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Italian B2B Lead List Scoring, Ranking & JSON Decisions

Get MachineSignal product catalog

get_product_catalog
Read-onlyIdempotent

Find an API product to score, classify, rank and prioritize an existing Italian B2B company or lead list into up to 250 explained JSON decisions. Returns the canonical catalog and permanent safety boundaries; read-only and no buyer data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
serviceYes
versionYes
productsYes
resourcesYesCanonical public MachineSignal resources.
boundariesYesPermanent safety boundaries of the public MCP discovery server.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint, and openWorldHint. The description adds context: 'Returns the canonical catalog and permanent safety boundaries; read-only and no buyer data.' This confirms the read-only nature and clarifies that no buyer data is involved, adding value beyond 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 concise: two sentences. The first sentence front-loads the core function, and the second provides return and behavioral details. Every sentence adds value without redundancy.

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?

Given the tool has no parameters, rich annotations, and an output schema, the description is complete. It explains the purpose, return value (catalog and safety boundaries), and behavioral traits. No additional information is needed for an agent to invoke it correctly.

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 input schema has no parameters, so baseline is 4. The description does not need to add parameter meaning since there are none. It correctly focuses on the tool's purpose and output.

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 tool's purpose: to find an API product that scores, classifies, ranks, and prioritizes Italian B2B companies into up to 250 JSON decisions. The verb 'find' and resource 'product catalog' are specific, and the scope distinguishes it from sibling tools like check_buyer_fit or get_purchase_requirements.

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 when needing to score, classify, rank, or prioritize Italian B2B companies, but it does not explicitly state when to use this tool versus siblings or provide exclusions. There is no direct guidance on alternatives, leaving some ambiguity for the agent.

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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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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