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get_experiment_run

Retrieve an experiment run's report, including cell outcomes, per-metric summaries, and grid rows for debugging and analysis.

Instructions

Reads one Experiment run: how its cells ended, a summary per Metric, then the grid rows.

A run judges each chosen Session with each Evaluator; one cell per pair. counts says how the cells ended: errored is the Evaluator's own code breaking (see the cell's error_detail), not a low Metric; failed is a cell that never ran and can be retried. metrics summarises each Evaluator's Metric over every row, rows past max_rows included: mean, min and max for a Score, a count per label for a Label, and how often it did not apply. Each row is a Session with its cells; a cell's metrics carry the value per address (session, turn/2, ...) and the rationale the Evaluator wrote, which is what to group failures by. run.selection says whether the Sessions were SAMPLED or HAND_PICKED; only sampled runs say anything about traffic as a whole. :param pipeline_name: Name of the pipeline. :param experiment_id: The Experiment's id. :param run_id: The run's id. :param max_rows: Most rows to return; the summaries still cover every row. :returns: The run report, or an error message.

The output is automatically stored and can be referenced in other functions. Returns a formatted preview with an object ID (e.g., @obj_123). Use the object store tools in combination with the object ID to view nested properties of the object. Use the returned object ID to pass this result to other functions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idYes
max_rowsNo
experiment_idYes
pipeline_nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.29

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses truncation behavior ('summaries still cover every row'), the distinction between errored (Evaluator code breaking) and failed (never ran, retryable) cells, and the object-store side effect returning a preview with an @obj_123 ID. It omits auth requirements and rate limits, keeping it short of a 5.

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 core purpose is front-loaded in the first sentence, and the dense interpretation paragraph earns its space by defining counts, metrics and selection semantics. The trailing object-store lines repeat the same idea ('automatically stored' then 'Use the object store tools') and could be trimmed.

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

Completeness4/5

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

No output schema exists, so the description must explain return values, and it does so thoroughly (counts, per-Metric summaries, per-row cells, run.selection). It is also complete about the object-store handoff. Only the three identifier parameters lack any added semantics.

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 coverage is 0%, so the description must compensate. It adds real meaning only for max_rows ('Most rows to return; the summaries still cover every row'); pipeline_name, experiment_id and run_id are merely restated as 'Name of the pipeline' and 'The Experiment's id', adding nothing beyond the schema titles.

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?

States a specific verb and resource with scope: 'Reads one Experiment run: how its cells ended, a summary per Metric, then the grid rows.' This clearly distinguishes it from list_experiments and list_evaluators, which enumerate rather than report a single run.

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

Usage Guidelines3/5

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

Usage is only implied through interpretation hints such as 'which is what to group failures by'. It never states when to reach for this tool versus siblings like get_evaluation_try or list_experiments, and gives no prerequisites or exclusions. The agent must infer the routing.

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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