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DeepSpringAI

parquet mcp server

by DeepSpringAI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

parquet_mcp_server

铁匠徽章

一个强大的 MCP（模型控制协议）服务器，提供执行网页搜索和查找类似内容的工具。该服务器旨在与 Claude Desktop 配合使用，并提供两项主要功能：

  1. 网络搜索：执行网络搜索并抓取结果

  2. 相似性搜索：从之前的搜索中提取相关信息

该服务器特别适用于：

  • 需要 Web 搜索功能的应用程序

  • 需要根据搜索查询查找类似内容的项目

安装

通过 Smithery 安装

要通过Smithery自动为 Claude Desktop 安装 Parquet MCP 服务器：

npx -y @smithery/cli install @DeepSpringAI/parquet_mcp_server --client claude

克隆此存储库

git clone ...
cd parquet_mcp_server

创建并激活虚拟环境

uv venv
.venv\Scripts\activate  # On Windows
source .venv/bin/activate  # On macOS/Linux

安装包

uv pip install -e .

环境

使用以下变量创建.env文件：

EMBEDDING_URL=http://sample-url.com/api/embed  # URL for the embedding service
OLLAMA_URL=http://sample-url.com/  # URL for Ollama server
EMBEDDING_MODEL=sample-model  # Model to use for generating embeddings
SEARCHAPI_API_KEY=your_searchapi_api_key
FIRECRAWL_API_KEY=your_firecrawl_api_key
VOYAGE_API_KEY=your_voyage_api_key
AZURE_OPENAI_ENDPOINT=http://sample-url.com/azure_openai
AZURE_OPENAI_API_KEY=your_azure_openai_api_key

Related MCP server: my-mcp-server

与 Claude Desktop 一起使用

将其添加到您的 Claude Desktop 配置文件（ claude_desktop_config.json ）：

{
  "mcpServers": {
    "parquet-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/home/${USER}/workspace/parquet_mcp_server/src/parquet_mcp_server",
        "run",
        "main.py"
      ]
    }
  }
}

可用工具

该服务器提供两个主要工具：

  1. 搜索网页：执行网页搜索并抓取结果

    • 必需参数：

      • queries ：搜索查询列表

    • 可选参数：

      • page_number ：搜索结果的页码（默认为 1）

  2. 从搜索中提取信息：从以前的搜索中提取相关信息

    • 必需参数：

      • queries ：要合并的搜索查询列表

示例提示

以下是您可以与代理一起使用的一些示例提示：

对于网页搜索：

"Please perform a web search for 'macbook' and 'laptop' and scrape the results from page 1"

从搜索中提取信息：

"Please extract relevant information from the previous searches for 'macbook'"

测试 MCP 服务器

该项目在src/tests目录中包含一个全面的测试套件。您可以使用以下命令运行所有测试：

python src/tests/run_tests.py

或者运行单独的测试：

# Test Web Search
python src/tests/test_search_web.py

# Test Extract Info from Search
python src/tests/test_extract_info_from_search.py

您还可以直接使用客户端测试服务器：

from parquet_mcp_server.client import (
    perform_search_and_scrape,  # New web search function
    find_similar_chunks  # New extract info function
)

# Perform a web search
perform_search_and_scrape(["macbook", "laptop"], page_number=1)

# Extract information from the search results
find_similar_chunks(["macbook"])

故障排除

  1. 如果出现 SSL 验证错误，请确保.env文件中的 SSL 设置正确

  2. 如果未生成嵌入，请检查：

    • Ollama 服务器正在运行并可访问

    • 您的 Ollama 服务器上有指定的模型

    • 文本列存在于输入的 Parquet 文件中

  3. 如果 DuckDB 转换失败，请检查：

    • 输入 Parquet 文件存在且可读

    • 您对输出目录有写入权限

    • Parquet 文件未损坏

  4. 如果 PostgreSQL 转换失败，请检查：

    • .env文件中的 PostgreSQL 连接设置正确

    • PostgreSQL 服务器正在运行并可访问

    • 您具有创建/修改表所需的权限

    • pgvector 扩展已安装在您的数据库中

用于向量相似性搜索的 PostgreSQL 函数

要在 PostgreSQL 中执行向量相似性搜索，可以使用以下函数：

-- Create the function for vector similarity search
CREATE OR REPLACE FUNCTION match_web_search(
  query_embedding vector(1024),  -- Adjusted vector size
  match_threshold float,
  match_count int  -- User-defined limit for number of results
)
RETURNS TABLE (
  id bigint,
  metadata jsonb,
  text TEXT,  -- Added text column to the result
  date TIMESTAMP,  -- Using the date column instead of created_at
  similarity float
)
LANGUAGE plpgsql
AS $$
BEGIN
  RETURN QUERY
  SELECT
    web_search.id,
    web_search.metadata,
    web_search.text,  -- Returning the full text of the chunk
    web_search.date,  -- Returning the date timestamp
    1 - (web_search.embedding <=> query_embedding) as similarity
  FROM web_search
  WHERE 1 - (web_search.embedding <=> query_embedding) > match_threshold
  ORDER BY web_search.date DESC,  -- Sort by date in descending order (newest first)
           web_search.embedding <=> query_embedding  -- Sort by similarity
  LIMIT match_count;  -- Limit the results to the match_count specified by the user
END;
$$;

此函数允许您对存储在 PostgreSQL 数据库中的向量嵌入执行相似性搜索，返回满足指定相似度阈值的结果，并根据用户输入限制结果数量。结果按日期和相似度排序。

Postgres 表创建

CREATE TABLE web_search (
    id SERIAL PRIMARY KEY,
    text TEXT,
    metadata JSONB,
    embedding VECTOR(1024),

    -- This will be auto-updated
    date TIMESTAMP DEFAULT NOW()
);

Available Tools

2 tools
search-webC

Perform a web search and scrape results

ParametersJSON Schema
NameRequiredDescriptionDefault
queriesYesList of search queries
page_numberNoPage number for the search results

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It states that results are scraped but provides no details on output format, pagination behavior, rate limits, or error handling.

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?

The description is a single, concise sentence with no extraneous information, making it efficiently front-loaded.

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?

Without an output schema or annotations, the description is insufficient for a web search tool. It does not explain return values, result structure, or how scraping integrates with search, leaving significant 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?

Schema description coverage is 100%, so the schema already documents both parameters. The description adds no additional meaning beyond the parameter descriptions, meeting the baseline of 3.

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 tool's action: perform a web search and scrape results. It uses a specific verb-resource pair, but does not explicitly differentiate from the sibling tool 'extract-info-from-search'.

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 the sibling tool 'extract-info-from-search' or in what contexts it is appropriate.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv1.0.0
    • First observedextract-info-from-search
    • First observedsearch-web
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.5/5.0

Scored across 2 tools

Disambiguation4/5

The two tools have clearly distinct purposes: one for performing web searches and scraping results, the other for extracting information from those prior searches. While they are related, there is no functional overlap.

Naming Consistency5/5

Both tool names follow a consistent snake_case verb_noun pattern. 'search-web' and 'extract-info-from-search' both clearly indicate action and target.

Tool Count3/5

With only 2 tools, the server feels minimal for a web search and extraction domain. However, the tools cover a basic two-step workflow, making the count borderline acceptable.

Completeness3/5

The server provides a basic search and extraction cycle but lacks obvious features like managing search history, caching, or supporting different output formats. Minor gaps exist.

Maintenance

ActivityInactive
ResponsivenessNo issues

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