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Glama

Italian B2B Lead List Scoring, Ranking & JSON Decisions

Get controlled purchase requirements

get_purchase_requirements
Read-onlyIdempotent

Explain the machine-origin purchase prerequisites without accepting an order, returning checkout data or calling a provider.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
product_codeYesProduct whose controlled machine-origin purchase requirements should be returned.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
boundariesYesPermanent safety boundaries of the public MCP discovery server.
order_policyYes
product_codeYes
customer_scopeYes
legal_resourcesYes
ready_to_prepareYes
input_requirementYes
machine_onboardingYes
checkout_url_returnedYes
recommended_next_toolNo
mcp_order_tool_exposedYes
recommended_next_argumentsNo
next_step_after_preparationNo
authoritative_order_protocolYes
buyer_data_must_remain_localYes

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds that it does not call a provider or return checkout data, reinforcing the safe, explanatory nature. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single clear sentence that efficiently conveys purpose and constraints. Every part is meaningful, though slight wordiness ('without accepting...') could be tightened.

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?

With a simple parameter, comprehensive annotations, and an output schema present, the description adequately covers the tool's behavior. No major gaps are evident.

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 the single product_code parameter fully described via enum and description. The tool description adds no new parameter information beyond this, so baseline score of 3 applies.

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 explains machine-origin purchase prerequisites with a specific verb and resource. It distinguishes itself from siblings like check_buyer_fit and prepare_order_intent by focusing on requirements without order processing, but does not explicitly name alternatives.

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 the tool is for informational purposes by noting it does not accept orders or return checkout data. It lacks explicit guidance on when to use this tool versus siblings like get_product_catalog or map_business_need.

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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