Agent Pricing Index

pricing_quote_check

FREE, give-to-get. Check a price you were quoted (or plan to quote) for an AI product against the vendor's own published list price and the category. Returns discount off list, position in the category and data freshness. The quote is added to the private benchmark (never published individually), which is the price of the answer.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "category": "<category>",
      "pricing_model": "<pricing_model>",
      "quoted_usd_month": "<quoted_usd_month>",
      "share_to_benchmark": "<share_to_benchmark>"
    },
    "name": "pricing_quote_check"
  }
}

Input Schema

Name	Required	Description
`seats`	No
`product`	No	Product name, e.g. Cursor.
`category`	Yes
`term_months`	No
`pricing_model`	Yes
`list_usd_month`	No	Optional: the list price you were shown, if different from the public page.
`quoted_usd_month`	Yes	Quoted monthly price in USD, per seat for per-seat.
`share_to_benchmark`	Yes	Must be true. The quote joins the private benchmark; it is never published individually.

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 27, 2026

TDQS

A3.8/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does disclose the critical non-obvious trait: the quote joins a private benchmark, is never published individually, and that submission 'is the price of the answer'. That is exactly the kind of data-handling disclosure a caller needs before submitting pricing. It does not mention authentication or any rate/volume limits, which keeps it short of a 5.

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?

Three tight sentences, front-loaded with the free/give-to-get hook and then the answer contents and the data-sharing condition. The closing clause about the price of the answer would be slightly obscure alone, but it reinforces the trade-off rather than padding.

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?

No output schema exists, and the description correctly enumerates what comes back: discount off list, position in category, and data freshness. For a write-ish tool with zero annotations it also covers the data-retention consequence, which is the main behavioral risk. A few input fields (seats, term_months, pricing_model) remain unaddressed, leaving a small gap for an 8-parameter tool.

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 coverage is 50%, and the description adds genuine meaning for category (category of AI product), list_usd_month (optional list price if different from the public page) and share_to_benchmark (consent to private benchmarking). However, seats, term_months and pricing_model are unexplained in both places, so the compensation for the coverage gap is only partial.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete verb+resource: check a quoted (or planned) price for an AI product against the vendor's published list price and category. It also previews the outputs (discount off list, category position, freshness), so the agent knows exactly what it gets. It never names the adjacent siblings (pricing_benchmark, pricing_submit, pricing_rows), so differentiation is left to inference.

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

Usage Guidelines3/5

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

'Check a price you were quoted (or plan to quote)' implies the trigger condition, and 'FREE, give-to-get' hints at the cost model. But there is no explicit when-not guidance and no routing against pricing_benchmark or pricing_submit, which an agent choosing among twelve pricing-related siblings badly needs.

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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