Quote Price Optimization (Win Probability)

Quote Price Optimization — Get Engine Script (large exports)

quote_pricing_get_engine

Read-only

For exports above the quote_pricing caps (over 20,000 quotes or 50,000 tender bids), or files already in your code sandbox. Returns a complete numpy-only Python script with the same engine as quote_pricing; it reads the CSV / TSV / JSON files named in the call, prints the decision cards, and writes quote_pricing_result.json and quote_model_snapshot.json. No 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": "quote_pricing_get_engine"
  }
}

Input Schema

Name	Required	Description
`params`	No	Other inputs exactly as quote_pricing takes them (deal, job, ...).
`deals_path`	No	deals file in your sandbox (CSV / TSV / JSON).
`tender_path`	No	tender file in your sandbox (CSV / TSV / JSON).
`quote_history_path`	No	quote_history file in your sandbox (CSV / TSV / JSON).
`tender_history_path`	No	tender_history file in your sandbox (CSV / TSV / JSON).
`quote_model_snapshot_path`	No	quote_model_snapshot file in your sandbox (CSV / TSV / JSON).

Output Schema

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

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 14, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds substantial context beyond that: the returned script reads named CSV/TSV/JSON files, prints decision cards, writes quote_pricing_result.json and quote_model_snapshot.json, and no data reaches the 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 condition, then explains the return artifact and script behavior, then closes with the data-privacy note. Every sentence carries useful information and there is no redundant wording.

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?

Given the tool's complexity, annotations, and existing output schema, the description is complete enough for an agent to select and invoke it correctly. It covers when to use it, what it returns, what the returned script does, and the important fact that no data reaches the server.

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 each file path and the nested params object. The description adds useful framing that params are exactly as quote_pricing takes them and that files are CSV/TSV/JSON in the sandbox, but it does not add per-parameter semantics 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 action and artifact: it returns a complete numpy-only Python script using the same engine as quote_pricing. It also distinguishes this tool from its sibling by naming the export-size thresholds and sandbox-file scenario.

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?

It gives explicit conditions for use: exports above the quote_pricing caps (over 20,000 quotes or 50,000 tender bids) or files already in the code sandbox. The reference to quote_pricing caps makes the alternative path clear without needing an exhaustive catalog.

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

All Quote Price Optimization (Win Probability) tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.