MCP Server Development

MCP servers that give Claude access to your systems

The Model Context Protocol (MCP) is Anthropic's open standard for connecting AI models to external data sources and tools. An MCP server sits between Claude and your systems. your database, CRM, analytics platform, internal API. and gives Claude secure, structured read and write access. We build MCP servers in production. Our own stack includes servers for Ahrefs, Google Search Console, BigQuery, and n8n. We know the patterns that work at scale.

Build Your MCP Server

Explore Claude Services

MCP servers running in production

15+

Client workflows served via MCP

100%

Citation rate. seo_query_kb MCP

Data leaves your infra without auth

What We Build

Six MCP server types for common enterprise needs.

MCP works for any system that has a structured API or query interface. These are the six categories we build most frequently.

Database MCP Servers

Give Claude read (and optionally write) access to your internal databases. SQL, BigQuery, Postgres, MongoDB, Supabase. Claude can query your data in response to natural language, generate reports, identify anomalies, and take action. all within defined permission boundaries.

Read-only or write-enabled access
Query authorization layer
Row-level security enforcement
Sensitive column masking

API Integration MCP Servers

Wrap any REST or GraphQL API in an MCP server so Claude can call it natively. CRM APIs, payment systems, logistics platforms, third-party data providers. The MCP server handles auth, rate limiting, error handling, and response normalization. Claude just uses the tools.

OAuth and API key auth management
Rate limit handling and retry logic
Response normalization to Claude-friendly format
Tool documentation auto-generation

Analytics Platform MCP Servers

Connect Claude to your analytics stack: Google Analytics 4, Mixpanel, Amplitude, Segment, Snowflake. Claude can pull reports, identify trends, surface anomalies, and explain performance changes without requiring a data analyst to mediate every query.

GA4, Mixpanel, Amplitude integrations
Natural language to analytics query translation
Automated report generation
Anomaly detection and alerting

CRM & Sales Tool MCP Servers

Salesforce, HubSpot, Freshsales, Pipedrive. Claude can query deal stages, pull contact records, generate follow-up drafts based on interaction history, and update field values. all from a conversation, with full audit logging.

Contact and deal record access
Field update with change logging
Interaction history summarization
Follow-up draft generation in context

Internal Knowledge Base MCP Servers

Give Claude structured access to your internal documentation, SOPs, policy docs, and knowledge base. The MCP server handles document chunking, semantic retrieval, relevance ranking, and source attribution so Claude retrieves the right context for each query rather than relying on what fits in its context window.

Vector search over internal docs
Relevance-ranked retrieval
Document freshness tracking
Role-based access control

Custom Tool MCP Servers

Not everything fits a standard category. We build MCP servers for custom internal tools: proprietary data pipelines, legacy systems with non-standard APIs, multi-system aggregation servers that combine data from several sources into one Claude-accessible interface.

Legacy API adapter patterns
Multi-source aggregation servers
Custom authentication schemes
Async tool call support

How We Build MCP Servers

Four stages from system access to production server.

Step 01

System mapping and access design

We map the systems you want Claude to access, the specific operations Claude should be able to perform, and the access controls that need to be in place. This design document defines every tool the MCP server will expose. what it does, what parameters it accepts, what auth it uses, and what it returns.

Step 02

Security and permission architecture

Before we write the server, we design the permission layer. Read-only vs. read-write access per tool. Role-based access control if multiple users will share the server. Sensitive field masking. Audit log schema. We treat security as a first-class concern, not an afterthought.

Step 03

Build and integration testing

We build the MCP server against your actual system. your real API endpoints, your real database schema. Integration tests verify every tool under both success and failure conditions. We test with Claude Code to validate that the tool descriptions are clear enough for Claude to use them correctly without explicit instruction.

Step 04

Deployment and monitoring

We handle deployment to your preferred environment (local, cloud VM, serverless). We configure logging, error alerting, and usage monitoring. You get a runbook covering how to update credentials, add new tools, and debug common failures. so your team is not dependent on us for ongoing operations.

Running in Production

How four MCP servers power our agency's daily operations.

The Challenge

An SEO agency working across 15 clients needs live data. current keyword rankings, traffic trends, backlink changes, content performance. Without live data, every analysis is stale by the time it is delivered. Switching between Ahrefs, Google Search Console, BigQuery, and n8n dashboards to pull that data manually costs 2-4 hours per client per week.

Our Solution

We built four MCP servers: one for Ahrefs (keyword data, backlink analysis, competitor research), one for Google Search Console (ranking, impressions, CTR by page), one for BigQuery (aggregated performance data, Salesforce cross-references), and one for n8n (workflow triggering and status monitoring). Claude Code CLI sessions connect to all four. Analysts ask questions in natural language. The MCP layer retrieves the right data, formatted for immediate use.

Results Achieved

MCP servers in production

Ahrefs, GSC, BigQuery, n8n

~40%

Manual data pulls eliminated

Per analyst per week

Real-time

Data freshness

vs. weekly CSV exports previously

seo_query_kb

Fine-tuned model access via MCP

100% citation rate, KB-grounded

Understanding MCP

What the Model Context Protocol actually does.

MCP is not a plugin system or an API wrapper. It is an open standard that defines how AI models discover, access, call, and receive results from external capabilities in a consistent way.

Tools

Functions Claude can call to take action
Read database, call API, write file
Parameters are typed and validated
Results returned in structured format
Errors handled gracefully

Resources

Data sources Claude can read from
Files, database records, API responses
Streamed or one-shot retrieval
URI-based addressing scheme
Version-aware for changing data

Prompts

Pre-built prompt templates exposed by the server
Include dynamic context at call time
Reusable across different Claude sessions
Can chain multiple tools in a workflow
Discoverable by Claude automatically

Security

Local transport (stdio) or remote (HTTP/SSE)
Auth token management server-side
Explicit tool permission declarations
Claude cannot access undeclared endpoints
Full request and response logging

FAQ

MCP server development frequently asked questions

MCP is an open standard published by Anthropic that defines how AI models communicate with external systems. An MCP server exposes a set of "tools" (functions Claude can call), "resources" (data sources Claude can read), and "prompts" (reusable prompt templates). Claude discovers these capabilities automatically and can use them during a conversation or task without additional user instruction.

RAG (Retrieval-Augmented Generation) retrieves text from a knowledge base and puts it into the context window. Fine-tuning bakes knowledge into model weights. MCP is different from both. it gives Claude real-time access to live systems and the ability to take actions (write to a database, call an API) not just read static data. The three approaches complement each other and serve different needs.

Yes, with proper design. MCP servers run under your control. on your infrastructure, behind your auth. Claude can only access the tools the server explicitly exposes. We design a permission layer that defines exactly what Claude can read, what it can write, and what it cannot touch. Everything is logged. For regulated industries we implement field-level masking, row-level security, and access control that maps to your existing identity management system.

Any system with a structured API or query interface: databases (Postgres, MySQL, BigQuery, MongoDB, Supabase), REST and GraphQL APIs, analytics platforms (GA4, Mixpanel, Amplitude), CRM systems (Salesforce, HubSpot, Freshsales), document stores, and internal tools. We have built servers for all of these. The pattern is the same; the integration details vary.

MCP supports two transport modes. Local transport (stdio) runs the server as a subprocess on the same machine as Claude Code. no network exposure, zero infrastructure overhead, ideal for developer use cases. Remote transport (HTTP/SSE) runs as a persistent server that multiple Claude instances can connect to. better for shared team use and production deployments. We build for whichever mode fits your needs.

A straightforward read-only API integration takes 5-10 business days: system mapping (1 day), security design (1 day), build and testing (3-5 days), deployment and documentation (1-2 days). More complex servers. multi-system aggregators, write-enabled servers with permission logic, legacy system adapters. run 2-4 weeks. We provide a specific timeline estimate after reviewing your system documentation.

Ready to Connect Claude to Your Systems?

Let's scope your MCP server build.

Tell us what systems you want Claude to access and what operations matter most. We design the permission architecture, build the server, and deploy it to your infrastructure.

Security and permission design included
Integration tested against your real systems
Deployment to your infrastructure. you own it