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fbmarket-mcp

fbmarket-mcp

by marchiani
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Get Comps

get_comps

Fetches comparable vehicle asking prices from collected listing history, expanding searches by +/-2 model years and +/-40,000 km to estimate market value for a given make, model, year, and mileage.

Instructions

What comparable vehicles are asking, from your own collected history.

Widens by +/-2 model years and +/-40,000 km. Needs roughly three weeks of daily scans before the numbers mean anything; the response says so when the sample is thin.

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": {
      "make": "<make>",
      "model": "<model>"
    },
    "name": "get_comps"
  }
}

Input Schema

NameRequiredDescriptionDefault
makeYes
yearNo
modelYes
mileage_kmNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the burden. It transparently notes the data collection requirement and that the response indicates thin samples, making the behavior reasonably predictable.

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 three short, focused sentences with no redundant wording or unnecessary detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

It provides useful context about output caveats and data requirements, but lacks explicit parameter definitions. The output schema exists, so not describing return values is acceptable, yet the parameter ambiguity leaves some gaps.

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?

Schema has no descriptions for parameters. The text mentions widening by model years and mileage, but it does not clarify how year and mileage_km parameters affect the comparison or what happens when they are omitted.

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 the tool returns price data for comparable vehicles based on collected history, distinguishing it from listing/search tools.

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?

It explains the tolerance for comparable matches and the need for at least three weeks of daily scans, giving practical guidance on when results are meaningful. It does not explicitly contrast with sibling tools but implies the data-sufficiency requirement.

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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