Stacks / 8 workflow stages
Build an MCP-Enabled Internal Tool
Understand the protocol, find existing servers, build only what is missing, then test and harden the tool boundary.
Best for
Teams connecting an AI assistant or agent to internal systems, APIs, data, and repeatable business actions through MCP.
Outcome
An MCP-enabled workflow with a deliberate tool contract, minimal custom integration work, and safer production behavior.
Not for
A workflow that only needs a normal API call and gains nothing from a reusable model-facing tool interface.
Use this as a decision framework, not a mandatory shopping list. Swap or skip layers based on your existing stack, constraints, and risk profile.
1. Learn the contract
MCP Documentation
Start with the protocol concepts and supported patterns before choosing or building servers.
AlternativesMCP Specification
2. Check what already exists
Official MCP Registry
Avoid custom work when a maintained server already covers the system or capability you need.
AlternativesMCP.so
3. Build what is missing
Anthropic MCP Builder
Use a focused builder resource when an internal or proprietary system needs a custom MCP implementation.
AlternativesMCP Connector
6. Put the tools behind an agent
Pydantic AI
Expose tools through typed agent interfaces and structured outputs instead of loose function calling.
AlternativesClaude Agent SDK
7. Test tool behavior
Promptfoo
Exercise expected and adversarial tool paths before giving an agent production permissions.
AlternativesBraintrust
8. Threat-model the boundary
OWASP GenAI / LLM Top 10 — 2026
Review prompt injection, excessive agency, data exposure, and other risks created by tool access.
AlternativesMITRE ATLAS
Need this workflow built and running, not just linked? EE Solutions implements agentic stacks like this one. EE Solutions is a senior technology team for private capital firms and their portfolio companies.
Talk to EE Solutions ↗