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MCP server development services

MCP server developmentconnect your data to Claude, Cursor & agents

Conalytic builds custom Model Context Protocol (MCP) servers so AI clients safely call your marketing data, CRM, ads APIs, and internal tools—with least-privilege auth and clear tool scopes.

We deploy on Vercel, Cloudflare, or your VPC, document client setup, and test with Claude Desktop, Cursor, and your internal agents.

Book a strategy callSee what we deliver
Streamable HTTPRemote MCP
SecuredOAuth 2.1

Sitemap

robots.txt

Allow: /

Sitemap: …/sitemap.xml

+ NEW audit
URL inspection passedIndexable
Organic workspace
QueryPosΔ
b2b seo agency#4+6
technical seo audit#7+3
organic growth strategy#2+1
google search console setup#11+9
Live GSC syncPosition ↑ 6
🕷️Crawl
Indexed
📈Rank
SSR HTML Google can render
Pillar: Organic growth
Technical SEO
Content briefs
Internal links
Schema
Cluster linked

Sitemap

robots.txt

Allow: /

Sitemap: …/sitemap.xml

+ NEW audit
URL inspection passedIndexable
Organic workspace
QueryPosΔ
b2b seo agency#4+6
technical seo audit#7+3
organic growth strategy#2+1
google search console setup#11+9
Live GSC syncPosition ↑ 6
🕷️Crawl
Indexed
📈Rank
SSR HTML Google can render
Pillar: Organic growth
Technical SEO
Content briefs
Internal links
Schema
Cluster linked

MCP servers that work with Claude, ChatGPT, Cursor, VS Code, and custom AI agents — built in TypeScript and Python.

HubSpot
Salesforce
Microsoft Dynamics 365
Zoho CRM
Pipedrive
WordPress
Webflow
Shopify
Contentful
Sanity
Strapi
Drupal
HubSpot
Salesforce
Microsoft Dynamics 365
Zoho CRM
Pipedrive
WordPress
Webflow
Shopify
Contentful
Sanity
Strapi
Drupal

What we do

Model Context Protocol development for production AI tool access

Custom MCP server tool design

Custom MCP servers

Well-designed tools, resources, and prompts that give AI assistants precise access to your product or data — not a raw API dump.

Remote MCP server hosting architecture

Remote MCP hosting

Streamable HTTP servers deployed on Vercel, Cloudflare, AWS, or GCP with scaling, rate limits, and uptime monitoring.

OAuth authentication for MCP servers

OAuth 2.1 & access control

OAuth authentication, scoped permissions, and role-based access so AI clients only see what each user is allowed to see.

SaaS MCP connector for AI assistants

SaaS product connectors

Public MCP connectors that let your customers use your product inside Claude, ChatGPT, and other AI clients.

Internal MCP server for company data

Internal data & tools servers

MCP servers for your warehouse, CRM, docs, and internal APIs so teams can query company data from their AI assistant.

Marketing analytics MCP server

Marketing & analytics MCP servers

MCP access to GA4, Search Console, ad platforms, and BigQuery — the same data layer that powers Conalytic.

MCP Apps interactive UI in an AI client

MCP Apps & interactive UI

Tools that return interactive interfaces inside AI clients, not just text, using the MCP Apps extension.

MCP server security audit

Security audits & spec upgrades

Reviews for authentication gaps, prompt injection, and over-broad permissions — plus migrations to the latest MCP specification.

Why Conalytic for MCP

MCP servers your security team can approve—scoped tools, OAuth, and audit-friendly logging

Protocol expertise, engineering, and analytics in one team

Tool design aligns with OpenAPI where possible; we deliver specs, hosting guidance, and checklists so dev and ops teams can maintain connectors long term.

Model Context Protocol server visual

Clear names, descriptions, and schemas help models pick the right tool and call it correctly.

OAuth, scoped permissions, input validation, and logging close the gaps common in public MCP servers.

The MCP specification changes quickly. We maintain your server through spec updates and new client features.

How it works

How our MCP server development process works

Spec tools, implement auth, deploy, test clients, hand off runbooks.

  1. 1
    Audit & baseline

    Design

    Define user tasks, tools, resources, and permissions; choose local or remote deployment.

  2. 2
    Prioritized roadmap

    Build

    Implement the server in TypeScript or Python with tests against real AI clients.

  3. 3
    Ship with owners

    Secure

    Add OAuth 2.1, rate limits, input validation, and audit logging; run a security review.

  4. 4
    Prove in GA4 & GSC

    Launch

    Deploy, document, list your connector where relevant, and monitor usage.

Compliance workspace
GDPR & privacy policyAligned
EU cookie & consentConfigured
Data processing (DPA)Documented
llms.txt & AI crawlersPublished
GDPR · EU · UK ready

Security

MCP servers that pass a security review

Many internet-facing MCP servers still ship without authentication. Ours include OAuth 2.1, least-privilege scopes, prompt-injection defenses, and audit logs from the first release.

Request a security review

Distribution & usage

Put your product inside the AI tools customers already use

A well-built connector makes your product available inside Claude, ChatGPT, and coding assistants — and usage analytics show which tools drive value.

Plan a connector
GA4 property mapped
Search Console linked
Consent Mode v2
Conversions verified

Tracking QA sent to your team

GA4 · GSC · Ads · CRM events

MCP tool design visual
conalytic.com / SEO workspace
Technical auditLive
Indexable URLs98%
Crawl errors3
Core Web VitalsPass

Tools designed around real user tasks

Fewer, better tools with clear schemas outperform one tool per API endpoint. We design for how AI models choose and call tools.

Discuss tool design
MCP authentication flow visual
conalytic.com / SEO workspace
Pillar: Marketing analytics
GA4 setup
KPI goals
Report templates
AI search

OAuth, permissions, and audit logs built in

Users sign in once, AI clients get scoped access, and every tool call is logged for compliance.

Secure my MCP server
SaaS connector in AI assistant visual
conalytic.com / SEO workspace

✓ GET /pricing → 200 index,follow

✓ canonical → https://conalytic.com/pricing

→ rendered HTML: 142KB (SSR)

! 2 URLs in “Crawled – not indexed” queue

opacity:0 instances in HTML: 0

Your SaaS, available inside AI assistants

Public MCP connectors that bring your product's actions and data into Claude, ChatGPT, and agent workflows.

Launch a connector

Proof in the data

MCP delivery standards we follow

Live program metrics

01

0+

MCP servers built and deployed

02

0+

Tools shipped across client servers

FAQ

MCP server development FAQ

Auth models, hosting, supported clients, and maintenance.

What is an MCP server?

An MCP server exposes tools, data, and prompts through the Model Context Protocol so AI assistants and agents can use your software in a standard way.

What is the difference between MCP and an API?

An API is built for developers; MCP is built for AI models. An MCP server usually wraps your API with task-focused tools and descriptions that models understand.

Which AI clients support MCP?

Claude, ChatGPT, Cursor, VS Code, Gemini tooling, Microsoft Copilot, and many agent frameworks support MCP.

How do you secure an MCP server?

With OAuth 2.1 authentication, scoped permissions, input validation, rate limiting, prompt-injection defenses, and audit logging.

How long does it take to build an MCP server?

A focused MCP server typically takes 2–6 weeks, depending on the number of tools, authentication needs, and integrations.

Do you maintain the server as the MCP spec changes?

Yes. We offer maintenance plans that cover spec updates, client compatibility, and new features.

Still have questions? Talk to our team

Ready for the next step?

Expose your stack to AI clients through MCP

Scope an MCP server with tool list, auth approach, and deployment plan for your first agent use case.

  • Indexation → pipeline
  • GSC + GA4 native
  • Ship weekly
Book an MCP consultationView all services

Non-brand clicks

+32%

QoQ · GSC verified

Live

Index coverage

94%

Money pages · last crawl

Program sync

Roadmap, owners & GA4 events aligned

Updated this week