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Italian B2B Lead Scoring, Ranking & JSON Decisions

Get a synthetic MachineSignal output example

get_synthetic_example
Read-onlyIdempotent

Preview the JSON produced when MachineSignal scores, classifies, ranks and prioritizes an Italian B2B lead list. Uses approved synthetic data only and creates no order.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
product_codeYesProduct whose approved synthetic output preview should be returned.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
exampleYesApproved synthetic product preview; never real buyer or company data.
boundariesYesPermanent safety boundaries of the public MCP discovery server.
canonical_example_urlYes

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context: 'Uses approved synthetic data only and creates no order'. This clarifies data source and confirms no side effects, going beyond annotations.

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?

Two sentences, front-loaded with the main purpose. Every sentence adds value—no wasted words.

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 simple tool with one enum parameter and an output schema, the description is complete. It explains purpose, data source, and side effects. No need to describe return values since output schema exists.

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 coverage is 100% and includes a description for the single parameter. The tool description does not add additional meaning about the parameter beyond what the schema provides, so 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 verb 'Preview' and the resource 'JSON produced when MachineSignal scores... an Italian B2B lead list'. It distinguishes from siblings which focus on checking fit, catalog, etc.

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?

The description implies usage for previewing synthetic output, but does not explicitly state when to use versus alternatives or when not to use. No sibling differentiation is mentioned in the description itself.

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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