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update_prompt_version

Assign or remove a label for a specific prompt version. Use null to clear the label after looking up IDs with list_prompt_labels.

Instructions

Update a specific prompt version's label assignment. This only assigns or removes a label, and null clears the label after you look up ids with list_prompt_labels.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
prompt_idYesPrompt ID or slug
version_idYesVersion UUID to update
label_idYesLabel ID to assign to this version, or null to remove the label

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesWhether the tool call succeeded and returned structured data
dataNoStructured success payload when ok is true
errorNoStructured error payload when ok is false

Implementation Reference

  • The actual handler/implementation of updatePromptVersion. It calls the underlying API via PUT to /prompts/{promptId}/versions/{versionId} with label_id data.
    async updatePromptVersion(
    	promptId: string,
    	versionId: string,
    	data: { label_id?: string | null },
    ): Promise<{ success: boolean }> {
    	await this.put(
    		`/prompts/${this.encodePathSegment(promptId)}/versions/${this.encodePathSegment(versionId)}`,
    		data,
    	);
    	return { success: true };
    }
  • Zod schema defining the input parameters for update_prompt_version: prompt_id (string), version_id (string), label_id (nullable string).
    updatePromptVersion: {
    	prompt_id: z.string().describe("Prompt ID or slug"),
    	version_id: z.string().describe("Version UUID to update"),
    	label_id: z
    		.string()
    		.nullable()
    		.describe(
    			"Label ID to assign to this version, or null to remove the label",
    		),
    },
  • Registration of the 'update_prompt_version' tool on the MCP server using server.tool(), with schema and handler function that validates label_id and calls the service.
    server.tool(
    	"update_prompt_version",
    	"Update a specific prompt version's label assignment. This only assigns or removes a label, and null clears the label after you look up ids with list_prompt_labels.",
    	PROMPTS_TOOL_SCHEMAS.updatePromptVersion,
    	async (params) => {
    		if (params.label_id === undefined) {
    			return {
    				content: [
    					{
    						type: "text" as const,
    						text: "Error: label_id is required — pass a label ID to assign, or null to remove the label",
    					},
    				],
    				isError: true,
    			};
    		}
    		await service.prompts.updatePromptVersion(
    			params.prompt_id,
    			params.version_id,
    			{
    				label_id: params.label_id,
    			},
    		);
    		return {
    			content: [
    				{
    					type: "text",
    					text: JSON.stringify(
    						{
    							message: `Successfully updated version "${params.version_id}" of prompt "${params.prompt_id}"`,
    							success: true,
    						},
    						null,
    						2,
    					),
    				},
    			],
    		};
    	},
    );
Behavior4/5

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

Annotations indicate mutation (readOnlyHint=false) but no destruction. The description adds that the tool only affects label assignment and clarifies null behavior ('null clears the label'). This adds meaningful context beyond the basic annotation flags.

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 concise sentences. The first states the core purpose, the second elaborates on null behavior and prerequisite workflow. No unnecessary words; all content earns its place.

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?

For a simple tool with three required parameters and an output schema, the description covers the essential: what it does, how null works, and where to get label IDs. No gaps for this complexity level.

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%, providing baseline 3. The description adds value by explaining the label_id parameter's role in assigning/removing labels and guiding the agent to use 'list_prompt_labels' for ID lookup. This goes beyond schema descriptions.

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 clearly states it updates a prompt version's label assignment, with specific verbs ('assigns', 'removes', 'clears'). It distinguishes itself from siblings like 'update_prompt' (which updates the prompt itself) and 'list_prompt_labels' (for looking up label IDs).

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 description provides clear usage context: it only assigns or removes a label, and recommends looking up IDs with 'list_prompt_labels'. It does not explicitly state when not to use it or list alternative tools, but the scope is well-defined.

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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