MCP server development is the engineering of Model Context Protocol servers that expose tools, resources and prompts to AI clients (Claude, ChatGPT, AWS Bedrock AgentCore, Microsoft Copilot, Cursor, Continue). MCP is the 2026 standard for connecting AI agents to your private APIs, databases and business logic — write the integration once, ship it to every AI client. iMagic Solutions builds production MCP servers with OAuth 2.1 authentication, scoped permissions, audit logging, rate limiting and observability. $8K–$60K offshore-delivered, USA / Europe / India delivery.
MCP (Model Context Protocol) is the JSON-RPC protocol Anthropic open-sourced in late 2024 and that the entire AI agent ecosystem now speaks — Claude, ChatGPT, AWS Bedrock AgentCore, Microsoft Copilot, plus IDE tools like Cursor, Continue and Zed. The breakthrough: any AI client can discover and invoke capabilities from any MCP-compliant server, so the same Salesforce integration written once works with Claude, GPT, Bedrock and any future AI client. Before MCP, every agent framework had its own tool format and integrations were rewrites; MCP eliminates that fragmentation.
We build three categories of MCP server: (1) custom MCP servers exposing your internal APIs (your CRM, your billing system, your internal microservices, your data warehouse) so AI agents can read and act on your private business logic; (2) remote multi-tenant MCP servers for SaaS products that want to ship MCP as a customer-facing feature (enabling customers' AI agents to operate inside your platform); (3) MCP-server hardening for existing prototypes that need OAuth 2.1, scoped permissions, audit logging and observability to be production-ready.
Production MCP servers have non-trivial engineering requirements that prototypes ignore. Authentication (OAuth 2.1 for remote, scoped env vars for local), authorization (per-client scoped tool permissions — the marketing-bot can read but not delete), rate limiting per client to prevent abuse, audit logging of every tool invocation with full input/output context, error handling that doesn't leak internals to the LLM, and observability (Datadog, Langfuse, Helicone). For high-stakes actions we add human-in-the-loop checkpoints — the MCP server returns a 'confirmation required' state and the AI client surfaces it to a human approver before execution.
We're MCP-native. We've shipped custom MCP servers for client CRMs, billing systems, internal data warehouses, custom search systems, document repositories and product-catalogue APIs. We use the official MCP SDKs (TypeScript and Python), default to HTTP/SSE transport for production multi-tenant use cases, and design tool definitions that are LLM-friendly (concise descriptions, structured schemas, predictable error semantics) rather than direct REST API mirrors. The difference between an MCP server an AI uses correctly and one it constantly gets wrong is almost entirely tool-description quality.
Every MCP engagement starts with a free 30-minute call and a fixed-price 1–2 week proof-of-concept exposing 3–5 tools from your existing API. You measure AI-client integration quality (do agents use the tools correctly?) before committing to a full build. Typical full MCP server: 3–6 weeks for 5–15 tools with OAuth, audit logging and observability. $8K–$15K offshore for the PoC, $15K–$60K for the production build.
1–2 week fixed-price proof-of-concept exposing 3–5 tools from your existing REST or GraphQL API. Output: a working MCP server, integration tests with Claude or ChatGPT, recommendation report. $8K–$15K offshore-delivered.
Full production MCP server with OAuth 2.1, scoped permissions per client, rate limiting, audit logging, observability and error handling. 3–6 weeks. $15K–$40K offshore.
Multi-tenant MCP server for SaaS products shipping MCP as a customer feature. Tenant isolation, per-customer scoped credentials, billing-aware rate limits, SOC 2-aligned audit. 6–10 weeks. $40K–$80K.
Take an existing prototype MCP server and add production essentials — OAuth, RBAC, rate limiting, structured logging, observability, human-in-the-loop checkpoints. 2–4 weeks.
Audit existing MCP server tool descriptions and schemas — rewrite for LLM consumption, add structured error semantics, improve agent-success rate. Typical 30–50% improvement in correct tool selection. 1–2 weeks.
Add Datadog, Langfuse or Helicone integration to existing MCP servers — tool invocation traces, latency, error rates, per-client usage, cost dashboards.
Port existing agent-framework tool integrations to MCP so the same tools work across Claude, ChatGPT, Bedrock AgentCore and future clients. 2–4 weeks per agent framework.
Wire your MCP server into AWS Bedrock AgentCore as a remote tool source — production observability, action tracing, evaluation hooks. AgentCore has native MCP support in 2026.
Stdio MCP servers for Claude Desktop, Cursor, Continue, Zed and other local AI clients — filesystem access, git, internal CLI tools, custom dev workflows. 1–3 weeks.
Monthly retainer covering new-tool addition, tool-description tuning, scope expansion as the AI client ecosystem evolves, observability dashboards, security patching.
We've shipped production MCP servers for CRMs, billing systems, data warehouses, document repos and product catalogues. We know the protocol and the gotchas — not just the SDK.
MCP-compliant by spec — works with Claude Desktop, ChatGPT, AWS Bedrock AgentCore (MCP-native in 2026), Microsoft Copilot, Cursor, Continue and any other MCP client. No vendor lock-in.
OAuth 2.1 for remote multi-tenant, scoped env credentials for local, per-client tool scoping (the marketing-bot can read but not delete), rate limiting and audit logging on every invocation.
The difference between an MCP server AI uses correctly and one it constantly gets wrong is tool-description quality. We design tool schemas, descriptions and error semantics for LLM consumption — not direct REST API mirrors.
For destructive or financial actions, the MCP server returns a 'confirmation required' state and the AI client surfaces it to a human approver. The agent drafts, the human approves.
Every production MCP server ships with Datadog, Langfuse or Helicone integration — tool invocation traces, latency, error rates, per-client usage, cost dashboards.
We build local stdio MCP servers (Claude Desktop, Cursor integrations) and remote HTTP/SSE servers (production multi-user services). Different transport, same engineering discipline.
Every engagement starts with a 1–2 week PoC exposing 3–5 tools from your existing API. You validate AI-client integration quality before committing to the full build.
A few of the things we deliver under mcp server development:
Free 30-minute call. We map which existing APIs / systems should expose tools, which AI clients will consume the server, auth requirements and success metric. Output: a written scope and tier recommendation within 48 hours.
Tool definitions, schemas, descriptions optimized for LLM consumption (not REST API mirrors). Auth model (OAuth 2.1 vs scoped env), permission scoping per client, audit-logging strategy. Written design doc before code.
Fixed-price 1–2 week PoC — server exposes 3–5 tools, validated end-to-end with Claude Desktop or ChatGPT custom GPT. You measure correct-tool-selection rate and integration quality.
Production MCP server — OAuth 2.1, RBAC, rate limiting, structured audit logging, observability, error handling, human-in-the-loop checkpoints for high-stakes actions. 3–6 weeks for 5–15 tools.
Deploy, monitor correct-tool-selection rate, iterate on tool descriptions (this is the single biggest accuracy lever post-launch), add new tools as the agent ecosystem matures.
MCP (Model Context Protocol) is the JSON-RPC protocol Anthropic open-sourced in late 2024 and that the entire AI agent ecosystem now speaks — Claude, ChatGPT, AWS Bedrock AgentCore, Microsoft Copilot, Cursor, Continue. It standardises how AI clients discover and invoke tools from external servers. Build the integration once, ship it to every AI client. See /blog/mcp-servers-explained for the deeper guide.
PoC (3–5 tools, 1–2 weeks): $8K–$15K offshore-delivered. Production MCP server (5–15 tools with OAuth, audit, observability): $15K–$40K, 3–6 weeks. Enterprise multi-tenant MCP server for SaaS products: $40K–$80K, 6–10 weeks. MCP-server hardening of existing prototypes: $10K–$25K, 2–4 weeks.
Use existing public MCP servers (filesystem, GitHub, Slack, Postgres, Stripe, Notion, Linear, Sentry) for standard integrations. Build custom MCP servers for proprietary internal APIs, business logic specific to your domain, or compliance requirements (rate limiting, audit logging, scoped permissions) the public servers don't offer. Most production stacks use 5–15 existing servers + 1–5 custom.
In 2026: Anthropic Claude (Desktop, API, Claude Code), OpenAI ChatGPT, AWS Bedrock AgentCore, Microsoft Copilot Studio, plus IDE tools — Cursor, Continue, Zed. Plus future clients — MCP is effectively the de facto standard for AI-tool integration, like HTTP became the standard for web APIs.
Yes, when built correctly. Production MCP servers ship with OAuth 2.1 authentication, scoped permissions per client (the marketing-bot can read but not delete), rate limiting per client, structured audit logging of every tool invocation, and human-in-the-loop checkpoints for high-stakes actions. We build all five into every production server.
Yes. HIPAA-eligible MCP servers ship inside the client's AWS account with the BAA, KMS encryption, PHI redaction at the tool boundary, audit logging aligned to the Security Rule. GDPR MCP servers run in eu-west-1 / eu-central-1. SOC 2 Type II controls (encryption, access control, monitoring, change management) are designed in from day one.
An MCP server in front of your existing REST or GraphQL API is the cleanest way — the MCP server enforces scoped permissions, audit logging and rate limiting that the underlying API may not, and translates between LLM-friendly tool schemas and your API's native shape. Typical build: 1–3 weeks for a server exposing 5–15 tools.
OpenAI Functions and LangChain tools are agent-framework-specific tool definitions — they only work inside that framework. MCP is a separate protocol any agent framework can speak. In practice modern frameworks now bridge to MCP (LangChain has an MCP adapter, AgentCore is MCP-native). MCP becomes the lower-level protocol; frameworks become MCP clients.
Both. Local stdio MCP servers run as subprocesses (Claude Desktop, Cursor for filesystem and IDE integrations). Remote HTTP/SSE MCP servers serve multi-tenant production use cases with OAuth 2.1 auth, scoped permissions and audit. Different transport, same engineering discipline. We build both.
AgentCore added native MCP client support in 2026 — it can invoke MCP servers as tools alongside native Lambda-based tool definitions. This is why we recommend Bedrock + AgentCore for production agents: tools written as MCP servers are portable across the agent stack, not locked to AgentCore's proprietary format.
The single biggest factor in MCP server accuracy is tool-description quality. The LLM decides which tool to call based on the description; bad descriptions cause wrong-tool selection. We rewrite tool descriptions iteratively post-launch, A/B-testing against held-out scenarios, typically improving correct-tool-selection rate 30–50%.
Book a free 30-minute MCP discovery call via /contact. We'll walk through which APIs / systems should expose tools, which AI clients will consume them, auth requirements and target use cases — then send a written scope and price band within 48 hours. Most engagements start with the 1–2 week fixed-price PoC.
Generative AI agents, RAG assistants, copilots and chatbots built on AWS Bedrock, Claude, OpenAI and open models — for India and the USA.
LLM-powered, RAG-grounded chatbots for web, WhatsApp, Slack and Teams — from $3K rule-based FAQ bots to $150K+ enterprise AI assistants. USD pricing, US/EU/India delivery.
Autonomous AI agents that take actions — not just answer — built on AWS Bedrock AgentCore, LangGraph and CrewAI. From $15K single-action to $300K+ enterprise.
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