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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

A4.4/5.0
Behavior5/5

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

The description discloses background execution with up to 10 parallel KYC calls, immediate job_id return, polling with kyc_screener_batch_result, and webhook callbacks. This goes beyond the annotations (readOnlyHint, destructiveHint) and provides substantial behavioral context.

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 front-loaded with core behavior and provides rich context about job polling and webhooks. It is slightly verbose where it summarizes entry fields already present in the schema, but remains efficient overall.

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?

Covers job submission, result retrieval, webhook alternative, use cases, and limits. The async parameter ambiguity and lack of error handling or job retention details prevent a higher score, though the output schema likely covers return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While schema coverage is 100%, the description introduces ambiguity with the `async` parameter. It states the tool 'returns immediately (<300ms) with a job_id', implying it is always async, but the schema's `async` parameter suggests it can be toggled (true or false) with a different timing (<200ms). This inconsistency could lead to incorrect invocation.

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 'Async batch variant of kyc_screener' and explains it accepts 1-100 names, returning a job_id immediately. This distinguishes it from the sibling kyc_screener and the result tool, giving a specific verb, resource, and scope.

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 lists use cases: 'bulk client onboarding, UBO list screening, or periodic AML refresh batches.' It also provides alternatives like registering a webhook via webhooks_manage instead of polling, showing clear when-to-use and alternative guidance.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.