gapup-mcp

kyc_screener_batch

Read-only

Async batch variant of kyc_screener. Accepts 1-100 names and returns immediately (<300ms) with a job_id. The screening runs in the background (up to 10 parallel KYC calls). Poll the result with kyc_screener_batch_result(job_id) after the eta_seconds hint. Each entry can specify name, type (person/company/any), and an optional birthdate hint. Use for bulk client onboarding, UBO list screening, or periodic AML refresh batches. Async tool — register a webhook via webhooks_manage(register, url, [job.completed]) to receive callbacks instead of polling. Faster + lighter.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`names`	Yes	List of entities to screen (1-100). Each entry requires at minimum a name.

Output Schema

TableJSON Schema

Name	Required	Description
`job_id`	Yes	Unique job identifier — pass to kyc_screener_batch_result
`status`	Yes
`batch_size`	Yes	Number of names queued for screening
`eta_seconds`	Yes	Estimated seconds until result is ready
`submitted_at`	Yes	ISO-8601 submission timestamp

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds value by explaining the async run, job_id, eta_seconds hint, and webhook option. 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 paragraph but well-organized: purpose, behavior, use cases, alternatives. Efficient with no wasted words, though could benefit from more structured formatting.

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?

For a complex async tool with an output schema (not shown), the description covers input, behavior, output (job_id), polling, and webhook alternative. Sufficient for an agent to decide usage.

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 coverage is 100%, so baseline 3. The description adds meaning by explaining names accept 1-100, type enum values, and birthdate for disambiguation, plus the async parameter's effect. This exceeds baseline.

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 it's an async batch variant of kyc_screener, accepting 1-100 names and returning a job_id. It distinguishes from the synchronous single by specifying batch size and async behavior.

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 provides context for use (bulk onboarding, UBO list screening, AML refresh) and mentions an alternative (webhook callback). It lacks explicit exclusion for when to use the non-batch variant but implies it through context.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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