MCP server development services from senior AI pod teams

Ryz builds Model Context Protocol (MCP) servers with dedicated AI pod teams of senior engineers who work in your cloud and repos, alongside your team. The pod designs the tools, resources and prompts your agents need, connects them to internal systems with proper authorization and scoping, and ships the servers to production with tests and monitoring. Every engineer comes from the top 1% of the tens of thousands we interview, on US business hours.

What we build

MCP is an open protocol, introduced by Anthropic in late 2024, that standardizes how AI applications discover and call external tools and read data. One well-built server can serve Claude, ChatGPT, IDE assistants and your own agents. The hard part is not the protocol. It is deciding what to expose, to whom, and with what guardrails. Typical deliverables:

• Internal API servers. MCP servers that wrap your CRM, ticketing, ERP or internal REST and GraphQL services as a small set of well-described tools, not a one-to-one dump of every endpoint.
• Data access servers. Read-only resources and query tools over Postgres, Snowflake or a data warehouse, with row-level filters and query limits.
• Remote servers with OAuth. Streamable HTTP servers that act as OAuth resource servers, integrated with your identity provider (Okta, Microsoft Entra ID, Auth0) so each call runs with the end user's permissions.
• Local developer servers. stdio servers for Claude Code, Cursor or VS Code that expose internal build, deploy, log and runbook tools to engineers.
• Write-action tools with confirmation. Tools that create tickets, update records or trigger workflows, with dry-run modes, idempotency keys and human approval for risky actions.
• MCP gateways. A central layer that registers approved servers, enforces policy, logs tool calls and gives security teams one place to review what agents can reach.
• Agent integration. Wiring the servers into your agents built on the Anthropic or OpenAI APIs, the OpenAI Agents SDK or LangGraph, with evals for tool selection.

How an engagement works

1. Talk. We map which systems agents should reach, which users they act for, which actions are read-only and which change data, and what your security team needs to approve.
2. Match. We propose a pod scoped to your stack, typically a tech lead, backend engineers who know your integration patterns and an AI engineer for tool design and evals, with names and a price.
3. Join. The pod works in your repos, CI and standups, with weekly demos of agents using the new tools.
4. Grow. You add servers for more systems, or your platform team takes over with a template and review checklist for future servers.

Week 1 covers access, the threat model and a first read-only server running locally against a staging system. Month 1 usually brings remote servers behind your identity provider, a tool-selection eval set and security review of scopes. Month 3 is broader rollout: write actions with approvals, a gateway or registry, monitoring and a standard pattern your teams use for new servers. Timelines depend on scope and your security review process.

The stack our teams work in

How we keep MCP servers safe and useful

An MCP server gives a model a way to act on your systems. Most failures fall into two groups: the agent can do too much, or it cannot figure out how to do the right thing. Our pods design against both:

• Confused deputy problems. A server running with a broad service account lets any user's agent do anything that account can. We run calls with the end user's delegated permissions and scope tokens to the minimum each tool needs.
• Token passthrough. The MCP spec forbids servers from accepting tokens not issued for them and forwarding them downstream. We validate audience and issue separate downstream credentials.
• Prompt injection through tool results. A ticket or email returned by a tool can contain instructions aimed at the model. We treat tool output as untrusted data, keep high-risk write tools behind confirmation and separate read and write servers where it helps.
• Tool poisoning and supply chain risk. Third-party servers can change tool descriptions after approval. We pin versions, review descriptions as code and allowlist servers through a gateway.
• Too many tools. Exposing 80 endpoints confuses models and burns context. We design a small set of task-shaped tools with clear names, typed input schemas and examples, then measure tool-selection accuracy with evals.
• Unbounded results. A query that returns 50,000 rows overflows context and leaks data. Tools paginate, cap sizes and return summaries with links.
• Non-idempotent retries. Agents retry. Write tools take idempotency keys so a retry never creates two orders.

Our pods have shipped agents that work with real business systems, such as an AI driver-support agent covering about 218,000 calls a year in three languages. See the case studies for more.

Team shapes and cost

Typical Ryz cost is $7,000 to $15,000 per engineer per month. Mid-level engineers run $7,000 to $10,000, seniors $10,000 to $15,000 and leads $15,000+, quoted per team.

• Starter pod: tech lead + 2 senior engineers. $15,000+ plus $20,000 to $30,000 is roughly $35,000 to $45,000+ per month. Good for the first servers over two or three internal systems.
• Platform pod: tech lead + 3 senior engineers + 1 mid-level engineer. $15,000+ plus $30,000 to $45,000 plus $7,000 to $10,000 is roughly $52,000 to $70,000+ per month. Good for a gateway, auth integration and servers across several teams.
• One or two senior engineers on your team. $10,000 to $30,000 per month. Good when your platform team owns the design and needs MCP and integration experience.

Project cost is team size × duration × monthly rate. A starter pod at about $40,000 per month for three months is about $120,000. Quotes are scoped per team, and you get a plan, a price and the names of the people before you start.

Dedicated team or staff augmentation?

Choose an AI pod team when you want MCP servers, auth integration and agent evals built and shipped as one scoped outcome. Choose staff augmentation when your platform or AI team owns the roadmap and wants senior MCP developers or AI agent developers working on your team.

When Ryz isn't the right fit

If you only need off-the-shelf connectors for common SaaS tools, the vendors' own MCP servers may be enough. If you want a proprietary agent platform rather than servers built in your stack, a platform vendor fits better. If you need coverage on European or Asian hours, use a global network.

Related

• AI development services
• AI agent development
• AI integration services
• RAG development services
• AI agents vs chatbots

FAQ

What is an MCP server?

It is a service that exposes tools, resources and prompts to AI applications using the Model Context Protocol. Any MCP-compatible client, such as Claude, ChatGPT or an IDE assistant, can discover and call those tools without a custom integration for each client.

How much does MCP server development cost?

Typical Ryz cost is $7,000 to $15,000 per engineer per month. A starter pod of a lead and two seniors is roughly $35,000 to $45,000+ per month. Total cost is team size × duration × monthly rate, and you get a scoped plan, price and names before you start.

How fast can work start?

After the scoping call we propose a team. Most of the timeline depends on scope and your onboarding, including access to the target systems and your security review.

Should we build MCP servers or call APIs directly from our agent?

If one agent calls one API, direct tool definitions are fine. MCP pays off when several clients or agents need the same tools, or when you want one place to enforce auth and logging for agent access.

Is MCP secure enough for production data?

The protocol defines OAuth-based authorization for remote servers, but security depends on implementation: scoping, user-delegated permissions, input validation and handling untrusted tool output. Those are what our pods spend most review time on.

Questions we didn't answer? Email info@ryzlabs.com.