Pinecone Developer MCP
OfficialPinecone 开发者 MCP 服务器
模型上下文协议(MCP) 是一项标准,允许编码助手和其他 AI 工具与 Pinecone 等平台进行交互。Pinecone 开发者 MCP 服务器允许您将这些工具与 Pinecone 项目和文档连接起来。
一旦连接,AI 工具可以:
搜索Pinecone 文档以准确回答问题。
帮助您根据应用程序的需求配置索引。
根据您的索引配置和数据以及 Pinecone 文档和示例生成代码。
在索引中插入和搜索数据,允许您在开发环境中测试查询和评估结果。
请参阅文档以获取更多详细信息。
此 MCP 服务器致力于提升开发者使用 Pinecone 作为其技术栈一部分的体验。它旨在与编码助手配合使用。Pinecone 还提供Assistant MCP ,旨在为 AI 助手提供来自您知识库的相关上下文。
设置
要配置 MCP 服务器以访问您的 Pinecone 项目,您需要使用控制台生成 API 密钥。即使没有 API 密钥,您的 AI 工具仍然可以搜索文档。但是,它将无法管理或查询您的索引。
MCP 服务器需要Node.js 。请确保node和npx在你的PATH中可用。
接下来,您需要配置您的 AI 助手以使用 MCP 服务器。
配置光标
要将 Pinecone MCP 服务器添加到项目,请在项目根目录中创建一个.cursor/mcp.json文件(如果它尚不存在)并添加以下配置:
{
"mcpServers": {
"pinecone": {
"command": "npx",
"args": [
"-y", "@pinecone-database/mcp"
],
"env": {
"PINECONE_API_KEY": "<your pinecone api key>"
}
}
}
}您可以在**“光标设置”>“MCP”**中检查服务器的状态。
要全局启用服务器,请将配置添加到主目录中的.cursor/mcp.json 。
建议使用规则来指导 Cursor 正确使用 MCP 服务器。请查看文档以获取一些建议。
配置 Claude 桌面
使用 Claude 桌面,通过导航至Settings > Developer > Edit Config找到claude_desktop_config.json文件。添加以下配置:
{
"mcpServers": {
"pinecone": {
"command": "npx",
"args": [
"-y", "@pinecone-database/mcp"
],
"env": {
"PINECONE_API_KEY": "<your pinecone api key>"
}
}
}
}重启 Claude 桌面。在新的聊天屏幕上,您应该会看到一个锤子 (MCP) 图标,其中包含可用的新 MCP 工具。
Related MCP server: Pinecone MCP Server
用法
配置完成后,您的 AI 工具将自动使用 MCP 与 Pinecone 交互。使用工具前,系统可能会提示您授予权限。您可以尝试让您的 AI 助手设置示例索引、上传示例数据或为您进行搜索!
工具
Pinecone Developer MCP Server 为 AI 助手提供了以下工具:
search-docs:搜索官方 Pinecone 文档。list-indexes:列出所有 Pinecone 索引。describe-index:描述索引的配置。describe-index-stats:提供有关索引中数据的统计信息,包括记录数和可用的命名空间。create-index-for-model:创建一个新索引,使用集成推理模型将文本嵌入为向量。upsert-records:使用集成推理在索引中插入或更新记录。search-records:基于文本查询在索引中搜索记录,并使用集成推理进行嵌入。提供元数据过滤和重新排序选项。cascading-search:跨多个索引搜索记录,对结果进行重复数据删除和重新排序。rerank-documents:使用专门的重新排名模型对记录集合或文本文档进行重新排名。
限制
仅支持集成推理的索引。不支持助手、未集成推理的索引、独立嵌入和向量搜索。
贡献
我们欢迎您携手改进 MCP 开发者体验。请在GitHub 问题跟踪器中提交问题。有关贡献的信息请参阅CONTRIBUTING.md 。
Available Tools
1 toolsearch-docsB
Search Pinecone documentation for relevant information
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The text to search for. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It states 'search' which implies a read-only operation, but does not disclose any behavioral traits such as result format, pagination, rate limits, or scope of documentation. This is insufficient for an agent to understand side effects or constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single clear sentence with no unnecessary words. It is front-loaded and efficient, though it could be slightly expanded to include more context without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description should explain what the search returns (e.g., relevant document snippets, titles, links). It does not, leaving the agent uncertain about the result format or how to interpret responses. This is a notable gap for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the single 'query' parameter, meaning the schema already explains the parameter. The description adds no additional meaning beyond the schema, earning the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as a search function for Pinecone documentation, specifying both the verb (search) and the resource (Pinecone docs). Since there are no sibling tools to distinguish from, the clarity is sufficient but not exceptional.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when searching Pinecone docs but provides no explicit guidance on when to use this tool, when not to, or any alternatives. Without siblings, explicit exclusions are less critical, but the lack of context still limits utility.
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. Dates show when Glama detected each change.
1 tool update
v0.2.1- Changed
search-docs1 field changed- removed
Input schema / additionalPropertiesRemoved value: -false
1 tool update
v1.0.0- First observed
search-docs
TDQS
With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly defined as searching Pinecone documentation.
With only one tool, naming consistency is trivially satisfied. The name 'search-docs' follows a clear verb_noun pattern.
A single search tool for a developer MCP server is far too limited. Pinecone developers typically need operations for managing indexes, vectors, and configurations, making 1 tool insufficient for the apparent scope.
The server is severely incomplete. A Pinecone Developer MCP should include tools for creating, listing, updating, and deleting indexes, as well as vector operations. Only a documentation search tool leaves major gaps.
Maintenance
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