Onboarding decision (bundle) — One call: APPROVE/REVIEW/REJECT onboarding gate + 0-100 risk score, fusing KYB counterparty vet, OFAC/CSL sanctions screening, and adverse US regulatory actions. Price: $0.50 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | business or counterparty name to screen | |
| domain | No | business domain (optional) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description only needs to add behavioral context beyond safety. It does so by disclosing that this is a single paid call ($0.50 USDC via x402) and by specifying the exact decision and risk-score output the agent should expect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one dense sentence that front-loads the tool's identity ('Onboarding decision (bundle)') and immediately states the output, data sources, and cost. Every clause earns its place; there is no filler or redundant restatement of the name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description compensates well by spelling out the return categories (APPROVE/REVIEW/REJECT) and the risk-score range. It also covers pricing and the payment mechanism, which are critical for agent invocation. It could mention score interpretation or thresholds, but that is not essential for selecting and calling the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters. The description adds no meaning beyond what the schema provides: 'business or counterparty name to screen' and 'business domain (optional)' are already in the input schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as an onboarding decision bundle: one call returning APPROVE/REVIEW/REJECT plus a 0-100 risk score. It names the fused data sources (KYB, OFAC/CSL sanctions, adverse US regulatory actions), which distinguishes it from sibling tools like business_vet or legal_sanctions_screen that cover only part of this scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool — when a consolidated onboarding decision is needed rather than separate vetting or sanctions checks — but it never explicitly says 'use this instead of X' or gives exclusion criteria. The 'bundle' framing is useful context, but routing guidance is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools are clearly separated by prefix and data source, but several bundled products overlap heavily: vehicle_deal_check vs vehicle_report, realestate_property_report vs realestate_site_risk, finance_company_360 vs finance_health_scan, and domain_due_diligence vs email_domain_check/business_vet. An agent would frequently struggle to pick the correct premium bundle.
Tool names overwhelmingly follow a consistent snake_case category-prefix pattern like weather_, crypto_, vehicle_, finance_, and geo_. Minor deviations such as bare names (domain, ip) and noun-verb forms (dns_lookup, url_check) are easy to learn and don't create real confusion.
50 tools is far beyond the typical well-scoped 3–15 range and will require heavy filtering to navigate. The broad multi-domain data marketplace partially justifies the size, but it would be more coherent split into per-domain servers or consolidated further.
For a read-only data/diligence marketplace, the surface is quite comprehensive: weather, vehicle, crypto, SEC/finance, domain/email, sanctions, and geo workflows all have core operations plus fused verdict bundles. Minor gaps exist—such as a simple crypto price lookup or vehicle market value—but agents can usually work around them.