Italian B2B Lead List Scoring, Ranking & JSON Decisions

Map a general B2B business need for product research

map_business_need

Read-onlyIdempotent

Map any structured B2B business need from Italy or a foreign market for aggregate product research. Italy remains the only commercial sales scope; foreign signals are research-only. The tool accepts only closed semantic fields and never creates a product or order.

Input Schema

TableJSON Schema

Name	Required	Description
`country`	No	Legacy 1.2.0 compatibility for Italian demand. Omit it when origin_market is supplied.
`urgency`	No	How soon the capability would be useful; defaults to exploratory.
`recurrence`	No	Whether the need is one-off, recurring or continuous; defaults to unknown.
`budget_band`	No	Optional coarse willingness-to-pay band in EUR; never send payment data.
`need_action`	Yes	Primary action the machine wants the capability to perform.
`need_domain`	Yes	Broad business domain of the requested capability; use other only when none applies.
`need_object`	Yes	Business object or subject on which the action should operate.
`customer_type`	Yes	Only business demand is observed; this does not expand commercial eligibility.
`origin_market`	No	Self-declared coarse origin bucket used only for aggregate research; defaults to UNKNOWN_NOT_DECLARED. Foreign buckets never enable sales.
`desired_output`	Yes	Machine-readable or operational result the buyer wants.
`existing_product_fit`	No	Whether an existing MachineSignal product appears to fit; defaults to unknown.

Output Schema

TableJSON Schema

Name	Required	Description
`scope`	Yes
`boundaries`	Yes	Permanent safety boundaries of the public MCP discovery server.
`signal_type`	Yes
`order_created`	Yes
`origin_market`	Yes
`persists_data`	Yes
`mapping_status`	Yes
`need_signature`	Yes
`product_created`	Yes
`free_text_accepted`	Yes
`buyer_data_accepted`	Yes
`market_verification`	Yes
`existing_product_fit`	Yes
`checkout_url_returned`	Yes
`potential_product_gap`	Yes
`provider_call_enabled`	Yes
`commercial_eligibility`	Yes

TDQS

A3.6/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds valuable behavioral context: 'never creates a product or order', 'accepts only closed semantic fields', and distinguishes commercial vs research scope for different markets. No contradictions.

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 three concise sentences, front-loaded with the primary action, and each sentence adds essential information (purpose, scope, constraints). No waste.

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?

Given the complexity (11 parameters, 5 required, output schema present), the description covers purpose, behavioral traits, and market scope. It could be improved by adding usage guidance relative to siblings, but overall it is nearly complete.

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 detailed descriptions for each parameter. The tool description adds no additional parameter-level semantics beyond what is already in the schema, so baseline 3 is appropriate.

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 clearly states the tool maps structured B2B business needs for aggregate product research, specifying scope (Italy vs foreign) and constraints (closed fields, no product/order creation). It implicitly differentiates from siblings like 'prepare_order_intent', but does not explicitly name alternatives, so not a 5.

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

Usage Guidelines2/5

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

The description provides no explicit guidance on when to use this tool versus sibling tools (e.g., check_buyer_fit, get_product_catalog). It does not state prerequisites or cases where the tool should not be used.

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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