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FlaskTrack Laboratory Operations & Data Platform
Integration layer · OpenAPI · MCP · service automation

Connect your software to the laboratory record

Use scoped APIs and MCP tools to work with protocols, samples, inventory, and results under the same permissions and attribution as the application.

FlaskTrack exposes organization-scoped interfaces for scientific records, execution, schedules, reporting, and controlled machine access so integration extends the platform instead of bypassing it.

FlaskTrack in practice

Automation with accountability

An approved integration reads or updates a permitted record while the organisation retains actor attribution and the surrounding laboratory context.

Bring FlaskTrack into the rest of your infrastructure

Laboratory operations rarely exist in a single application. FlaskTrack provides structured programmatic access so approved systems can exchange operational data without bypassing organization boundaries, authorization rules, validation, or audit attribution.

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System Integrations Connect internal applications, LIMS and ERP systems, laboratory interfaces, inventory services, and external operational tools.
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Operational Automation Create records, retrieve execution context, inspect schedules, complete supported steps, and coordinate repeatable laboratory processes programmatically.
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Reporting Pipelines Discover available reports, connect analytical consumers, and incorporate FlaskTrack data into dashboards, scheduled exports, and downstream reporting workflows. Explore Reporting & data pipelines →
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Agents & MCP Clients Give approved AI clients a structured tool surface for retrieving context and performing permitted FlaskTrack operations through governed machine identities. Explore Build an MCP agent →

Manage operational biology programmatically

The API exposes structured laboratory records and execution context, allowing external systems to participate in daily operations instead of functioning only as passive data exports.

OpenAPI-based integration

FlaskTrack publishes an interactive OpenAPI interface describing available routes, request bodies, authentication requirements, parameters, and response structures.

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Interactive API Documentation Inspect the currently available operations and schemas directly through the live API documentation. Explore Interactive API Documentation →
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Typed Request & Response Schemas Use documented JSON structures to generate clients, validate payloads, and reduce ambiguity between connected applications.
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Client Generation Use the OpenAPI specification with compatible code generators, testing tools, API clients, and integration development workflows.
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Continuously Expanding Surface The FlaskTrack API evolves alongside the platform. The live OpenAPI documentation remains the authoritative reference for currently available operations.

MCP tooling for governed AI operations

FlaskTrack also provides Model Context Protocol tooling that maps approved platform capabilities into structured tools for compatible AI clients and agent environments.

Automate execution without bypassing the operator

Connected systems can assist with execution, data capture, and synchronization while human operators remain responsible for laboratory judgment, exception handling, and production decisions.

Programmatic reporting and analytical access

Use the API to discover reusable reports and connect FlaskTrack to dashboards, scheduled reporting jobs, data consumers, and broader analytical infrastructure.

Service users for accountable machine access

API keys are associated with service users: dedicated machine identities that can be assigned organization roles and governed separately from human accounts.

Organization-scoped authentication

API requests use organization-aware authentication so each request is evaluated within the correct tenant, identity, and permission context.

Currently documented API areas

The API documentation currently includes operations across the following platform areas. Available routes continue to expand, so integrations should use the live OpenAPI documentation as the source of truth.

Built for practical integration workflows

  • ✔ Connect internal applications, automation services, robotics, dashboards, and external operational systems
  • ✔ Read structured species, ingredient, tool, plasmid, protocol, workflow, sample, and batch records
  • ✔ Create supported operational records through validated, organization-scoped routes
  • ✔ Retrieve sample events and inspect generated batch and sample schedules
  • ✔ Complete supported protocol steps and submit execution timestamps programmatically
  • ✔ Discover reusable reports for dashboards, BI tools, and analytical pipelines
  • ✔ Authenticate with managed service users and organization API keys
  • ✔ Connect compatible AI agents through structured MCP tooling
  • ✔ Generate clients and integrations from an evolving OpenAPI specification
  • ✔ Preserve permission checks, compliance rules, rate limits, and audit attribution for machine activity
Buyer evaluation

Plan the integration before committing to automation

The API is most valuable when ownership, authorization, failure handling, and audit expectations are decided before production use.

Integration scope

Identify the records and actions that genuinely need programmatic access instead of exposing the entire operating model at once.

Accountability

Assign service identities, API-key ownership, rotation procedures, request attribution, and incident-response responsibilities.

Change management

Test integrations against the live OpenAPI specification and plan for version changes, retries, validation failures, and downstream outages.

Programmatic access without surrendering operational control

FlaskTrack gives approved systems meaningful access to laboratory operations while preserving human responsibility, organization isolation, service-user accountability, validation, compliance-aware authorization, and auditable execution history.

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