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Glama

Evaluate many texts

evaluate_batch

Score up to 50 texts against a rule set in one call, getting a summary plus per-item verdicts to identify independent failures.

Instructions

Score up to 50 texts against one rule set in a single call. Each item counts as one evaluation. Returns a summary plus per-item verdicts; items can fail independently.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYes
rule_setNocontent-safety

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the return shape ('summary plus per-item verdicts'), failure independence ('items can fail independently'), and a business constraint ('Each item counts as one evaluation'). It does not cover authentication, rate limits, or invalid-input handling, but what it includes is genuine behavioral context.

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?

Three short sentences, each earning its place: the operation, the limit/constraint, and the result shape. Front-loaded with the most important information about batching.

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

Completeness3/5

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

For a two-parameter tool with no annotations and no output schema, the description covers result shape and partial-failure semantics, which is helpful. However, it omits parameter details, usage guidance versus siblings, and any mention of the default rule set beyond its existence, leaving meaningful gaps.

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%, so the description must compensate for the parameters. It only hints at 'texts' and 'one rule set', leaving the 'items' object structure (id, metadata), the 'input' constraints, and the 'rule_set' default undocumented.

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 states a specific verb ('Score'), a clear resource ('up to 50 texts against one rule set'), and a distinguishing constraint ('in a single call'). It clearly separates batch evaluation from the sibling single-item 'evaluate' tool.

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 implies batch use via 'up to 50 texts' and 'single call', giving clear context for when this tool fits. It does not explicitly name the alternative 'evaluate' for single-text use or state exclusions, so it stops short of full guidance.

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