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Illumio MCP Server

by alexgoller
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get-workloads

Fetch workloads from the Illumio PCE, filtering by name, labels, status, or enforcement mode. Choose compact, full, or labels-only detail to match your analysis needs.

Instructions

Get workloads from the PCE. Use detail_level to control breadth vs depth: 'compact' (default) for tabular overviews of thousands of workloads, 'full' for complete data on specific workloads, 'labels_only' for maximum breadth with just identity and labels.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoFilter by workload name (supports partial matches)
labelsNoJSON-encoded list of label URIs to filter by
onlineNoFilter online (true) or offline (false) workloads
managedNoFilter managed (true) or unmanaged (false) workloads
hostnameNoFilter by hostname (supports partial matches)
ip_addressNoFilter by IP address (supports partial matches)
descriptionNoFilter by description (supports partial matches)
max_resultsNoMaximum number of workloads to return (default 10000)
detail_levelNoLevel of detail. 'compact' (default): tabular summary with key fields and labels. 'full': complete workload data including services, VEN agent, interfaces. 'labels_only': minimal table of href, name, hostname, and labels.compact
enforcement_modeNoFilter by enforcement mode

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed11 schema fields changedv0.8.0
    • addedInput schema / properties / description
      Added value: +{
+  "description": "Filter by description (supports partial matches)",
+  "type": "string"
+}
    • addedInput schema / properties / detail_level
      Added value: +{
+  "default": "compact",
+  "description": "Level of detail. 'compact' (default): tabular summary with key fields and labels. 'full': complete workload data including services, VEN agent, interfaces. 'labels_only': minimal table of href, name, hostname, and labels.",
+  "enum": [
+    "compact",
+    "full",
+    "labels_only"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / enforcement_mode
      Added value: +{
+  "description": "Filter by enforcement mode",
+  "enum": [
+    "visibility_only",
+    "full",
+    "idle",
+    "selective"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / hostname
      Added value: +{
+  "description": "Filter by hostname (supports partial matches)",
+  "type": "string"
+}
    • addedInput schema / properties / ip_address
      Added value: +{
+  "description": "Filter by IP address (supports partial matches)",
+  "type": "string"
+}
    • addedInput schema / properties / labels
      Added value: +{
+  "description": "JSON-encoded list of label URIs to filter by",
+  "type": "string"
+}
    • addedInput schema / properties / managed
      Added value: +{
+  "description": "Filter managed (true) or unmanaged (false) workloads",
+  "type": "boolean"
+}
    • addedInput schema / properties / max_results
      Added value: +{
+  "description": "Maximum number of workloads to return (default 10000)",
+  "type": "integer"
+}
    • addedInput schema / properties / name / description
      Added value: +"Filter by workload name (supports partial matches)"
    • addedInput schema / properties / online
      Added value: +{
+  "description": "Filter online (true) or offline (false) workloads",
+  "type": "boolean"
+}
    • removedInput schema / required
      Removed value: -[
-  "name"
-]
  2. Addedv1.0.0

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. It adds scale context ('tabular overviews of thousands of workloads') and characterizes each detail mode, but it does not disclose output shape, pagination, default result limits, or any side-effect/permission considerations. The behavioral info is limited to what the schema already hints at.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler. The action is front-loaded and the detail_level guidance is compactly organized by the three enum values with default called out.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only list tool with 10 fully self-describing parameters and no output schema, the description is workable but leaves gaps: it doesn't mention pagination, max_results behavior, or how results are returned (e.g., JSON array vs nested structure). It is adequate but not complete enough to raise above average.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining the breadth-vs-depth tradeoff and giving a practical rule of thumb for when each detail_level value is appropriate. This is meaningful enrichment of the most consequential parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource ('Get workloads from the PCE') and clarifies the output options via detail_level. It does not explicitly differentiate from sibling tools like get-kubernetes-workloads or get-workload-enforcement-status, but the core purpose is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives concrete guidance on choosing detail_level: compact for tabular overviews of thousands, full for specific workloads, labels_only for maximum breadth. However, it never says when to choose this tool over sibling alternatives such as get-kubernetes-workloads or get-traffic-flows, so tool-selection guidance is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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