Polarion MCP Server

generateCompletion

Generate LLM chat completions for Polarion REST API actions. Preview requests with dry_run to validate before sending without side effects.

Instructions

Generates a chat completion using a Large Language Model (LLM). Effect: triggers a server-side action rather than a plain resource creation — whether repeating it is safe, a no-op, or rejected depends on the specific action; treat it as not guaranteed idempotent. Tip: set dry_run: true first to preview the exact request Polarion would receive, without changing anything. On tools with a typed output schema, this preview is returned as an error-flagged result since it is not real tool output -- read the text content regardless of that flag.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`dry_run`	No	If true, validates the request and returns the exact request that would be sent to Polarion — with the Authorization header redacted and any binary payload summarized by byte length — without actually sending it.
`requestBody`	Yes	Generate completion parameters.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	No	The result of a completion generation request.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.2.0Sep 17, 2026

changedInput schema / properties / dry_run / description

Previous value: -"If true, validate and return the request that would be sent without calling Polarion."New value: +"If true, validates the request and returns the exact request that would be sent to Polarion — with the Authorization header redacted and any binary payload summarized by byte length — without actually sending it."

Changed1 schema field changedv1.1.0Sep 11, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "description": "Response body for LLM completion generation.",
+  "properties": {
+    "data": {
+      "description": "The result of a completion generation request.",
+      "properties": {
+        "message": {
+          "description": "A message in the LLM completion generation request or response.",
+          "properties": {
+            "content": {
+              "description": "Content of the message.",
+              "type": "string"
+            },
+            "role": {
+              "description": "Role of the message sender, e.g., 'user' or 'assistant.'",
+              "example": "user",
+              "type": "string"
+            }
+          },
+          "required": [
+            "content",
+            "role"
+          ],
+          "type": "object"
+        }
+      },
+      "required": [
+        "message"
+      ],
+      "type": "object"
+    }
+  },
+  "type": "object"
+}

AddedSep 7, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description goes well beyond the annotations by explaining that this triggers a server-side action, that repetition may be safe, a no-op, or rejected, and that idempotency should not be assumed. It also discloses the dry_run preview behavior, including the important quirk that the preview is returned as an error-flagged result despite being valid output. This is exactly the kind of behavioral context annotations alone do not provide.

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?

Three sentences, each earning its place: purpose, behavioral caveat, and actionable dry_run tip. The most important information is front-loaded, and there is no filler or redundant restatement of schema content.

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

Completeness5/5

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

Given the nested request body, the presence of an output schema, and annotations that already signal read/write and idempotency, the description covers the remaining operational essentials: what the tool does, how to preview safely, and what to expect from the preview result. Nothing needed to call the tool correctly is missing.

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?

The schema already provides 100% coverage of both parameters, so the baseline is 3. The description adds extra value for dry_run by clarifying that the preview is returned as an error-flagged result and should be read from the text content regardless of the flag, which is not stated in the schema. This enrichment justifies an above-baseline score.

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

Purpose5/5

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: 'Generates a chat completion using a Large Language Model (LLM).' This clearly distinguishes the tool from siblings like getLlms, which list LLMs rather than generate completions. The 'server-side action' clarification further positions it against resource CRUD operations.

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

Usage Guidelines4/5

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

The intended use case is obvious from the first sentence, and the dry_run tip gives concrete operational guidance. It does not explicitly name alternative tools or state when not to use this tool, but for a chat-completion endpoint the trigger condition is clear enough. The 'not guaranteed idempotent' note also helps an agent avoid unsafe repetition.

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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