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Glama

jev_check_completion

Check requirement/evidence coverage before a full review. Returns per-requirement likelihoods and flags items that warrant closer inspection.

Instructions

Cheap requirement/evidence coverage check before an expensive full-task review. Use on large tasks with explicit requirements. Returns per-requirement likelihoods and which items warrant review. Never certifies correctness, safety, or merge readiness.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYes
clientNo
use_cacheNo
verificationNo
implementationYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses the tool's lightweight nature, its output (per-requirement likelihoods and review-warranted items), and its non-certification limitations. It does not mention side effects or external dependencies, but the key behavioral boundaries are present.

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 three concise sentences with the main purpose front-loaded. Every sentence adds value: what it does, when to use it, what it returns, and what it cannot vouch for. There is no filler or repetition.

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?

The description gives solid selection guidance and output expectations, and an output schema exists so return values are covered. However, for a tool with five parameters and nested objects, it leaves the construction of task and implementation inputs unexplained. It is adequate but not fully self-sufficient for correct invocation.

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 parameter meaning, but it does not. It hints at 'requirements' and 'evidence' but does not explain the roles of task, implementation, verification, use_cache, or client. This leaves most parameters semantically under-specified.

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 names a specific verb and resource: a cheap requirement/evidence coverage check performed before an expensive full-task review. It distinguishes the tool from a full review, though it does not explicitly differentiate it from the listed sibling tools by name.

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?

It gives an explicit use case ('Use on large tasks with explicit requirements') and a clear boundary ('Never certifies correctness, safety, or merge readiness'). It does not name alternative sibling tools, so the guidance is strong but not fully explicit on what to use instead.

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