Skip to main content
Glama

get_metadata_simulation

Read-only

Retrieve a saved listing metadata simulation by ID to inspect its four scored fields, assumed context, score, and findings for past ASO drafts.

Instructions

Read one saved listing draft in full: the four fields it was scored on, the context it assumed, the score it reached, and the findings behind it. This is what list_metadata_simulations cannot carry, and it is what makes a past run worth picking up: the text is here. Pass the "id" of a row from list_metadata_simulations, or the "saved.id" a simulate_metadata call returned. Read-only and instant.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe saved run id (numeric), from list_metadata_simulations or from simulate_metadata's saved.id

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, and the description agrees ('Read-only and instant'), adding a latency/behavioral note the annotation does not carry. It also describes the shape of the returned data, which is valuable given there is no output schema. It stops short of anything about pagination or error behavior, but for a single-record read that is minor.

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?

Front-loaded with the action and the returned contents, then the id sourcing, then the read-only note. The middle sentence ('This is what list_metadata_simulations cannot carry...') is slightly rhetorical but earns its place by justifying the tool's existence against a sibling.

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

Completeness5/5

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

For a one-param read tool with no output schema, the description compensates by enumerating the returned fields (scored fields, assumed context, score, findings). An agent has everything needed to decide to call it and to interpret the result.

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?

With one parameter at 100% schema description coverage, the schema already documents the id and its two possible origins. The description repeats 'the id of a row from list_metadata_simulations, or the saved.id a simulate_metadata call returned' without adding format or validation detail beyond the schema, so this is baseline-3 territory.

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?

States a specific verb and resource ('Read one saved listing draft in full') and enumerates exactly what the payload contains: the four scored fields, assumed context, achieved score, and findings. It explicitly contrasts itself with list_metadata_simulations and simulate_metadata, so an agent can distinguish it from siblings without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit routing: use this when you need the text that list_metadata_simulations cannot carry, and names both valid sources of the id (a row from list_metadata_simulations or simulate_metadata's saved.id). Both the when and the required input origin are stated rather than inferred.

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