Collections Optimizer

Collections Optimizer — Get Engine Script (large books)

collections_optimizer_get_engine

Read-only

For books over 5,000 accounts or 20,000 rows of history, contact log or snapshots, or any such file already in your code sandbox. Returns a complete numpy-only Python script with the same engine as collections_optimizer; it reads accounts, history, contact log, snapshots, agents and test results, prints the decision cards, the queue and the do-not-contact list, and writes everything to collections_plan.json. No debtor data reaches this server on this path.

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": "collections_optimizer_get_engine"
  }
}

Input Schema

Name	Required	Description
`params`	No	Other inputs exactly as collections_optimizer takes them (jurisdiction, as_of, policy ...).
`agents_path`	No	Optional agents file.
`history_path`	No	Past accounts with paid / contacted outcomes.
`accounts_path`	No	Today's overdue accounts file (CSV/TSV/JSON).
`snapshots_path`	No	Optional monthly snapshots file.
`contact_log_path`	No	Optional contact attempts file.
`test_results_path`	No	Optional champion-challenger outcomes file.

Output Schema

Name	Required	Description	Default
`notes`	No
`paths`	No
`script`	Yes
`language`	No
`requires`	No
`writes_file`	No
`instructions`	Yes
`engine_version`	Yes
`scipy_required`	No
`data_requirements`	No
`network_access_required`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Beyond the annotations, the description discloses the return artifact, the script's behavior, the files it reads, the outputs it prints, and that it writes collections_plan.json. It also states an important privacy property: no debtor data reaches this server on this path.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the usage threshold, then explains the return value and side effects in a compact sequence. Every sentence contributes useful selection or behavioral context.

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?

Given the available output schema, 100% parameter coverage, and annotations, the description is largely complete. It relies on the sibling collections_optimizer to define the exact engine and parameter set, but it covers the key path-specific behavior and privacy constraint.

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

Parameters3/5

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 all seven parameters. The description only refers to them collectively as 'other inputs exactly as collections_optimizer takes them' and lists the file inputs, adding little semantic detail beyond the 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 states a specific verb and resource: it returns a complete numpy-only Python script with the same engine as collections_optimizer. It clearly distinguishes this path from the sibling tool by targeting books over specific size thresholds or files already in the code sandbox.

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 gives explicit triggering conditions: books over 5,000 accounts or 20,000 rows of history, contact log, or snapshots, or any such file already in the code sandbox. It does not explicitly say when to use collections_optimizer instead for smaller books, though the sibling and threshold context imply the alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Collections Optimizer tools Inspect

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