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discovery

Demonstration scenarios (discovered → diagnosed → solved)

get_situations
Read-only

Demonstration scenarios (discovered → diagnosed → solved). The proof layer paired with brandscripts: concrete situations a product resolves. ?product filters; ?status opts into drafts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusNolive (default) | draft | all.
productNoFilter to one product.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
filterYes
contractYes
situationsYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already mark the tool read-only and non-destructive, so the description only needs to add contextual behavior. It adds that status can opt into drafts and that product filters scoping, which is useful, but it does not describe pagination, empty results, or related behavioral nuances. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loads the core concept, then gives the query-parameter hints. There is a minor redundancy: the first clause duplicates the title verbatim, but the rest of the sentence earns its place by adding the proof-layer and product-situation context.

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 read-only tool with two optional parameters and an output schema, the description adequately explains the resource and the available filters. It could clarify the 'discovered → diagnosed → solved' lifecycle, but that omission is not blocking given the schema and low invocation complexity.

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% and both parameters are well documented (status enum with default, product filter). The description's '?product filters; ?status opts into drafts' largely restates the schema rather than adding new meaning, so the schema carries the parameter semantics.

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 identifies the resource as 'Demonstration scenarios' and clarifies them as 'the proof layer paired with brandscripts: concrete situations a product resolves.' This is more specific than the bare title, though it doesn't explicitly state a retrieval verb beyond the tool name or directly contrast with a sibling.

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?

It implies when to use the tool by describing scenarios as proof-layer evidence for brandscripts and mentions filtering by product and opting into drafts, but it never explicitly says 'use this when' or names alternatives like get_brandscripts. The context is clear but exclusion/selection guidance is absent.

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

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