What can you do with this server?

This server provides AI-powered document parsing capabilities for financial and general documents, allowing AI clients to extract structured data from images or PDFs. * parse_bank_receipt: Parse bank receipt images or PDFs, with automatic cropping of multiple receipts per page. Extracts structured fields such as transaction date, summary, amount, account numbers, account holder names, bank names, and transaction reference numbers. * parse_bank_statement: Parse bank statement images or PDFs, returning structured account information and transaction details. * parse_invoice: Parse VAT invoice images or PDFs, extracting invoice code, number, issue date, amounts, and buyer/seller information. * parse_document: General document parsing to extract structured text content from various PDFs or images. * get_task_result: Query the results of previously submitted asynchronous parsing tasks using a task ID. All tools require a 云启智联 API Key and accept either a local file path or a remote file URL as input. An optional callback URL can be provided for asynchronous task notifications.

How do I use 云启智联 MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@云启智联 MCP Server 解析这张银行回单 /tmp/receipt.pdf" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

云启智联 MCP Server

Official

by yunqi-zhilian

Overview Schema Source Related Servers Discussions Admin

Python

Remote

云启智联 MCP Server

云启智联AI文档解析服务的 Model Context Protocol (MCP) 服务器，让 Claude、Cursor、Windsurf 等支持 MCP 的 AI 客户端直接调用票据识别能力。

功能

parse_bank_receipt — 银行回单解析（支持每页多张自动裁剪）
parse_bank_statement — 银行对账单解析
parse_invoice — 增值税发票解析
parse_document — 通用文档解析
get_task_result — 查询异步任务结果

Related MCP server: receiptconverter-mcp

前置要求

Python 3.10+
云启智联 API Key（从 http://8.135.62.13:5000 注册获取）

安装

pip install yqzl-mcp-server

或从源码安装：

git clone https://github.com/yunqi-zhilian/yqzl-mcp-server.git
cd yqzl-mcp-server
pip install -e .

启动

安装后，如果 yqzl-mcp-server 命令可用，直接运行：

yqzl-mcp-server

部分环境（尤其是 Windows）安装后脚本目录可能不在 PATH 中，导致命令找不到。此时可使用 Python 模块方式启动，效果完全相同：

python -m yqzl_mcp_server.server

在 Claude Desktop 中配置

编辑 ~/Library/Application Support/Claude/claude_desktop_config.json（macOS）或 %APPDATA%\Claude\claude_desktop_config.json（Windows）：

{
  "mcpServers": {
    "yqzl-ai": {
      "command": "yqzl-mcp-server",
      "env": {
        "YQZL_API_BASE_URL": "http://8.135.62.13:5000/AIService"
      }
    }
  }
}

如果系统提示找不到 yqzl-mcp-server 命令（常见于 Windows），可将 command 改为使用 Python 模块方式启动：

{
  "mcpServers": {
    "yqzl-ai": {
      "command": "python",
      "args": ["-m", "yqzl_mcp_server.server"],
      "env": {
        "YQZL_API_BASE_URL": "http://8.135.62.13:5000/AIService"
      }
    }
  }
}

配置完成后重启 Claude Desktop，即可在对话中让 Claude 调用票据识别工具。

在 Cursor 中配置

打开 Cursor Settings → MCP，添加 server：

Type: stdio
Name: yqzl-ai
Command: yqzl-mcp-server

如果命令找不到，可改用：

Command: python
Args: -m yqzl_mcp_server.server

使用示例

对 AI 说：

请用云启智联解析这张银行回单 /Users/xxx/receipt.pdf，API Key 是 as-xxxxxxxx。

AI 会自动调用 parse_bank_receipt 工具并返回结构化结果。

计费说明

MCP Server 不掌握计费逻辑。所有调用都通过你的云启智联 API Key 进行，按量从你在云启智联平台的余额中扣费。具体价格见官网。

自定义上游地址

默认使用 http://8.135.62.13:5000/AIService。域名备案完成后，可通过环境变量切换：

export YQZL_API_BASE_URL="https://yunqi-zhilian.com/AIService"

开发

pip install -e ".[dev]"
python -m yqzl_mcp_server.server

许可证

MIT

Available Tools

5 tools

get_task_resultB

根据 task_id 查询异步解析任务的结果。

ParametersJSON Schema

Name	Required	Description	Default
`api_key`	Yes
`task_id`	Yes

TDQS

B3.1/5.0

Behavior2/5

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

No annotations provided. The description lacks details on behavior such as polling requirements, error handling for incomplete tasks, or authentication specifics beyond the api_key parameter.

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?

One concise sentence with no fluff. However, it could be slightly more informative without losing conciseness.

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?

No output schema; description does not mention return format or structure. Lacks context on when to call (after task submission) and prerequisites.

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 implies task_id identifies the task, but api_key is unclear (likely authentication). With 0% schema coverage, only partial semantics are conveyed.

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

Purpose5/5

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

The description clearly states the verb 'query' and the resource 'result of asynchronous parsing task'. It effectively differentiates from sibling parse tools that initiate tasks.

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 on when to use this tool (e.g., after task submission) or not to use it (e.g., before completion). No alternatives mentioned.

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

parse_bank_receiptA

解析银行回单图片或 PDF，支持每页多张回单自动裁剪为单张。返回结构化字段：交易日期、摘要、金额、收支账号户名、银行、交易流水号等。

ParametersJSON Schema

Name	Required	Description
`api_key`	Yes	云启智联平台 API Key（从 http://8.135.62.13:5000 注册获取）
`file_path`	No	本地文件路径（与 file_url 二选一）
`file_url`	No	文件 URL 地址（与 file_path 二选一）
`callback_url`	No	可选：任务完成后的回调地址

TDQS

A3.6/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the auto-cropping feature and the structured return fields but lacks details on error handling, authentication beyond api_key, or limitations like file size or supported image formats.

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 concise with two sentences: one explaining the core functionality and the second listing output fields. It is front-loaded but could be slightly more structured (e.g., separated sections for process and output).

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 4 parameters, no output schema, and no annotations, the description covers the main purpose and return fields but lacks details on async behavior (callback_url), file format limits, timeouts, or integration with get_task_result. Adequate but incomplete.

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 baseline is 3. The description adds no additional nuance beyond the schema; it merely restates that callback_url is optional and the api_key is required. No parameter-level elaboration is provided.

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

Purpose5/5

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

The description clearly states it parses bank receipt images/PDFs, with automatic cropping of multiple receipts per page, and lists the structured fields returned. It distinguishes from siblings like parse_bank_statement and parse_invoice.

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

Usage Guidelines3/5

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

The description implies usage for bank receipt parsing but does not explicitly state when to use it versus alternatives like parse_bank_statement or parse_document. No exclusions or prerequisites are mentioned.

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

parse_bank_statementB

解析银行对账单图片或 PDF，返回账户信息、交易明细等结构化数据。

ParametersJSON Schema

Name	Required	Description	Default
`api_key`	Yes
`file_path`	No
`file_url`	No
`callback_url`	No

TDQS

B3.3/5.0

Behavior2/5

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

No annotations are provided, leaving the description to fully convey behavioral traits. It mentions returning structured data but omits critical details such as file format support, required permissions, error handling, or any side effects of the operation.

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, concise sentence that front-loads the primary action and output. No unnecessary words; every part contributes to understanding the tool's main purpose.

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 the tool has 4 parameters, no output schema, and no annotations, the description is too sparse. It lacks information on required input formats, parameter interdependencies, return value structure, and potential limitations, leaving significant gaps for an agent to use the tool correctly.

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 input schema has 4 parameters with 0% description coverage. The description adds context by mentioning image or PDF inputs, but fails to explain the specific role of each parameter (api_key, file_path, file_url, callback_url) or the constraints on their usage.

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

Purpose5/5

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

The description clearly states the tool parses bank statement images or PDFs and returns structured data like account info and transactions. It is specific to bank statements, distinguishing it from siblings like parse_bank_receipt or parse_invoice.

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

Usage Guidelines3/5

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

The description implies the tool is for bank statement parsing, providing some context for when to use it. However, it offers no explicit guidance on when not to use it or how it compares to alternatives like parse_document or parse_bank_receipt.

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

parse_documentC

通用文档解析，提取 PDF/图片中的结构化文本内容。

ParametersJSON Schema

Name	Required	Description	Default
`api_key`	Yes
`file_path`	No
`file_url`	No
`callback_url`	No

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as authentication requirements, rate limits, or what happens to the file. The api_key parameter hints at auth, but it's not explained.

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 a single sentence that is concise but lacks structure. It could benefit from being front-loaded with clearer scoping and parameter explanations.

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?

With 4 parameters, no output schema, and no annotations, the description is incomplete. It fails to explain inputs, outputs, or error handling, leaving significant gaps for the agent.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds no parameter-level meaning. It does not explain the purpose of api_key, file_path, file_url, or callback_url.

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

Purpose5/5

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

The description states it parses PDF/images to extract structured text, which clearly defines the verb+resource and distinguishes it from specialized siblings like parse_invoice and parse_bank_receipt.

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

Usage Guidelines3/5

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

No explicit guidance on when to use this tool vs specialized parsers (parse_invoice, etc.). It is implied for general documents, but not stated.

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

parse_invoiceB

解析增值税发票图片或 PDF，返回发票代码、发票号码、开票日期、金额、购销方信息等。

ParametersJSON Schema

Name	Required	Description	Default
`api_key`	Yes
`file_path`	No
`file_url`	No
`callback_url`	No

TDQS

B3.2/5.0

Behavior2/5

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

With no annotations, the description fails to disclose behavioral traits such as whether the operation is read-only, if it requires network access or authentication beyond the api_key, or any potential side effects. It only says 'parse' and returns data, lacking detail on process limitations.

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 sentence that immediately states the verb and resource. It is efficient but could be more structured by separating input requirements from output details.

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 4 parameters, no output schema, and no annotations, the description is incomplete. It does not explain the required combination of file_path or file_url, nor the asynchronous nature possibly implied by callback_url, nor what the return format looks like.

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?

Schema description coverage is 0%, yet the description does not explain any of the 4 parameters (api_key, file_path, file_url, callback_url). It only mentions the overall purpose, leaving parameter roles ambiguous.

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

Purpose5/5

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

The description clearly states the verb 'parse', the resource 'VAT invoice images or PDF', and lists specific return fields (invoice code, number, date, amount, buyer/seller info). It distinguishes from sibling tools that parse bank receipts, bank statements, or generic documents.

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

Usage Guidelines3/5

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

The description implies usage for invoices but does not explicitly state when to use this tool versus alternatives like parse_bank_receipt. No guidance on prerequisites or when not to use.

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.

5 tool updatesv0.1.0Jun 19, 2026
- First observedget_task_result
- First observedparse_bank_receipt
- First observedparse_bank_statement
- First observedparse_document
- First observedparse_invoice

Deploy Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.5/5.0

Scored 2026-06-19 04:25 across 5 tools

Disambiguation5/5

Each parse tool targets a distinct document type (bank receipt, bank statement, invoice, generic document), and get_task_result is clearly separate for async task management. No overlap.

Naming Consistency4/5

Most tools follow parse_* pattern, but get_task_result uses get_ instead. However, the action difference justifies the variation, and naming is otherwise consistent snake_case.

Tool Count5/5

5 tools is appropriate for a document parsing server, covering major document types and adding a task result retrieval tool without bloat.

Completeness4/5

Core document parsing types (receipt, statement, invoice, generic) are covered, and async result retrieval is included. Missing potentially other document types like contracts, but not a significant gap for the stated purpose.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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Appeared in Searches

发票识别

云启智联 MCP Server

功能

前置要求

安装

启动

在 Claude Desktop 中配置

在 Cursor 中配置

使用示例

计费说明

自定义上游地址

开发

许可证

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

DocumentPro MCPofficial

Appeared in Searches