Nanites

Get Pending Judgments

get_pending_judgments

Retrieve cleaned outputs with rubric and prompt context for pending orchestrator-judged units, optionally filtered by unit IDs, so the orchestrator can review and score them in the same turn.

Instructions

Fetch cleaned raw output plus rubric and prompt context for pending orchestrator-judged units (all, or a requested subset) for the orchestrator to read and judge in the same turn.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "profile": "<profile>",
      "model_id": "<model_id>"
    },
    "name": "get_pending_judgments"
  }
}

Input Schema

Name	Required	Description	Default
`profile`	Yes
`model_id`	Yes
`unit_ids`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 27, 2026

TDQS

B3.1/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It communicates that the tool fetches data for reading and judging, which suggests a non-destructive operation, and it discloses key content categories (raw output, rubric, prompt context). It does not disclose possible side effects, pagination, ordering, or how 'pending' is determined.

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 a single sentence and is reasonably compact, front-loading the main action. Some jargon ('cleaned raw output', 'orchestrator-judged units') adds density, but there is no filler or repetition.

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 tool with three parameters, no annotations, and no output schema, the description leaves key gaps: the meaning of `profile` and `model_id`, the format of the returned data, how subsets are expressed, and what 'cleaned raw output' actually contains. An agent would likely need external context to invoke this correctly.

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. It adds meaning for the optional subset concept ('all, or a requested subset') but never names or explains `unit_ids`. The required `profile` and `model_id` parameters are entirely unexplained, leaving the agent to guess their roles.

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 names a specific verb ('Fetch') and a specific resource: cleaned raw output plus rubric and prompt context for pending orchestrator-judged units. It clearly implies a read-oriented tool for judgment work, but it does not explicitly contrast with siblings like submit_test_judgment, so differentiation is somewhat implicit.

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 'for the orchestrator to read and judge in the same turn' implies when to use the tool, and 'all, or a requested subset' gives a scoping choice. However, there is no explicit guidance about when to prefer this tool over alternatives or when not to use it.

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

Deploy Server