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Glama

jev_verify_completion

Validate step completion by scoring evidence against acceptance criteria to determine if done, preventing expensive extra review loops.

Instructions

Calibrates step completion against acceptance criteria using typed rubric scoring. Checks if evidence is sufficient to declare done without launching expensive extra review loops.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
produced_outputYesThe evidence, test results, code diff, or output produced.
acceptance_criteriaYesExplicit requirements, constraints, or definition of done.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool performs a calibration without launching expensive extra review loops, giving insight into its efficiency but not detailing side effects or return structure. However, since it's likely a read-only evaluation, the lack of further detail is acceptable.

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 two sentences with zero wasted words, front-loading the core purpose and adding a performance-related benefit. It is concise and structured well for LLM consumption.

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

Completeness4/5

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

For a two-parameter verification tool with 100% schema coverage and no output schema, the description is sufficient. It clarifies the tool's role and efficiency without needing to explain return values. However, it could mention typical return formats, but that may be a minor gap.

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?

The schema description coverage is 100%, so both parameters are already described clearly. The description adds context by mentioning 'evidence' and 'acceptance criteria' but does not add significant new semantics beyond what the schema provides. 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?

The description clearly states the tool's function: calibrating step completion against acceptance criteria using typed rubric scoring, and it distinguishes itself from vague alternatives by mentioning 'typed rubric scoring' and the specific purpose. This is a specific verb+resource combo that sets it apart from sibling tools like jev_triage_test_failure.

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 when to use it: when you need to verify completion without launching expensive extra review loops. It clearly states the tool avoids unnecessary additional processes, but it does not explicitly exclude any sibling tools or state when not to use them. Clear context is provided, but explicit alternatives are missing.

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