Italian B2B Lead List Scoring, Ranking & JSON Decisions

Prepare a local MS-DEC-250 order-intent template

prepare_order_intent

Read-onlyIdempotent

Return the canonical endpoint, OpenAPI, 17 required fields and a locally fillable MS-DEC-250 template. It accepts no buyer data and never creates an order.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`product_code`	Yes	Entry product whose order-intent template should be prepared locally without submitting it.

Output Schema

TableJSON Schema

Name	Required	Description
`boundaries`	Yes	Permanent safety boundaries of the public MCP discovery server.
`instructions`	Yes
`product_code`	Yes
`order_created`	Yes
`order_openapi`	Yes
`persists_data`	Yes
`submit_method`	Yes
`order_endpoint`	Yes
`required_fields`	Yes
`accepts_buyer_data`	Yes
`idempotency_header`	Yes
`preparation_status`	Yes
`request_schema_ref`	Yes
`local_fill_template`	Yes
`static_order_openapi`	Yes
`checkout_url_returned`	Yes
`provider_call_enabled`	Yes
`validation_profile_url`	Yes

TDQS

A4.5/5.0

Behavior5/5

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: 'It accepts no buyer data and never creates an order,' reinforcing safety and clarifying constraints 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 with no fluff. The description is front-loaded with the key action and output, making it easy for an agent to parse quickly.

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 single parameter, rich annotations, and presence of an output schema, the description covers all necessary information: what is returned, what is not done, and input constraints. No gaps.

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 a clear description for product_code. The tool description adds no additional meaning beyond the schema, which is adequate. Baseline 3 applies per guidelines.

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 what the tool does: 'Return the canonical endpoint, OpenAPI, 17 required fields and a locally fillable MS-DEC-250 template.' It uses specific verbs and resources, distinguishing it from sibling tools like check_buyer_fit or get_product_catalog.

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

Usage Guidelines4/5

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

The description implies when to use (preparing a local template) and clarifies what it does not do ('never creates an order'), but it does not explicitly mention when not to use or provide alternative tools.

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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