The Model Context Protocol (MCP), introduced by Anthropic in 2024, is an open standard that defines how AI language models communicate with external data sources and tools. An MCP server exposes a structured set of tools (functions the LLM can call), resources (data it can read), and prompts (reusable templates) — all over a secure, authenticated connection.
For enterprises, this means you can give Claude, your internal AI assistant, or any MCP-compatible model direct access to live inventory data, customer records, financial systems, or internal knowledge bases — without hardcoding integrations or exposing raw API credentials to the model. Inventiple has built MCP servers for clients in healthcare, fintech, logistics, and SaaS, connecting AI to systems including PostgreSQL, Salesforce, SAP, and proprietary REST APIs.
Connect LLMs to PostgreSQL, MySQL, MongoDB, Elasticsearch with row-level security and read/write controls.
Expose REST, GraphQL, and internal microservice APIs as typed MCP tools with schema validation.
OAuth 2.0 auth, audit logging, rate limiting, and prompt injection protection built into every server.
Every MCP server we build is scoped to a specific business outcome — not a generic demo.
Connect Claude to your Confluence, Notion, or SharePoint via MCP so engineers and support teams get instant, cited answers from internal documentation.
Give your operations team an AI analyst that queries your PostgreSQL or BigQuery warehouse in natural language, with row-level permissions enforced at the MCP layer.
MCP bridge between Salesforce or HubSpot and your AI assistant — deal summaries, next-best-action recommendations, and contact enrichment without copy-pasting data.
MCP servers with PHI access controls, audit trails, and BAA-compatible architecture for clinical decision support and patient data retrieval.
LLM access to your GitHub, Jira, CI/CD pipelines, and monitoring systems — enabling AI agents that triage incidents, review PRs, and manage deployments.
Secure read access to trading systems, regulatory filings, and internal financial models with PCI-DSS and SOC-2 compatible audit logging.
Cost savings for enterprise AI automation client
Return on AI investment across MCP-powered workflows
Typical time from design to production MCP deployment
Our MCP server work is part of a broader agentic AI practice. We have built autonomous systems where MCP servers provide the data layer, LangGraph or CrewAI agents provide the reasoning layer, and enterprise APIs provide the action layer — all working together in production.
Map your data sources, access patterns, auth mechanisms, and compliance requirements. Define tool schema and resource types.
Design permission model, auth flow, audit logging strategy, and deployment architecture before writing a line of code.
Implement MCP server with typed tools, resources, and error handling. Integration tests against your actual systems.
CI/CD pipeline, health checks, OpenTelemetry instrumentation, alerting. Handoff with runbooks and architecture docs.
An MCP server is a lightweight service that exposes tools, resources, and prompts to AI language models via Anthropic's open Model Context Protocol. It acts as a secure bridge between an LLM and your existing systems — databases, APIs, file systems, CRMs — letting the AI read and act on live data without requiring custom glue code for every integration.
Off-the-shelf AI tools cannot access your internal systems, proprietary data, or business logic. Custom MCP servers give LLMs governed, authenticated access to your specific infrastructure — SAP, Salesforce, internal databases, warehouse systems — while enforcing your security policies, audit logging, and rate limits at the protocol layer.
A focused single-system MCP server (e.g. connecting Claude to your PostgreSQL database or REST API) typically takes 2–3 weeks from design to production. Multi-system enterprise MCP platforms with auth, observability, and CI/CD pipelines range from 6–10 weeks. We scope every engagement after a free technical discovery call.
Every Inventiple MCP server is built with OAuth 2.0 / API key authentication, request-level audit logging, tool-scoped permissions (read-only vs write), input sanitization against prompt injection, rate limiting, and TLS encryption in transit. For regulated industries we add HIPAA or PCI-DSS compliance controls at the architecture level.
MCP is an open standard supported natively by Claude (Anthropic), and increasingly adopted across the ecosystem. Our servers are compatible with Claude API, Claude Desktop, Cursor, and any MCP-capable host. We also build adapter layers for OpenAI and Gemini where needed.
Yes — we have built MCP servers on top of PostgreSQL, MySQL, MongoDB, Elasticsearch, REST APIs, GraphQL APIs, Salesforce, HubSpot, internal microservices, and file storage systems (S3, GCS). Our integration approach preserves your existing auth and access control systems rather than bypassing them.
Both pages cover MCP work. The mcp-server-development page focuses on the full development lifecycle — architecture, build, and deployment. This page focuses on the MCP server as a component and its enterprise use cases. For an engagement, both lead to the same team.
Yes. We offer optional SLA-backed support packages covering uptime monitoring, error alerting, performance optimization, and quarterly architecture reviews. Production MCP servers serving enterprise clients benefit from our observability stack (OpenTelemetry + Datadog/Grafana) deployed during the initial build.
Related services: MCP Server Development · Agentic AI Development · LangChain Development · Generative AI Development