Scoped services
Custom MCP Servers & AI Integrations
For teams connecting AI assistants to specific business data and actions with explicit permissions.
Scope and deliverables
Define tool contracts, authentication, validation, side effects, useful errors and integration acceptance checks. Confirm protocol/runtime support before implementation.
- Bounded tool/action contracts
- Authenticated integration implementation within confirmed stack
- Allow/deny and failure-path tests
- Operating documentation and handover
Process and access
Start with discovery, confirm support and permissions, agree acceptance checks, perform the work and retest before handover. Pricing and timing follow the actual scope.
Provider documentation, a test account, agreed caller identities and permitted actions; credentials are exchanged only through an agreed private process.
Evidence and limitations
Python/FastAPI and API orchestration are source-supported. No protocol credential, partner status or prior MCP client result is claimed. MCP alone does not establish security.
The synthetic audit report demonstrates the reporting format. It is not a client outcome. No invented credentials or performance statistics are used.
Frequently asked questions
Does MCP make a connection secure?
No. Authentication, permissions and data boundaries need implementation and tests.
Can tools change business records?
Only within an agreed action and permission scope, including retry and confirmation behavior.
Which protocol version is supported?
The version, transport and runtime are confirmed during discovery.
Can RAG be included?
Retrieval and evaluation can be scoped under custom AI tools when the source and permission requirements are clear.