MCP is the standard that lets Claude, ChatGPT, Cursor, and Lovable call your internal systems as tools. We build production MCP servers with RBAC, OAuth, audit logging, and real-world system integrations — deployed in 4–8 weeks.
What MCP is
Before MCP, every AI integration was a custom API connector — Slack had a different format than Salesforce, which was different from your internal database. Every connection was unique engineering work.
MCP (Model Context Protocol, released by Anthropic) standardises how AI agents call external tools. An AI agent that speaks MCP can use any MCP server — regardless of what system it connects to. Build once, use with any compliant client.
Claude, ChatGPT, Cursor, Lovable, and most production AI frameworks now support MCP. Which means a CRM MCP server you build today works across all of them — not just Claude.
Architecture
AI Agent (Claude, ChatGPT, Cursor, Lovable)
│
│ MCP protocol (JSON-RPC over stdio / HTTP/SSE)
▼
┌─────────────────────────────────┐
│ MCP Server │
│ ┌─────────────────────────┐ │
│ │ Tool Registry │ │
│ │ search_contacts() │ │
│ │ create_lead() │ │
│ │ update_deal() │ │
│ └─────────────────────────┘ │
│ ┌─────────────────────────┐ │
│ │ Auth Layer (OAuth/JWT) │ │
│ │ RBAC enforcement │ │
│ │ Audit logging │ │
│ └─────────────────────────┘ │
└──────────────┬──────────────────┘
│
┌────────┴────────┐
│ │
Salesforce CRM Internal DB
(your existing (your existing
systems) systems)MCP is supported by
What we build
Each MCP server exposes a defined set of tools — functions the AI agent can call. The server handles auth, rate limiting, and logging. Your systems stay exactly where they are.
Salesforce, HubSpot, Pipedrive, Zoho
search_contacts, create_lead, update_deal, log_activity, get_pipeline_stage
Claude or ChatGPT looks up a contact mid-conversation, creates a deal, updates a stage — without the AI having direct API access to your CRM.
Confluence, Notion, SharePoint, Google Drive
search_docs, get_page, list_spaces, search_by_tag, get_recent_changes
Cursor or Claude Desktop can search your internal knowledge base during coding or writing sessions — without manual copy-paste of context.
Jira, Linear, GitHub Issues, Asana
create_issue, search_issues, update_status, assign_ticket, get_sprint_board
AI agents create tickets from conversation, update sprint boards, and pull issue context into code reviews — all through natural language.
Salesforce (Sales Cloud, Service Cloud, CPQ)
query_soql, get_opportunity, update_contact, run_report, get_account_hierarchy
Sales agents get full Salesforce context in Claude — account history, open opportunities, renewal dates — without copying data into the chat.
PostgreSQL, MySQL, MongoDB, BigQuery, Snowflake
query_safe (read-only SQL), search_records, aggregate_data, export_results
AI analysts query your data warehouse in natural language through a safe, read-only MCP interface — no direct DB access, no SQL injection risk.
Shopify, WooCommerce, BigCommerce + fulfilment APIs
get_order, update_fulfilment_status, check_inventory, create_refund, search_products
Customer service AI agents handle order lookups, refund creation, and inventory checks without needing a separate UI or human handoff.
Security
Every MCP server we build includes the security layer your enterprise requires. These are not optional add-ons — they are in the default implementation.
Tool exposure is scoped per client and per role. A sales agent MCP server sees contacts and deals — not billing data. An analyst MCP server is read-only. Access boundaries are enforced at the server, not the client.
Users authenticate through your existing identity provider (Okta, Auth0, Azure AD, Google Workspace). The MCP server verifies JWTs before executing any tool call. No shared API keys, no static credentials.
Every tool call is logged: which AI agent called it, which user triggered the call, what arguments were passed, what was returned, and the timestamp. Logs are immutable and exportable for compliance review.
Tool call rates are capped per API key and per user. Burst protection prevents a runaway AI agent from hammering downstream APIs. Quotas are configurable per integration.
The LLM calls the MCP server via a defined protocol — it never gets raw API keys, database credentials, or direct network access to your systems. The MCP server is the only authorised client of your internal APIs.
Configurable PII and sensitive field redaction on MCP server responses. SSNs, card numbers, internal pricing, and other sensitive fields can be masked before data reaches the model context window.
Delivery
We map the tools the AI agent needs, the systems they connect to, authentication flows, and access control requirements. We define the tool schema and test against your API docs.
MCP server implementation in TypeScript or Python. OAuth/JWT integration with your identity provider. Tool implementations with error handling and rate limiting.
Role definitions applied to tool exposure. Audit log pipeline configured. Security review: injection testing, auth bypass testing, data redaction validation.
End-to-end testing with your AI agent (Claude Desktop, API, or custom agent). Load testing at expected call volumes. Deployment to your infrastructure (hosted or on-prem).
Deployment
| Aspect | Hosted (cloud account) | Self-hosted |
|---|---|---|
| Infrastructure ownership | We host on your cloud account (AWS, GCP, Azure) | You deploy on your own servers |
| Maintenance | We manage updates, uptime, and scaling | Your team manages infrastructure |
| Data residency | Configurable — your cloud region | Full control — on-prem or air-gapped |
| Cost | $200–600/month cloud infra (billed to you) | Infrastructure cost only (~$50–200/month) |
| Setup time | Included in project | Add 1–2 weeks for DevOps |
| Best for | Teams without dedicated DevOps | Regulated industries, on-prem requirement |
30-minute scoping call. We map the tools your AI agent needs, identify the systems, and design the server architecture. Most MCP servers are scoped to a firm price in the first session.