 'New York' AND LastActivityDate < 2024-05-15
02

KineticMCP Translates

Our server receives the intent and converts it into safe, optimized SOQL queries and API calls. It checks permissions, schema compliance, and limits before execution.

03

Salesforce Executes

The action is executed via the official Salesforce API. Records are updated, tasks are created, and everything is logged. Invisible to the user, fully audited.

Operation Successful
200 OK • 1.2s latency

Output:

Found 12 Accounts.

Created 12 Tasks for Account Owners: "Re-engage Customer"

Under the hood

🔍

Semantic Search

We index your metadata. Agents know that "Annual Value" maps to `Amount` and "Closed" means `StageName = 'Closed Won'`, without you teaching them.

📦

Smart Bulk API

Need to update 5,000 records? KineticMCP automatically switches from REST to Bulk API to handle load efficiently and avoid limit hits.

🐳

Docker Native

Deploys as a single, self-contained container. No complex microservices. Works on any cloud (AWS, Azure, GCP) or strictly on-prem.

See it in Action.

Theory is good. Reality is better. Explore real-world scenarios where KineticMCP transforms business logic into automated reality.