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Glama

pricemyrepair-mcp

get_repair_cost

Get the typical UK cost range (GBP) for a specific car repair on a specific car model, from PriceMyRepair. Returns independent-garage and dealer estimates plus a link to the source page with engine-level detail and live garage quotes. Use for questions like "how much is a clutch on a Ford Fiesta in the UK". Covers 15 popular UK models plus a UK-average figure; use list_models and list_repairs to see what is available.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesCar model, e.g. "Ford Fiesta", "VW Golf", "Qashqai". Use "uk-average" if the model is unknown or not covered.
repairYesRepair or service, e.g. "clutch", "cambelt", "brake pads and discs", "full service", "MOT".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states that the tool returns independent-garage and dealer estimates plus a link to the source page, and it discloses scope limitations ('Covers 15 popular UK models plus a UK-average figure'). It could additionally note error or unknown-input behavior, but the main behavior and outputs are well explained.

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 concise and front-loaded, opening with the core purpose, then returning to what the agent receives, then usage and coverage. Every sentence adds value, and there is no redundant or filler content.

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?

For a simple two-parameter lookup with no output schema, the description covers the main purpose, return content, coverage limits, example usage, and discovery tools. It does not fully specify the exact output structure or error behavior, and it misses an explicit nod to get_model_costs, but the essential information for calling the tool correctly is present.

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 fully documents both parameters. The description adds useful context about coverage (15 models and UK-average) and gives examples, but it does not substantially add meaning beyond the parameter descriptions already present. Baseline 3 is appropriate.

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?

The description uses a specific verb and resource: 'Get the typical UK cost range (GBP) for a specific car repair on a specific car model.' It clearly states what the tool does and differentiates from the list_* siblings by indicating it returns costs rather than listing available models/repairs. However, it does not explicitly distinguish itself from the sibling get_model_costs, so it is not a full 5.

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 a concrete usage example ('how much is a clutch on a Ford Fiesta in the UK') and explicitly directs users to list_models and list_repairs to discover available data. The schema also guides using 'uk-average' for unknown models. It lacks an explicit 'when not to use this tool' or comparison with get_model_costs, but the guidance is otherwise clear.

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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TDQS

A4.2/5.0
Disambiguation4/5

The two getter tools have a slight overlap: get_model_costs returns all repairs for a model, while get_repair_cost targets one repair on one model, so a specific repair query could be served by either. However, the descriptions give clear intended use cases, and list_models/list_repairs are completely distinct.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: get_model_costs, get_repair_cost, list_models, list_repairs. This makes the set predictable and easy to navigate.

Tool Count5/5

Four tools is well-scoped for a read-only repair cost lookup service: two list endpoints for discovery and two get endpoints for retrieving costs. Each tool earns its place without redundancy.

Completeness5/5

The tool surface covers the full user journey: discover available models and repairs, then fetch either a comprehensive model cost table or a specific repair estimate. No obvious dead ends or missing operations exist for the stated purpose.