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update-experiment

Rename an MLflow experiment by specifying its experiment ID and a new name.

Instructions

Rename an experiment

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
experimentIdYesExperiment ID
newNameYesNew experiment name

Implementation Reference

  • The handler function that executes the update-experiment tool logic. It calls mlflowClient.post('/experiments/update', ...) with experiment_id and new_name.
    export async function updateExperiment(params: z.infer<typeof updateExperimentSchema>) {
      assertWriteAllowed();
      return mlflowClient.post("/experiments/update", {
        experiment_id: params.experimentId,
        new_name: params.newName,
      });
    }
  • Zod schema defining the input parameters for update-experiment: experimentId (string) and newName (string).
    export const updateExperimentSchema = z.object({
      experimentId: z.string().describe("Experiment ID"),
      newName: z.string().describe("New experiment name"),
    });
  • src/index.ts:145-145 (registration)
    Registration of the update-experiment tool via the tool() helper, which registers it in the registry and conditionally adds it to the MCP server.
    tool("update-experiment", "Rename an experiment", updateExperimentSchema.shape, wrapToolHandler(updateExperiment));
  • wrapToolHandler wraps the handler with error handling (redaction patterns for MLflow basic auth, structured error extraction for MlflowError and WriteBlockedError).
    export const wrapToolHandler = createWrapToolHandler({
      // Defaults already cover api_key, authorization, bearer, password, secret, token.
      // Basic auth header values are MLflow-specific and not in the default set.
      redactionPatterns: [/basic\s+\S+/i],
      errorExtractors: [
        {
          match: (error) => error instanceof WriteBlockedError,
          extract: (error) => ({
            kind: "passthrough",
            text: (error as WriteBlockedError).message,
          }),
        },
        {
          match: (error) => error instanceof MlflowError,
          extract: (error) => {
            const err = error as MlflowError;
            return {
              kind: "structured",
              data: {
                message: err.message,
                status: err.status,
                body: err.body,
              },
            };
          },
        },
      ],
    });
  • assertWriteAllowed() is called by the handler to enforce read-only mode if MLFLOW_ALLOW_WRITE is not set.
    export function assertWriteAllowed(): void {
      if (!config.allowWrite) {
        throw new WriteBlockedError();
      }
Behavior2/5

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

No annotations are present, so the description carries the full burden. It does not disclose behavioral traits such as idempotency, permissions required, side effects, or return values. The description is minimally transparent beyond the action itself.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at only two words, which efficiently conveys the core purpose. However, it lacks any structure (e.g., bullet points) and could benefit from a bit more context without sacrificing brevity.

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 the domain with many sibling tools, the description lacks completeness. It does not explain how to obtain experimentId, constraints on newName, or what the tool returns. No output schema compensates.

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 input schema already describes both parameters (experimentId and newName) with 100% coverage. The description adds no additional meaning beyond the schema, so baseline 3 is appropriate.

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 states 'Rename an experiment,' which clearly indicates the action on a specific resource. It distinguishes from sibling tools like create-experiment, delete-experiment, and set-experiment-tag, though it could explicitly state that only renaming is supported.

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?

No guidance is provided on when to use this tool versus alternatives. For example, it doesn't mention prerequisites (e.g., experiment existence) or when to use other tools like set-experiment-tag for updates. The agent must infer from the schema and name.

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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