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ascentkorea

Hubble MCP Server

by ascentkorea

crawl_google_suggest

Get keyword suggestions from Google Suggest for a given keyword in Korea, US, or Japan. Collects autocomplete data to inform SEO and content strategy.

Instructions

입력된 키워드에 대해 구글 서제스트에 나타난 키워드 수집 요청
args:
    keyword: str, 키워드
    gl: Literal['kr', 'us', 'jp'] = "kr", 국가 코드
returns:
    dict[Any] | None: 구글 서제스트 수집 결과
키워드 suggestions
한국 (gl: kr)
미국 (gl: us)
일본 (gl: jp)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYes
glNokr

Implementation Reference

  • The main handler function for the 'crawl_google_suggest' MCP tool. It sends a POST request to the Hubble API's /google_suggest endpoint with a keyword (q) and country code (gl), returns suggestions from Google Suggest.
    @mcp.tool()
    @async_retry(exceptions=(Exception), tries=2, delay=0.3)
    async def crawl_google_suggest(
            q: str,
            gl: Literal['kr', 'us', 'jp'] = "kr") -> dict[Any] | None:
        '''
        입력된 키워드에 대해 구글 서제스트에 나타난 키워드 수집 요청
        args:
            keyword: str, 키워드
            gl: Literal['kr', 'us', 'jp'] = "kr", 국가 코드
        returns:
            dict[Any] | None: 구글 서제스트 수집 결과
        키워드 suggestions
        한국 (gl: kr)
        미국 (gl: us)
        일본 (gl: jp)
        '''
    
        async with httpx.AsyncClient() as client:
            payload = {
                "q": q,
                "gl": gl,
            }
            headers = {"X-API-Key": HUBBLE_API_KEY}
            response = await client.post(
                f"{HUBBLE_API_URL}/google_suggest",
                headers=headers,
                json=payload,
                timeout=30.0)
            response.raise_for_status()
            return response.text
  • data_api.py:423-424 (registration)
    The tool is registered with the FastMCP server via the @mcp.tool() decorator, which registers 'crawl_google_suggest' as an MCP tool named 'hubble'.
    @mcp.tool()
    @async_retry(exceptions=(Exception), tries=2, delay=0.3)
  • The HUBBLE_API_KEY is loaded from environment variable and used in the X-API-Key header for authentication.
    HUBBLE_API_KEY = os.environ.get('HUBBLE_API_KEY')
  • The base URL for the external Hubble API service used by crawl_google_suggest.
    HUBBLE_API_URL = "https://alpha-listeningmind-mcp-api.ascentlab.io"
  • The async_retry decorator wraps crawl_google_suggest to retry up to 2 times on Exception with a 0.3s delay.
    def async_retry(exceptions=(Exception), tries=3, delay=0.3, logger=None):
        def wrapper(func):
Behavior2/5

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

The description indicates it's a read operation collecting suggestions, but lacks details on rate limits, potential blocking, or response structure. The return type 'dict[Any] | None' is vague and insufficient for an agent to understand what to expect.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively short but contains a docstring-like structure (args, returns). However, there is redundancy in listing the country codes twice, and the parameter mismatch adds confusion. Could be more concise.

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

Completeness2/5

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

Given no output schema, the description should explain the return format in detail. It only says 'dict[Any] | None', which is insufficient. Additionally, the parameter mismatch and lack of behavioral detail make the tool description incomplete for effective use.

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?

The schema has 0% description coverage. The description redundantly lists 'keyword' and 'gl' but inaccurately uses 'keyword' while the schema has 'q'. It adds minimal meaning by listing country labels for the 'gl' enum, but does not clearly explain the parameters' purposes.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states it collects Google Suggest keywords for a given keyword, which is clear. However, there is a mismatch between the parameter name in the description ('keyword') and the actual schema name ('q'), which could confuse an AI agent.

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?

No guidance is provided on when to use this tool versus its siblings (e.g., crawl_google_serp, crawl_google_trends). The description simply explains what the tool does without usage context.

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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