Anweshna Risk Screening

bulk_screen

Upload up to 25 documents and screen them as one batch. Waits for text extraction, then starts scoring and returns job ids; poll get_bulk_status until the batch is finished. Each item is {title, content_base64 | text, filename?}. The 25-document limit is not the binding one for large files: the whole call must stay under 32 MB including base64 overhead, so send fewer, larger documents per batch rather than filling all 25 slots. Exceeding it is a bare HTTP 413 from the transport, before this tool runs. To RESUME a batch that was uploaded but not scored — this tool timed out waiting for extraction, or scoring failed to start — call it again with 'bulk_job_id' instead of 'documents'. That re-uses the existing upload and does NOT spend the account's quota a second time. If the batch is rejected because one or more documents are too large for the plan's scan engine, retry with force_precision=true to scan those documents truncated to the recall window (coverage beyond it is excluded).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "bulk_screen"
  }
}

Input Schema

Name	Required	Default
`engine`	No
`industry`	No
`documents`	No
`bulk_job_id`	No
`deal_room_id`	No
`document_type`	No	Report
`force_precision`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 19, 2026

TDQS

A4.7/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so: it discloses the 32 MB cap including base64 overhead, that the limit surfaces as a bare HTTP 413 from the transport before the tool runs, that it blocks on text extraction before returning, that resuming does not spend quota twice, and that force_precision truncates to the recall window and excludes coverage beyond it.

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?

Front-loaded with purpose and operationally dense throughout, with every sentence carrying distinct information. The 25-document versus 32 MB nuance and the 413 note take several sentences to make a single point, which is the only mild bloat.

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?

There is no output schema or annotation set, so the description must explain returns and failure paths; it covers job ids, the polling sibling, the 413, quota-safe resume, and the plan-limit retry. The only real gap is that the four unexplained optional parameters leave configuration ambiguity for an agent.

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 defines the documents item shape ({title, content_base64 | text, filename?}), the resume semantics of bulk_job_id, and the effect of force_precision, but leaves engine, industry, deal_room_id, and document_type entirely unmentioned, so the agent must guess at four optional parameters.

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?

States a specific verb and resource with explicit scope: 'Upload up to 25 documents and screen them as one batch.' It also distinguishes itself from siblings by naming get_bulk_status for polling and by framing itself against the implicit single-document case handled by screen_document.

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?

Gives explicit when-to-use conditions plus alternatives: poll get_bulk_status for progress, re-invoke with bulk_job_id to resume an unscored batch, and retry with force_precision=true when documents exceed the plan's scan engine. Each branch names the trigger and the correct action.

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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