cyclesite-mcp-server

Suggest listing price

suggest_listing_price

Read-onlyIdempotent

For a seller about to list: suggested ask, floor, and ceiling for their bike's brand+model[+year][+condition] on the UK market. Same measured UK used-price data as the valuations, framed as seller guidance. When year is given, the ask is anchored on that model year's own median rather than the all-years median. Example: 'I'm selling a 2021 Specialized Allez in good condition, what should I ask?'.

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": {
      "brand": "<brand>",
      "model": "<model>"
    },
    "name": "suggest_listing_price"
  }
}

Input Schema

Name	Required	Description
`year`	No	Model year, e.g. 2021. Optional. Anchors the ask on that year when the curve covers it; `basis` in the response always names what the ask was actually derived from.
`brand`	Yes
`model`	Yes
`condition`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 15, 2026

addedInput schema / properties / year

Added value: +{
+  "description": "Model year, e.g. 2021. Optional. Anchors the ask on that year when the curve covers it; `basis` in the response always names what the ask was actually derived from.",
+  "type": "number"
+}

First observedMay 7, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description still adds non-obvious behavior: the ask is anchored on the model year's own median rather than the all-years median, and the response's `basis` field names what was actually used. That is genuine disclosure beyond the annotations.

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 the audience and the three returned numbers, then the data provenance, then the year nuance, then an example. Slightly redundant with the schema's own `year` text, but every sentence is doing work and the example clarifies the natural-language query shape.

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?

With an output schema present and rich annotations, the description need not explain return values or safety, and it covers audience, market scope, inputs and the one surprising behavior (year anchoring). The only gap is that it never signals what to do next after getting a price, e.g. draft_listing or publish_listing.

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 low at 25% — only `year` is documented, and its schema text already explains the anchoring and `basis` behavior. The description compensates by showing the optional-parameter pattern brand+model[+year][+condition] and that year and condition are optional, but it never says how condition moves the ask, nor explains the six condition enum values.

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 noun and outcome (suggested ask, floor, ceiling) for a named resource (brand+model[+year][+condition]) and scoped market (UK). It also explicitly separates itself from the valuations tool by noting the same data 'framed as seller guidance', which lets an agent tell it apart from get_valuation without reading either schema.

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?

'For a seller about to list' gives a clear triggering context, and the comparison to the valuations tool implies the buyer-side alternative. There is no explicit exclusion ('do not use for buying decisions, use get_valuation') and no mention of follow-on tools like draft_listing or publish_listing, so the routing help stops short of a full when/when-not statement.

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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