Italian B2B Lead List 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

Name	Required	Description	Default
`product_code`	Yes	Product whose approved synthetic output preview should be returned.

Output Schema

TableJSON Schema

Name	Required	Description
`example`	Yes	Approved synthetic product preview; never real buyer or company data.
`boundaries`	Yes	Permanent safety boundaries of the public MCP discovery server.
`canonical_example_url`	Yes

TDQS

A4/5.0

Behavior4/5

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful context about using synthetic data only and creating no order, which aligns with and reinforces the 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?

The description is extremely concise with two sentences that front-load the purpose and key constraints. No wasted words.

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?

The tool has an output schema, so return values are covered. The description provides adequate context for a preview tool, though it lacks details on potential errors or response size.

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% with enum and description for the single parameter. The description does not add further parameter details, so it meets the baseline but does not exceed it.

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 it provides a preview of JSON output for a specific product code, using a specific verb ('Preview') and resource ('JSON produced when MachineSignal scores...'). It distinguishes from sibling tools like check_buyer_fit or prepare_order_intent by focusing on previewing output rather than performing actions.

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 mentions 'uses approved synthetic data only and creates no order,' implying it is safe and non-destructive, but does not explicitly state when to use this tool versus alternatives like get_product_catalog or check_buyer_fit.

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

All Italian B2B Lead List Scoring, Ranking & JSON Decisions tools Inspect

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TDQS

A3.9/5.0

Disambiguation5/5

Each tool targets a distinct function: buyer fit check, catalog retrieval, purchase requirements, synthetic example, business need mapping, and order intent preparation. No two tools have overlapping purposes, making it clear to an agent which tool to use for each task.

Naming Consistency5/5

All six tools follow a consistent verb_noun snake_case pattern (e.g., check_buyer_fit, get_product_catalog). The naming is uniform and predictable, aiding agent selection.

Tool Count4/5

With 6 tools, the count is reasonable for a focused server. However, the server name 'B2B Lead Scoring' suggests tools for scoring leads, but the actual tools are more about product catalog and order preparation, creating a slight mismatch in perceived scope.

Completeness2/5

For a lead scoring server, critical tools such as score_lead, get_lead_score, or lead_ranking are missing. The existing tools cover pre-sales steps but not the core scoring functionality, leaving significant gaps for the stated purpose.

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