Skip to main content
Glama

Evaluate

evaluate

Run a Cookbook benchmark against a model or checkpoint, scoring datasets with configurable metrics and persisting evaluation artifacts. Use to measure model quality before or after training.

Instructions

Run a Cookbook benchmark and persist evaluation artifacts. This spends credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYes
backgroundNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior3/5

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

Annotation readOnlyHint=false only signals a write; the description usefully adds that the call spends credits and persists artifacts, which the annotations do not convey. Beyond that it says nothing about runtime, background execution (despite a 'background' parameter), idempotency, or failure/cancellation behavior.

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?

Two short sentences with no filler, and the action plus cost warning are front-loaded. Both sentences earn their place, though the total size is arguably thin for a tool with this much configuration surface.

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 complex tool with a deeply nested 2-parameter request object, no parameter descriptions, minimal annotations (readOnlyHint only), and no parameter guidance in prose, the description is far too sparse. An output schema exists, so return values need not be explained, but the invocation contract is effectively undocumented.

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

Parameters2/5

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

Schema description coverage is 0% and the description explains none of the nested request fields, leaving ambiguous pairs like benchmark vs benchmarks, target.model vs checkpoint_path, idempotency_key, and background undocumented. Defaults and enums in the schema carry some meaning, but the description contributes nothing.

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?

States a specific verb+resource ('Run a Cookbook benchmark') and adds the side effect ('persist evaluation artifacts'), which distinguishes it from the read-side siblings evaluation_get and evaluation_failures. It does not name a sibling explicitly, but the action is unambiguous.

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?

There is no statement of when to use this tool versus benchmarks_list, evaluation_get, evaluation_failures, or the training/experiment siblings. The only qualifier is the cost note, which advises caution but does not route the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.