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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
NameRequiredDescriptionDefault
asyncNoIf 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.
namesYesList of entities to screen (1-100). Each entry requires at minimum a name.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesUnique job identifier — pass to kyc_screener_batch_result
statusYes
batch_sizeYesNumber of names queued for screening
eta_secondsYesEstimated seconds until result is ready
submitted_atYesISO-8601 submission timestamp

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that the tool is async, returns in <300ms, runs up to 10 parallel KYC calls, and includes an eta_seconds hint. 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?

Description is fairly long but all sentences are informative. Starts with core purpose then details, use cases, and async behavior. Slightly dense but efficient.

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?

Comprehensive coverage for a batch async tool: purpose, parameters, behavior, use cases, alternatives, and webhook registration. Output schema exists. No gaps.

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 covers both parameters fully. Description adds context: type defaults to 'any', birthdate optional for disambiguation, names list size 1-100. Enhances understanding beyond schema.

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 identifies the tool as an async batch variant of kyc_screener, accepting 1-100 names and returning a job_id. It distinguishes itself from sibling tools like the synchronous kyc_screener and the polling tool kyc_screener_batch_result.

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

Usage Guidelines5/5

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

Explicitly states use cases: 'bulk client onboarding, UBO list screening, or periodic AML refresh batches'. Also provides alternatives: polling via kyc_screener_batch_result or registering a webhook via webhooks_manage.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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