Skip to main content
Glama
Sign In
errorbar

errorbar

by omnia-v
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

list labels

list_labels
Read-onlyIdempotent

Retrieve human pass/fail grades on logged requests, newest first, to audit or export the ground-truth verdicts judges are evaluated against.

Instructions

List the workspace's human pass/fail grades (ground-truth labels) on logged requests, newest first — use it to audit or export the verdicts every judge is measured against. GET /v1/labels (API-key scope: read). Returns: JSON { object: 'list', total: integer (matching rows across all pages), data: [ { id, request_id, verdict ('pass'|'fail'), critique: string|null, source ('human'|'assist_accepted'|'verifier'), scope ('request'|'trace'), created_at (ISO) } ] }. Notes: Offset pagination: page through with offset += limit until offset >= total. Rows include labels of every source (human, assist_accepted, verifier).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_labels"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoPage size. Default 50, clamped 1..200. Non-numeric or 0 falls back to the default. Default: 50.
offsetNoRows to skip (offset pagination). Default 0. Default: 0.
verdictNoFilter to one verdict. Any other value is ignored (no filter).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds substantial context: newest-first ordering, offset pagination mechanics, the fact that all sources are included, and the exact response shape. No contradiction with annotations.

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?

The description is front-loaded with the core verb, resource, ordering, and use case, then supplies endpoint, response shape, and pagination notes. Every sentence adds value, and the structure makes the pagination and source-inclusion caveats easy to find.

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

Completeness5/5

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

With no output schema, the description fully specifies the return JSON, including field types and enums. It also covers pagination and source semantics, leaving little ambiguity for an agent deciding whether and how to call this tool.

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%, so the schema already documents limit, offset, and verdict thoroughly. The description adds no parameter-specific meaning beyond the schema, but it does clarify that rows include all sources regardless of verdict filter, which slightly reinforces optional-filter behavior. Baseline 3 is appropriate.

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: 'List the workspace's human pass/fail grades... on logged requests, newest first.' It clearly differentiates from siblings like list_label_sets and create_label by focusing on ground-truth verdicts rather than label sets or label creation.

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?

Gives explicit use context: 'use it to audit or export the verdicts every judge is measured against.' It doesn't name alternative tools or exclusions, but the purpose is specific enough to guide tool selection against the large sibling list.

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

Deploy Server

Other Tools