AI-Agent Infrastructure
Custom MCP server development and MCP security reviews for teams connecting AI agents to internal tools and business data.
Book a scoping callAn agent can demonstrate a useful task with a broad API credential. That demonstration does not answer the production questions. Which records should it read, which actions should it perform, and whose authority does it use? Those decisions belong in the interface and access model before more users depend on the integration.
Existing deployments can be difficult to inspect. Tool descriptions, permissions, credentials, and logs may have developed separately. Content returned by a business system can also contain instructions the agent should not follow. Reviewing the whole path helps identify where the boundaries are defined and where the deployment relies on assumptions instead.
Bipath builds scoped MCP servers and reviews agent deployments against an agreed set of scenarios. The founder operates more than ten production MCP servers used by internal agents. Engagements focus on the business system, permitted operations, and written evidence of how access is designed and used. Multi-system orchestration is scoped separately.
Design and build an MCP server exposing scoped read and write tools against one business system or API. The engagement includes authentication, least-privilege tool design, logging, deployment, and documentation. Define the permitted operations and the intended users before implementation so the interface reflects a business need rather than exposing every operation the underlying API supports.
Review an existing agent deployment for credential handling, tool scoping, prompt-injection exposure, data egress, audit logging, and secrets management. Receive findings, fixes within the agreed review scope, and a readout. Documented scenarios define the review boundary; any additional remediation or implementation is identified and scoped separately before work begins.
The founder operates 10+ production MCP servers, builds custom APIs on Cloudflare Workers, and operates an AI data-integration SaaS. See the work page for the underlying platforms.
See the workAn MCP server exposes tools and data through the Model Context Protocol so an agent can interact with a system. A custom server is useful when a generic integration does not match your business permissions, supported operations, or logging needs. The build starts by defining that boundary for one system or API.
Start with the permissions enforced by the underlying system and narrowly scoped tools. Separate read and write operations, constrain inputs, and limit credentials to the required access. The implementation must enforce these boundaries rather than rely on the agent to respect a written instruction. The proposal defines the exact controls in scope.
Yes. The MCP and Agent Security Review can examine an existing deployment, including credential handling, tool scoping, prompt-injection exposure, data egress, and audit logs. The scoping call establishes available access and documented scenarios. Findings, agreed fixes, and a readout are delivered within that defined review boundary.
The service focuses on MCP integrations, Claude and agent SDKs, and custom APIs. Bring the actual client, runtime, and business system to the scoping call. Compatibility is assessed against that setup before the proposal, rather than assuming every framework exposes the same authentication, permission, or deployment features.
Documentation explains the tools exposed, the authentication model, the permitted operations, and how activity is logged. A review identifies gaps in that record and recommends next steps. Your team and auditor decide how those materials fit the broader evidence strategy; the documentation does not itself establish a successful examination outcome.
