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Plane MCP Server

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update_label

Modify an existing label in the Plane MCP Server by specifying project and label IDs, along with updated label attributes such as name, color, or description.

Instructions

Update an existing label

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
label_dataYesThe fields to update on the label
label_idYesThe uuid identifier of the label to update
project_idYesThe uuid identifier of the project containing the label

Implementation Reference

  • Handler function that executes the PATCH request to update a label via Plane API.
    async ({ project_id, label_id, label_data }) => {
      const response = await makePlaneRequest(
        "PATCH",
        `workspaces/${process.env.PLANE_WORKSPACE_SLUG}/projects/${project_id}/labels/${label_id}/`,
        label_data
      );
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(response, null, 2),
          },
        ],
      };
    }
  • Input schema definition for the update_label tool parameters.
    {
      project_id: z.string().describe("The uuid identifier of the project containing the label"),
      label_id: z.string().describe("The uuid identifier of the label to update"),
      label_data: LabelSchema.partial().describe("The fields to update on the label"),
    },
  • Registration of the update_label tool using server.tool() in registerMetadataTools.
    server.tool(
      "update_label",
      "Update an existing label",
      {
        project_id: z.string().describe("The uuid identifier of the project containing the label"),
        label_id: z.string().describe("The uuid identifier of the label to update"),
        label_data: LabelSchema.partial().describe("The fields to update on the label"),
      },
      async ({ project_id, label_id, label_data }) => {
        const response = await makePlaneRequest(
          "PATCH",
          `workspaces/${process.env.PLANE_WORKSPACE_SLUG}/projects/${project_id}/labels/${label_id}/`,
          label_data
        );
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify(response, null, 2),
            },
          ],
        };
      }
    );
  • Zod schema for Label object, used as base for label_data in update_label tool.
    export const Label = z.object({
      color: z.string().max(255).optional(),
      created_at: z.string().datetime({ offset: true }).readonly(),
      created_by: z.string().uuid().readonly(),
      deleted_at: z.string().datetime({ offset: true }).readonly(),
      description: z.string().optional(),
      external_id: z.string().max(255).optional(),
      external_source: z.string().max(255).optional(),
      id: z.string().uuid().readonly(),
      name: z.string().max(255),
      parent: z.string().uuid().optional(),
      project: z.string().uuid().readonly(),
      sort_order: z.number().optional(),
      updated_at: z.string().datetime({ offset: true }).readonly(),
      updated_by: z.string().uuid().readonly(),
      workspace: z.string().uuid().readonly(),
    });
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. 'Update an existing label' implies a mutation operation but doesn't specify required permissions, whether changes are reversible, error conditions, or what happens to unspecified fields. For a mutation tool with zero annotation coverage, this leaves critical behavioral aspects undocumented.

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?

The description is a single, efficient sentence with zero wasted words. It's appropriately sized for what it communicates and front-loads the essential action and resource.

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

Completeness2/5

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

For a mutation tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what fields can be updated, what the tool returns, error handling, or permissions required. The agent lacks critical context needed to use this tool effectively.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema fully documents all 3 parameters (project_id, label_id, label_data) and their nested properties. The description adds no parameter information beyond what's in the schema, which meets the baseline expectation when schema coverage is complete.

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

Purpose3/5

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

The description 'Update an existing label' clearly states the action (update) and resource (label), but it's vague about what specifically gets updated. It doesn't distinguish this tool from other update tools like update_issue or update_cycle, nor does it mention the specific fields that can be modified.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (like needing an existing label), when not to use it, or how it differs from sibling tools like create_label or get_label. The agent must infer usage from the tool name alone.

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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