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JavidGlyv

Turbo.az MCP Server

by JavidGlyv

get_makes_models

Retrieve vehicle makes and models from Turbo.az to filter automotive listings and refine search queries.

Instructions

Fetches list of available makes and models on Turbo.az.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
makeNoMake name (to see its models). Leave empty for all makes.

Implementation Reference

  • Implementation of get_makes_models in scraper.py which performs the web scraping logic.
    async def get_makes_models(self, make: Optional[str] = None) -> dict:
        """Gets available makes and models."""
        
        url = f"{BASE_URL}/autos"
        
        def _scrape():
            driver = self._get_driver()
            
            try:
                driver.get(url)
                WebDriverWait(driver, 20).until(
                    EC.presence_of_element_located((By.CSS_SELECTOR, '.tz-dropdown[data-id="q_make"]'))
                )
                time.sleep(0.5)
                try:
                    driver.find_element(By.CSS_SELECTOR, '.tz-dropdown[data-id="q_make"] .tz-dropdown__selected').click()
                    time.sleep(0.4)
                except Exception:
                    pass
                if make:
                    make_opts = self._parse_tz_dropdown_options(driver, "q_make")
                    make_lower = make.strip().lower()
                    make_id = None
                    for val, label in make_opts:
                        if label.lower() == make_lower or make_lower in label.lower():
                            make_id = val
                            break
                    if not make_id:
                        return {"success": False, "error": f"Make not found: {make}"}
                    try:
                        make_cont = driver.find_element(By.CSS_SELECTOR, '.tz-dropdown[data-id="q_make"]')
                        make_cont.find_element(By.CSS_SELECTOR, ".tz-dropdown__selected").click()
                        time.sleep(0.3)
                        for el in make_cont.find_elements(By.CSS_SELECTOR, ".tz-dropdown__list .tz-dropdown__option"):
                            if (el.get_attribute("data-val") or "").strip() == make_id:
                                el.click()
                                break
                        time.sleep(0.5)
                    except Exception:
                        pass
                    model_opts = self._parse_tz_dropdown_options(driver, "q_model")
                    models = [label for _, label in model_opts]
                    return {"success": True, "make": make, "models": models}
                
                make_opts = self._parse_tz_dropdown_options(driver, "q_make")
                makes = [label for _, label in make_opts]
                return {"success": True, "makes": makes}
                
            except TimeoutException:
                return {"success": False, "error": "Page failed to load"}
            except Exception as e:
                return {"success": False, "error": str(e)}
        
        loop = asyncio.get_event_loop()
        return await loop.run_in_executor(None, _scrape)
    
    async def get_trending(self, category: str = "new", limit: int = 20) -> dict:
        """Gets newest/popular listings."""
        
        if category == "vip":
            url = f"{BASE_URL}/autos?q[extras][]=vip"
        elif category == "popular":
            url = f"{BASE_URL}/autos?order=view_count"
        else:  # new
            url = f"{BASE_URL}/autos"
    
        # Use search_cars function
        return await self.search_cars(limit=limit)
    
    def __del__(self):
        """Destructor - closes driver."""
        self._close_driver()
  • src/server.py:147-159 (registration)
    Tool definition/registration in server.py.
    Tool(
        name="get_makes_models",
        description="Fetches list of available makes and models on Turbo.az.",
        inputSchema={
            "type": "object",
            "properties": {
                "make": {
                    "type": "string",
                    "description": "Make name (to see its models). Leave empty for all makes."
                }
            }
        }
    ),
  • The handler logic in call_tool that routes to the scraper method.
    elif name == "get_makes_models":
        make = arguments.get("make")
        results = await scraper.get_makes_models(make)
        return [TextContent(type="text", text=json.dumps(results, ensure_ascii=False, indent=2))]
Behavior2/5

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 states it 'fetches list', implying a read-only operation, but doesn't address key aspects like authentication needs, rate limits, pagination, or error handling. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with zero waste, making it easy to parse quickly.

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?

Given the tool's low complexity (one optional parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage, behavior, and output format, which could be important for an agent to use it effectively. It meets the minimum threshold but has clear gaps.

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?

The description doesn't mention the 'make' parameter or add any semantic context beyond what's in the input schema, which has 100% coverage. The schema already describes the parameter as 'Make name (to see its models). Leave empty for all makes.', so the description provides no additional value. Baseline is 3 when schema coverage is high.

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 clearly states the action ('fetches list') and resource ('available makes and models on Turbo.az'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_cars' or 'get_car_details', which might also involve makes/models in some capacity, so it doesn't reach the highest level of specificity.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'search_cars' or 'get_trending'. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone.

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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