Skip to main content
Glama

Batch Evaluation

dcl_evaluate_batch

Evaluate multiple agent responses against policies in a single pre-action call, reducing processing fees and time while maintaining tamper-evident audit trails.

Instructions

PRE-ACTION Bulk Processing ($0.10). Evaluates multiple responses in a single call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYesList of items to evaluate, each shaped like {'response': str, 'policy'?: str}.
agent_idYesIdentifier of the agent that produced the responses.
payment_idNoOptional payment identifier returned by a previous call when payment is required

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNumber of items evaluated in this batch.
resultsYesPer-item evaluation results, in input order.
agent_idYesIdentifier of the agent whose responses were evaluated.
batch_idYesUnique identifier for this batch run.
Behavior3/5

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

The description adds cost information ('$0.10') and a 'PRE-ACTION' label, which hints at a paid/pre-paid action. It does not disclose potential side effects or requirements beyond what the schema already covers, but annotations already indicate readOnlyHint=false, so the description is not misleading. It provides some added value but lacks rich 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.

Conciseness4/5

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

The description is very concise: one sentence plus a cost note. It is front-loaded and efficient, but the term 'PRE-ACTION' is unexplained and may cause slight ambiguity, preventing a perfect score.

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?

With an output schema present and full parameter descriptions, the description does not need to explain return values. However, it does not clarify what kind of evaluation is performed (e.g., quality, safety, generic) or how payment works beyond the payment_id field. It is adequate but leaves some contextual gaps.

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 description coverage is 100%, with each parameter (items, agent_id, payment_id) having explicit descriptions. The tool description adds no additional parameter semantics, so it does not compensate beyond the schema. Baseline of 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 clearly states the tool evaluates multiple responses in a single call, providing a specific verb (evaluates), resource (responses), and scope (multiple/repeated). However, it does not differentiate from sibling evaluate tools by naming alternatives or specific evaluation criteria, so it is clear but lacks explicit sibling distinction.

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?

The phrase 'multiple responses in a single call' implies a usage context (when you have many responses), but there is no explicit 'when to use' or mention of alternatives. No guidance is given on when not to use it or how it compares to single-evaluation tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Fronesis-Labs/dcl-webhook'

If you have feedback or need assistance with the MCP directory API, please join our Discord server