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Polarion MCP Server

postProjectCustomFields

Creates custom fields for a specific Polarion project using its project ID. Use dry_run to preview the request before creating the resource.

Instructions

Creates a list of Custom Fields in the Project context. Scope: one project (requires a project ID). To act across all projects, use postGlobalCustomFields instead. Effect: creates a new resource on each call — calling it again with the same input creates a duplicate; it is not 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
NameRequiredDescriptionDefault
dry_runNoIf 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.
projectIdYesThe Polarion project ID (its URL segment, e.g. `myproject`), case-sensitive. Required to scope the request to one project; call getProjects to list valid IDs.
requestBodyYesCustom Fields Body

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.2.0
    • 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."
    • changedInput schema / properties / projectId / description
      Previous value: -"The Project ID."New value: +"The Polarion project ID (its URL segment, e.g. `myproject`), case-sensitive. Required to scope the request to one project; call getProjects to list valid IDs."
  2. Changed1 schema field changedv1.1.0
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "data": {
      +      "items": {
      +        "properties": {
      +          "id": {
      +            "example": "MyProjectId/workitems/epic",
      +            "type": "string"
      +          },
      +          "links": {
      +            "properties": {
      +              "self": {
      +                "example": "server-host-name/application-path/projects/MyProjectId/customfields/workitems/epic",
      +                "type": "string"
      +              }
      +            },
      +            "type": "object"
      +          },
      +          "type": {
      +            "enum": [
      +              "customfields"
      +            ],
      +            "type": "string"
      +          }
      +        },
      +        "type": "object"
      +      },
      +      "type": "array"
      +    }
      +  },
      +  "type": "object"
      +}
  3. Added

TDQS

A4.7/5.0
Behavior4/5

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

The description adds concrete behavioral detail beyond the annotations: each call creates a duplicate and the operation is not idempotent. It also discloses the dry_run preview is returned as an error-flagged result when an output schema is present, which is non-obvious behavior.

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?

Four sentences, each carrying distinct, decision-relevant information: what it creates, where it operates, what it is not, and how to preview safely. The most important scoping fact is front-loaded, and there is no filler.

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 rich input schema, the presence of a typed output schema, and annotations covering read-only/idempotency/destructiveness, the description covers the remaining contextual essentials: scope, sibling routing, non-idempotency, and dry_run behavior. Nothing needed to select or 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?

Schema description coverage is 100%, so the baseline is 3. The description adds extra meaning by recommending dry_run and explaining the error-flagged preview caveat, which goes beyond the schema's dry_run parameter text. This is meaningful additional context for invoking the parameters.

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: 'Creates a list of Custom Fields in the Project context.' It then distinguishes itself from the global sibling by name ('postGlobalCustomFields') and by scope, so an agent can choose correctly without opening the schema.

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

Usage Guidelines5/5

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

It states the condition for use ('Scope: one project (requires a project ID)') and the alternative for the opposite case ('To act across all projects, use postGlobalCustomFields instead'). It also gives a concrete pre-call strategy: use dry_run to preview the request.

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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