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MolTrust MCP Server

mt_salesguard_verify

Verify product provenance via MT Salesguard.

Checks whether a product ID has a valid ProductProvenanceCredential
issued by an authorized brand. Returns brand info, credential hash,
Base anchor, and risk level.

Args:
    product_id: Product identifier (e.g. "AIRMAX-90-WHITE-43")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
product_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations provided, the description must carry the full behavioral burden. It discloses what the tool returns (brand info, credential hash, Base anchor, risk level), which is useful, but it does not explain side effects, failure modes, or whether it performs external lookups. It also doesn't state that it is a read-only operation, though that is implied by 'verify'. This is adequate but not rich.

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 concise and front-loaded with the purpose. The opening sentence states the function, followed by a brief explanation and parameter details. The example is useful and not redundant. However, the first two sentences are slightly repetitive ('Verify product provenance' and 'Checks whether a product ID has a valid...'), which could be tightened, but it's not verbose overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple verify tool with one parameter and an existing output schema, the description covers the input and the key return values. It lacks usage guidance and behavioral details like error handling or prerequisites. Given the large sibling family, more differentiation would improve completeness, but for its simplicity it is minimally adequate.

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?

Schema description coverage is 0%, so the description must compensate. It provides a clear parameter description with an example format ('AIRMAX-90-WHITE-43'), which adds meaning beyond the bare 'Product Id' string type. This helps the agent construct valid inputs, though it doesn't specify constraints like length or allowed characters.

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 action ('Verify product provenance') and the resource ('MT Salesguard'). It further specifies what it checks (a valid ProductProvenanceCredential) and lists return fields, making the tool's purpose unmistakable. The example product ID adds concreteness. It distinguishes itself from generic verify tools by its focus on product provenance.

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 use when you need to verify a product's provenance credential, but it does not explicitly state when to use this tool over siblings like moltguard_credential_verify or mt_shopping_verify. There is no mention of alternatives or exclusions, leaving the agent to infer the appropriate context. The example clarifies the input format but not the selection criteria.

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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