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Glama

arabam_listing_detail

Fetches complete details for a used-car listing from arabam.com using its URL, including price, a 19-field Turkish spec table, and images.

Instructions

Full detail of one listing: title, price, 19-field spec table (Marka, Seri, Model, Yıl, Kilometre, Vites, Yakıt, Renk, Motor, hasar kaydı, Kimden…), images. url: the listing URL from arabam_search.

Input Schema

NameRequiredDescriptionDefault
urlYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It does disclose what content comes back and that a spec table plus images are included, which is meaningful behavioral context; however it says nothing about auth requirements, rate limiting, failure modes for expired/removed listings, or whether the call is a read-only scrape. With an output schema present, the return-value gap is forgivable, but the operational gaps remain.

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?

A single dense sentence front-loads the primary purpose, the parenthetical spec table documents scope economically, and the trailing parameter note is separated by a colon. No filler or redundant restatement of the tool name.

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 one-parameter retrieval tool with an output schema, the description supplies the essentials: what is returned, how much structure to expect, and where the url comes from. The main missing piece is robustness guidance around invalid or stale listing URLs, which matters for a scraper-style detail endpoint.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the only parameter is undocumented in the schema, yet the description compensates by stating the parameter's meaning and its source ('the listing URL from arabam_search'), which tells the agent the expected format and provenance. It stops short of giving an example URL shape or saying whether relative paths are accepted.

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 opens with a precise verb+resource framing ('Full detail of one listing') and then enumerates the returned content (title, price, 19-field spec table, images), which clearly separates it from arabam_search's multi-result listing behavior. It never names the sibling tools as alternatives directly, but 'one listing' plus the url-provenance note makes the distinction inferable.

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 clause 'url: the listing URL from arabam_search' establishes the intended workflow: search first, then fetch detail for a single result. There is no explicit when-not guidance (e.g., do not call for bulk listing data) and no statement of what happens if the URL is invalid, but the context for use is clear.

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