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remove-tag-from-entity

Remove specific tags from entities such as Kafka topics in Confluent Cloud using Schema Registry REST API, streamlining data management and organization.

Instructions

Remove tag from an entity in Confluent Cloud.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseUrlNoThe base URL of the Schema Registry REST API.
qualifiedNameYesQualified name of the entity. If not provided, you can obtain it from using the search-topics-by-tag tool. example: "lsrc-g2p81:lkc-xq8k7g:my-flights"
tagNameYesName of the tag to remove from the entity.
typeNameNoType of the entitykafka_topic

Implementation Reference

  • Core handler logic: parses input arguments, optionally sets schema registry endpoint, wraps client, and sends DELETE request to /catalog/v1/entity/type/{typeName}/name/{qualifiedName}/tags/{tagName} to remove the tag.
    async handle(
      clientManager: ClientManager,
      toolArguments: Record<string, unknown>,
    ): Promise<CallToolResult> {
      const { tagName, typeName, qualifiedName, baseUrl } =
        removeTagFromEntityArguments.parse(toolArguments);
    
      if (baseUrl !== undefined && baseUrl !== "") {
        clientManager.setConfluentCloudSchemaRegistryEndpoint(baseUrl);
      }
    
      const pathBasedClient = wrapAsPathBasedClient(
        clientManager.getConfluentCloudSchemaRegistryRestClient(),
      );
    
      const { response, error } = await pathBasedClient[
        "/catalog/v1/entity/type/{typeName}/name/{qualifiedName}/tags/{tagName}"
      ].DELETE({
        params: {
          path: {
            typeName,
            qualifiedName,
            tagName,
          },
        },
      });
    
      if (error) {
        return this.createResponse(
          `Failed to remove tag from entity: ${JSON.stringify(error)}`,
          true,
        );
      }
      return this.createResponse(
        `Successfully removed tag ${tagName} from entity ${qualifiedName} with type ${typeName}. Status: ${response?.status}`,
      );
    }
  • Zod schema defining the input parameters for the tool: baseUrl (optional), tagName (required), typeName (default 'kafka_topic'), qualifiedName (required).
    const removeTagFromEntityArguments = z.object({
      baseUrl: z
        .string()
        .describe("The base URL of the Schema Registry REST API.")
        .url()
        .default(() => env.SCHEMA_REGISTRY_ENDPOINT ?? "")
        .optional(),
      tagName: z
        .string()
        .describe("Name of the tag to remove from the entity.")
        .nonempty(),
      typeName: z
        .string()
        .describe("Type of the entity")
        .nonempty()
        .default("kafka_topic"),
      qualifiedName: z
        .string()
        .describe(
          `Qualified name of the entity. If not provided, you can obtain it from using the ${ToolName.SEARCH_TOPICS_BY_TAG} tool. example: "lsrc-g2p81:lkc-xq8k7g:my-flights"`,
        )
        .nonempty(),
    });
  • Registration of RemoveTagFromEntityHandler instance in the ToolFactory's static handlers Map using the tool name constant.
    [ToolName.REMOVE_TAG_FROM_ENTITY, new RemoveTagFromEntityHandler()],
  • Enum definition providing the exact string name 'remove-tag-from-entity' used for tool identification and registration.
    REMOVE_TAG_FROM_ENTITY = "remove-tag-from-entity",
  • ToolConfig method returning the tool name, description, and input schema for MCP tool registration.
    getToolConfig(): ToolConfig {
      return {
        name: ToolName.REMOVE_TAG_FROM_ENTITY,
        description: "Remove tag from an entity in Confluent Cloud.",
        inputSchema: removeTagFromEntityArguments.shape,
      };
    }
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool removes a tag from an entity, implying a mutation operation, but doesn't disclose critical behavioral traits such as required permissions, whether the operation is reversible, potential side effects, error conditions, or rate limits. This leaves significant gaps for an AI agent to understand the tool's behavior beyond the basic action.

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, clear sentence with zero wasted words—it directly states the tool's purpose without unnecessary elaboration. It's appropriately sized and front-loaded, making it easy for an AI agent to parse quickly.

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?

Given the complexity of a mutation tool with no annotations and no output schema, the description is incomplete. It lacks information on behavioral aspects (e.g., permissions, reversibility), output format, error handling, and how it relates to sibling tools. While the input schema is well-documented, the description doesn't compensate for the missing context needed for safe and effective tool invocation.

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?

The description adds no parameter-specific information beyond what's already in the input schema, which has 100% coverage with detailed descriptions for all four parameters. The baseline score of 3 reflects that the schema adequately documents parameters, so the description doesn't need to compensate, but it also doesn't provide additional context like examples or usage notes beyond the schema.

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 clearly states the action ('Remove tag') and target ('from an entity in Confluent Cloud'), providing a specific verb+resource combination. However, it doesn't explicitly distinguish this tool from sibling tools like 'delete-tag' or 'create-topic-tags', which handle related tag operations but with different scopes or actions.

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. While it mentions obtaining 'qualifiedName' from 'search-topics-by-tag' in the input schema, this is not part of the description text itself. There's no explicit context, prerequisites, or comparison to sibling tools like 'delete-tag' (which might delete tags entirely rather than remove them from entities).

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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