AgentTool: connect agents to external APIs

The AgentTool CRD lets you expose any HTTP API as a callable tool for your AI agents. Each AgentTool is backed by an OpenAPI specification — the operator stores this schema and makes it available to agents so their underlying LLM framework knows exactly which operations are available, what parameters each operation accepts, and what responses to expect. You can target external URLs or internal Kubernetes services, and you can share a single AgentTool across many agents in different namespaces.

API reference

apiVersion: agent.flokoa.ai/v1alpha1
kind: AgentTool

Basic structure

apiVersion: agent.flokoa.ai/v1alpha1
kind: AgentTool
metadata:
  name: weather-api
spec:
  type: openapi
  description: "Get current weather conditions and forecasts for any location"

  openApi:
    url: "https://api.weather.com/v1"
    openApiSchema:
      endpointPath: "/openapi.json"

The description field is critical — the LLM reads it to decide when and whether to invoke the tool. Write it as a clear, one-sentence summary of what the API does.

OpenAPI schema sources

The OpenAPI schema is required for every AgentTool. It tells the LLM what operations are available and what shape the request and response payloads take. You can supply the schema in three ways:

endpointPath
value (inline)
valueFrom (ConfigMap)

The simplest option: the operator fetches the schema directly from a path on the target service. Use this when your service already serves its own OpenAPI spec.

spec:
  type: openapi
  description: "Query the product catalogue"
  openApi:
    url: "https://api.example.com"
    openApiSchema:
      endpointPath: "/docs/openapi.json"

Embed the complete OpenAPI spec as a YAML object directly in the AgentTool manifest. This is useful for small, stable APIs where you want a single self-contained manifest.

spec:
  type: openapi
  description: "Perform arithmetic calculations"
  openApi:
    url: "https://api.example.com"
    openApiSchema:
      value:
        openapi: "3.0.0"
        info:
          title: Calculator API
          version: "1.0"
        paths:
          /calculate:
            post:
              summary: Perform a calculation
              requestBody:
                content:
                  application/json:
                    schema:
                      type: object
                      properties:
                        operation:
                          type: string
                          enum: ["add", "subtract", "multiply", "divide"]
                        a:
                          type: number
                        b:
                          type: number
              responses:
                "200":
                  description: Calculation result
                  content:
                    application/json:
                      schema:
                        type: object
                        properties:
                          result:
                            type: number

Reference an OpenAPI spec stored in a Kubernetes ConfigMap. This is the right choice for larger specs, or when multiple teams manage the spec independently of the AgentTool manifest.

# 1. Store the spec in a ConfigMap
apiVersion: v1
kind: ConfigMap
metadata:
  name: api-specs
data:
  openapi.json: |
    {
      "openapi": "3.0.0",
      "info": {"title": "My API", "version": "1.0"},
      "paths": {}
    }

# 2. Reference it from the AgentTool
apiVersion: agent.flokoa.ai/v1alpha1
kind: AgentTool
metadata:
  name: my-tool
spec:
  type: openapi
  description: "Call my internal API"
  openApi:
    url: "https://api.example.com"
    openApiSchema:
      valueFrom:
        name: api-specs
        key: openapi.json

Target configuration

External URL
Internal Kubernetes service

Point the tool at any publicly reachable or VPN-accessible HTTP endpoint. You can attach custom headers and a timeout:

spec:
  type: openapi
  description: "Send transactional emails via SendGrid"
  openApi:
    url: "https://api.sendgrid.com/v3"
    timeoutSeconds: 30
    headers:
      Accept: "application/json"
      Content-Type: "application/json"
    openApiSchema:
      endpointPath: "/openapi.json"

Use serviceRef to target a Service inside the cluster. Flokoa resolves the DNS name automatically so you never hard-code internal hostnames:

spec:
  type: openapi
  description: "Check real-time inventory levels in the warehouse system"
  openApi:
    serviceRef:
      name: inventory-service
      namespace: backend
      port: 8080
    timeoutSeconds: 10
    openApiSchema:
      endpointPath: "/api/openapi.json"

Prefer serviceRef over url for in-cluster services. It is faster, avoids external network hops, and lets NetworkPolicies enforce access control cleanly.

Using tools in agents

You can attach tools to an Agent either by referencing an existing AgentTool CR or by defining the tool inline in the Agent spec. Referencing an AgentTool CR (recommended):

apiVersion: agent.flokoa.ai/v1alpha1
kind: Agent
metadata:
  name: customer-service-agent
spec:
  tools:
    # Same namespace
    - toolRef:
        name: weather-api

    # Cross-namespace reference
    - toolRef:
        name: inventory-check
        namespace: shared-tools

  runtime:
    type: standard
    spec:
      container:
        name: agent
        image: ghcr.io/example/agent:v1.0.0
        ports:
          - containerPort: 8080
            name: http

Inline tool definition (no separate CR needed):

spec:
  tools:
    - name: petstore
      template:
        type: openapi
        description: "Browse and manage pet listings"
        openApi:
          url: "https://petstore3.swagger.io"
          openApiSchema:
            endpointPath: "/api/v3/openapi.json"

Inline tool definitions cannot be reused across multiple agents. Extract frequently used tools into standalone AgentTool CRs and share them by reference.

Status fields

The operator validates and stores each tool’s OpenAPI spec. The resulting status looks like:

status:
  conditions:
    - type: Validated
      status: "True"
      lastTransitionTime: "2026-01-15T10:30:00Z"
      reason: ValidationSuccess
      message: "Spec is valid"
    - type: Stored
      status: "True"
      lastTransitionTime: "2026-01-15T10:30:00Z"
      reason: StorageSuccess
      message: "Spec stored in ConfigMap"

  observedGeneration: 1

Field	Description
`conditions[Validated]`	Whether the OpenAPI spec passed schema validation
`conditions[Stored]`	Whether the spec was successfully written to a ConfigMap
`observedGeneration`	The last spec generation reconciled by the operator

Security

API credentials via secrets

Never embed API keys or bearer tokens as plain strings in tool headers. Instead, inject them as environment variables in the agent container and reference them at runtime:

# 1. Store the credential in a Secret
apiVersion: v1
kind: Secret
metadata:
  name: sendgrid-credentials
stringData:
  api-key: "SG.xxx"

# 2. Inject the credential into the agent container env
apiVersion: agent.flokoa.ai/v1alpha1
kind: Agent
metadata:
  name: email-agent
spec:
  tools:
    - toolRef:
        name: send-email
  runtime:
    type: standard
    spec:
      container:
        name: agent
        image: ghcr.io/example/email-agent:v1.0.0
        env:
          - name: SENDGRID_API_KEY
            valueFrom:
              secretKeyRef:
                name: sendgrid-credentials
                key: api-key
        ports:
          - containerPort: 8080

Network policies

Use a NetworkPolicy to restrict which services your agents may reach through their tools:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: agent-egress
spec:
  podSelector:
    matchLabels:
      flokoa.ai/agent: customer-service-agent
  policyTypes:
    - Egress
  egress:
    # Allow DNS
    - to:
        - namespaceSelector:
            matchLabels:
              name: kube-system
      ports:
        - protocol: UDP
          port: 53
    # Allow only the inventory service
    - to:
        - podSelector:
            matchLabels:
              app: inventory-service
      ports:
        - protocol: TCP
          port: 8080

Troubleshooting

Tool is not appearing as callable in the agent

# Check the tool's validation and storage conditions
kubectl describe agenttool <name>

# Verify the tool is referenced correctly in the agent
kubectl get agent my-agent -o jsonpath='{.spec.tools}'

Confirm the Validated and Stored conditions are both True.
Check that toolRef.name and toolRef.namespace match the actual AgentTool metadata exactly.
Ensure the OpenAPI spec contains at least one operation (paths entry).

Timeout errors when the tool is called

# Increase the per-request timeout on the tool
spec:
  openApi:
    timeoutSeconds: 120

Also check whether the target service is under load, and whether any NetworkPolicies are delaying or dropping packets.

OpenAPI schema validation failures

# Read the full validation error from the condition message
kubectl get agenttool <name> -o jsonpath='{.status.conditions[?(@.type=="Validated")].message}'

For endpointPath: verify the path returns a valid JSON or YAML OpenAPI document.
For valueFrom: confirm the ConfigMap exists in the same namespace and the key contains valid YAML or JSON.
For value: check that the inline object is well-formed YAML with no tab characters.
Use an online validator (e.g., editor.swagger.io) to confirm spec correctness before applying.

Service resolution failures for internal services

# Verify the target Service exists
kubectl get svc inventory-service -n backend

# Test DNS from inside an agent pod
kubectl exec -it <agent-pod> -- nslookup inventory-service.backend.svc.cluster.local

# Test HTTP connectivity
kubectl exec -it <agent-pod> -- curl http://inventory-service.backend:8080/health

Ensure serviceRef.namespace matches the namespace where the Service lives.
Confirm serviceRef.port matches a port defined on the Service spec.
Check that NetworkPolicies in the target namespace allow ingress from the agent pod.

Best practices

Write a precise description — the LLM relies entirely on this text to decide when to call the tool, so be specific about what the API does and what inputs it expects.
Prefer endpointPath when your service serves its own OpenAPI spec — it keeps the AgentTool manifest minimal and the spec always up to date.
Use valueFrom for large or shared specs — storing the spec in a ConfigMap allows it to be managed independently and rolled back separately.
Use serviceRef for in-cluster targets — it avoids external DNS round-trips and integrates naturally with Kubernetes NetworkPolicies.
Set conservative timeouts — a stuck tool call blocks the entire agent turn; use timeoutSeconds that match the API’s p99 latency plus a buffer.
Share tools via a dedicated namespace — create reusable AgentTool CRs in shared-tools and reference them with toolRef.namespace rather than duplicating definitions.
Store API credentials in Secrets — inject them into the agent container environment rather than hard-coding them in tool headers.
Test tools in isolation before attaching them to agents — verify the target endpoint is reachable and returns a valid OpenAPI spec independently.
Version your tool manifests alongside application code so schema changes are auditable.

Get Started

Core Resources

Guides

Python SDK

Operations

AgentTool: connect agents to external APIs

API reference

Basic structure

OpenAPI schema sources

Target configuration

Using tools in agents

Status fields

Security

API credentials via secrets

Network policies

Troubleshooting

Best practices

​API reference

​Basic structure

​OpenAPI schema sources

​Target configuration

​Using tools in agents

​Status fields

​Security

​API credentials via secrets

​Network policies

​Troubleshooting

​Best practices

API reference

Basic structure

OpenAPI schema sources

Target configuration

Using tools in agents

Status fields

Security

API credentials via secrets

Network policies

Troubleshooting

Best practices