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Glama

list_scenarios

Lists supported synthetic fruit decision scenarios and their explicit limits so agents can identify valid scenario options for physical-AI evidence review.

Instructions

List supported synthetic fruit decision scenarios and explicit limits.

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.5/5.0
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 implies a read-only enumeration and mentions that limits are included, but says nothing about return shape, ordering, pagination, or authorization. That is thin for an unannotated tool, though the zero-parameter surface limits the risk.

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 definition is a single front-loaded sentence with no wasted clauses. It loses a point only because the key phrase 'synthetic fruit decision scenarios' is opaque rather than precise.

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?

For a simple, zero-argument list tool this is adequate but not complete. It does not define what a 'supported' scenario is, what the 'explicit limits' refer to, or what the returned list contains, and there is no output schema to compensate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the baseline is 4. There are no parameter semantics to explain or get wrong.

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?

It states a specific verb and resource ('List ... scenarios') and adds scope ('supported', 'explicit limits'). However, the domain term 'synthetic fruit decision scenarios' is unexplained and it does not explicitly contrast with the sibling review_scenario, so the agent must infer the 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?

Listing scenarios implies this is a discovery step before review_scenario, but the description never says when to use it, when not to, or that review_scenario is the follow-up. Usage is only implied.

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