What can you do with this server?

The Insights Knowledge Base MCP Server is a plug-and-play knowledge base server with built-in reports and local data storage capabilities. * Search Functionality: Query reports using various criteria including keywords, title, content, publisher, and date range. Use search_report_profile for summaries and search_content_detail for detailed page content. * Advanced Search Options: Combine search criteria using either OR or AND logic for more precise results. * Automatic Translation: Keywords are automatically translated into both Chinese and English for broader searchability. * Result Management: Handle large result sets with pagination support. * Private Document Integration: Upload and parse private PDF documents into the database for future queries. * Attribution Support: Ensure proper attribution with markdown formatting when referencing reports. * Low Maintenance: Weekly updates to report database with bug fixes as needed.

Which integrations are available for this server?

Enables configuration of VLM models and parameters for private document parsing through environment variables Used for cloning and managing the knowledge base repository which contains the insight reports Provides access to report repository and allows users to contribute report sources through issues Serves as the runtime environment for the knowledge base with specific version requirements (3.12+)

How do I use Insights Knowledge Base MCP Server?

1. Click on "Install 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., "@Insights Knowledge Base MCP Server find recent reports on AI and sustainability trends" 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.

Insights Knowledge Base MCP Server

by v587d

Overview Schema Related Servers Score Discussions

Python

Local

Insights Knowledge Base(IKB) MCP Server

>>>中文版

🍭A free, plug-and-play knowledge base. Built-in with 10,000+ high-quality insights reports, packaged as MCP Server, and secure local data storage.

⚠️⚠️ All collected reports in this project come from free resources on official research report websites. ⚠️⚠️

Features

🍾 Zero configuration required, designed for plug-and-play usage.
🚀 Built-in Qwen3-Embedding-0.6B embedding model, related reports can be retrieved through vector search.📢 Report details can also be searched via keyword retrieval.
🍥 over 100 insights reports from well-known consulting firms such as McKinsey, PwC, and BAIN have been collected, including 6,000+ report pages, covering 70+ topics.
💎 Real-time online browsing of full reports in MCP Client.
🎉 Ultra-fast response: All Function_call returns typically <1 second, keyword-based queries <150ms.
🎨 Paste private local documents into the library_files folder (create it manually if absent; name must match). Configure VLM models/parameters in .env (e.g., VLM_MODEL_NAME=qwen2.5-vl-72b-instruct) for local document extraction, parsing, and recognition.
🦉 Permanently free—no wasted effort collecting reports. Share reliable, copyright-compliant resources via issues.
🔔 Commit to weekly report updates; bug fixes depend on personal whim (I'm not an engineer 🤭).

Related MCP server: MyAIGist MCP

Optimizations as of June 30

Added 2000+ report pages.

Future Directions

Continuous report updates.
Prompt engineering optimization.

Newest Files Profile

{
    "statistics": {
        "total_files": 174,
        "total_pages": 9320,
        "unique_publishers": 9,
        "unique_topics": 93,
        "last_updated": "2025-06-30T10:08:35.928329"
    },
    "details": {
        "publishers": [
            "",
            "Accenture",
            "BAIN",
            "BCG",
            "CBS",
            "Deloite",
            "McKinsey",
            "PWC",
            "亿欧"
        ],
        "topics": [
            "",
            "AI",
            "AI Agent",
            "Africa",
            "Aftermarket",
            "Asian American",
            "Auto",
            "Aviation",
            "Beauty",
            "Business",
            "Chemical industry",
            "Chemicals",
            "Chinese banking",
            "Chinese securities",
            "Consumer Goods",
            "Decarbonation",
            "Decarbonization",
            "Digital",
            "ESG",
            "Economy",
            "Economy and Trade",
            "Education",
            "Electric two wheelers",
            "Employment",
            "Energy",
            "Europe",
            "FMCG",
            "Fashion",
            "Finance",
            "Financial Technology",
            "Financial service",
            "Fintech",
            "Food-meatless",
            "Gen Z",
            "Global banking",
            "Global energy",
            "Global insurance",
            "Global macroeconomic",
            "Global materials",
            "Global private market",
            "Global private markets",
            "Global trade",
            "Grocery",
            "Grocery retail",
            "Health",
            "Healthcare",
            "Human capital",
            "Hydrogen",
            "Insurance",
            "Investing",
            "Investment management",
            "Labor market",
            "Latinos",
            "Low-altitude Economy",
            "Luxury Goods",
            "Luxury goods",
            "M&A",
            "Maritime",
            "Media",
            "Medical Health",
            "Medtech",
            "Net zero",
            "New Energy Vehicle",
            "New era",
            "Packing",
            "Payments",
            "Pet Food",
            "Population",
            "Power",
            "Private Equity",
            "Private market",
            "Productivity",
            "Quantum",
            "Real estate",
            "Retail",
            "Retail Digitalization",
            "Retailers",
            "Risk",
            "Small business",
            "Smart Home",
            "Smart hospital",
            "Sporting goods",
            "Sustainability",
            "Sustainable",
            "Tax-free",
            "Technology",
            "Travel",
            "Truck",
            "United Kingdom",
            "VSOC",
            "Wealth management",
            "Workplace",
            "连锁经营"
        ]
    }
}

Installation (Beginner-Friendly)

💡Pro tip: Stuck? Drag this page to an LLM client (like DeepSeek) for step-by-step guidance. Actually, these instructions were written by DeepSeek too...

Prerequisites: Python 3.12+ (Download from official website and ADD ENVIRONMENT PATH)

Install UV:

pip install uv

1. Clone the project(Confirm successfully installed Git and Git LFS)

git clone https://github.com/v587d/InsightsLibrary.git
cd InsightsLibrary
git lfs pull

2. Create virtual environment

uv venv .venv  # Create dedicated virtual environment

# Activate environment
# Windows:
.\.venv\Scripts\activate
# Mac/Linux:
source .venv/bin/activate

3. Install core dependencies

uv install .  # Note the trailing dot indicating current directory

4. Create environment variables (for future needs)

notepad .env  # Windows
# Or
nano .env     # Mac/Linux

5. Configure MCP Server

VSCode.Cline

Note: Replace <Your Project Root Directory!!!> with actual root directory.

{
  "mcpServers": {
    "ikb-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "<Your Project Root Directory!!!>", 
        "run",
        "ikb_mcp_server.py"
      ]
    }
  }
}

Cherry Studio
- Command: uv
- Arguments:

--directory
<Your Project Root Directory!!!>
run
ikb_mcp_server.py

Adding Private Documents to ikb_mcp_server

Configure VLM models and parameters in .env:

VLM_API_KEY=<API Key>
VLM_BASE_URL=<Base URL> # https://openrouter.ai/api/v1
VLM_MODEL_NAME=<Model Name> # qwen/qwen2.5-vl-72b-instruct:free

Upload the PDF document to the library_files folder under the project root directory.
Manually run main.py.

# Navigate to the project root directory
# Activate the virtual environment
uv run main.py
(InsightsLibrary) PS D:\Projects\mcp\InsightsLibrary> uv run main.py
[INFO] extractor: PDF extraction initialized | Files directory: library_files | Pages directory: library_pages
[INFO] extractor: Starting scan of directory: library_files
[INFO] extractor: Found 69 PDF files
[INFO] extractor: Scan completed | Total files: 69 | Processed: 0 | Failed: 0
[INFO] recognizer: No pages to process.
# Data has been updated to the database
============================================================
Confirm if you need to create text vector embeddings
⚠️ This process may take approximately 20 minutes
============================================================
Create embeddings? (Enter Y or N): 
# Y: create text vector embeddings
# N: Skip text vector embeddings and exit program

License

This project is licensed under the MIT License. See the LICENSE file for details.

Optimization Updates as of June 17th

💡Optimized models.py: Improved data query efficiency by 1,000%
💡Optimized extractor.py: Slightly enhanced PDF extraction efficiency
💡Optimized recognizer.py: Boosted image comprehension efficiency by 50%
💡Optimized ikb_mcp_server.py:
- Added pagination functionality
- Displayed local paths of referenced files
💡Add MIT License(https://github.com/v587d/InsightsLibrary/pull/1#issuecomment-2969226661)
📦 Overall compressed project package size reduced by approximately 50%
💡Streamline Private Document Handling
💡Fixed other identified bugs

Optimizations as of June 22

Added embedder.py: Implements text vectorization indexing via local Qwen3-Embedding-0.6B model, stored in faiss_index.
Modified main.py: Closed-loop workflow PDFExtractor → IMGRecognizer → Embedder (optional).
New @mcp.tool(): get_similar_content_by_rag: Finds most similar document content via vector similarity (RAG).
All admin-uploaded reports now support online viewing → Removed library_files folder to reduce project size.
Added 2000+ report pages.

Available Tools

3 tools

get_similar_content_by_ragA

该方法用于通过计算用户输入与文档内容向量之间相似度，进而找到最相似的文档内容，即RAG。 ⚠️注意：1. 当LLM无法从用户输入中提取明确指令时，优先使用此方法。 2. 当LLM引用该方法返回的结果时，必须用markdown格式明确、醒目告知用户引自哪篇报告和具体访问地址！比如“观点引自《21世纪CEO的成功法则》第10、16页。(查看完整报告)[<如果"download_url"不为空，填入download_url>]” ！！！注意每份报告单独列举 download_url，不要笼统指向某一个可能不存在的地址。

 参数：
    user_query: str 必填。

返回：
    page_number: 报告详情页页码
    page_title: 标题
    page_abstract: 摘要
    page_content: 内容
    file_name: 报告名称
    local_path: 报告存放于本地地址
    download_url: 报告网络链接
    published_by: 报告发布机构
    published_date: 报告发布日期
    vector_similarity: 向量相似度

LLM需将该方法返回结果组织成通畅的语言传达给用户。

ParametersJSON Schema

Name	Required	Description	Default
`user_query`	Yes

TDQS

A4.1/5.0

Behavior3/5

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

No annotations are provided, so the description carries full burden. It describes the tool as computing similarity and returning matched content, including warnings about citing results. It is transparent about the read-only nature (similarity search) but does not detail any side effects or permissions. Good overall, but lacks depth in behavioral context.

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 well-structured with sections for purpose, usage notes, parameters, and return fields. It is somewhat verbose due to detailed citation formatting instructions, but the core information is front-loaded and clear. Minor redundancy reduces conciseness.

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

Completeness4/5

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

With no output schema, the description thoroughly explains return fields (page_number, file_name, download_url, etc.) and provides usage context. It covers when to use and how to cite results. However, it does not explain technical details like similarity computation method or limits, leaving minor gaps.

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

Parameters4/5

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

Schema description coverage is 0%, but the description compensates by listing the parameter (user_query: str 必填) and its required status. While the schema lacks description, the tool description adds value by clarifying the parameter's role and requirements.

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 explicitly states that the tool computes similarity between user input and document content vectors to find the most similar documents (RAG). It clearly distinguishes from sibling tools (search_report_profile, search_content_detail) which serve different purposes.

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

Usage Guidelines4/5

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

The description provides a clear when-to-use instruction: '当LLM无法从用户输入中提取明确指令时，优先使用此方法' (When LLM cannot extract clear instructions, prefer this method). It also includes citation formatting guidelines. However, it does not explicitly state when not to use or mention alternatives.

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

search_content_detailA

该方法用于查询符合多条件组合的报告详情页面。LLM需根据用户输入的消息(user_message)提炼出以下参数。 ⚠️注意：当LLM引用该方法返回的结果时，必须用markdown格式明确、醒目告知用户引自哪篇报告和具体访问地址！比如“观点引自《21世纪CEO的成功法则》第10、16页。(查看完整报告)[<如果"download_url"不为空，填入download_url>]” ！！！注意每份报告单独列举 download_url，不要笼统指向某一个可能不存在的地址。

参数：
    keywords: List[str] = None, 报告详情页的关键词。
    title: str = "", 报告详情页标题包含词。
    content: str = "", 报告详情页内容包含词。
    publisher: str = "", 报告发布者。
    start_date: Optional[datetime] = None, 报告查询开始日期。
    end_date: Optional[datetime] = None, 报告查询结束日期。
    match_logic: str = "OR", 匹配逻辑。"OR" 或者 "AND"，二选一，**优先用 "OR"**。
    page_index: int = 1, 页码，默认仅显示第一页。

返回：
    results：报告详情
      - file_name: 详情页来自于哪份报告名
      - page_number: 页码
      - page_abstract: 摘要
      - page_content: 完整内容
      - page_keywords: 详情页关键词
      - published_by: 报告发布机构
      - published_date:报告发布日期
      - local_path: 报告存放于本地地址
      - download_url: 报告网络链接
      - matched_keywords: 匹配关键词组
    current_page：当前页码。⚠️当前页码小于总页码时，LLM需在结尾处提示用户可输入“下一页”查询更多记录。
    total_pages： 总页数
    total_matches： 总匹配记录条数

LLM需将该方法返回结果组织成通畅的语言传达给用户。

ParametersJSON Schema

Name	Required	Default
`title`	No
`content`	No
`end_date`	No
`keywords`	No
`publisher`	No
`page_index`	No
`start_date`	No
`match_logic`	No	OR

TDQS

A3.8/5.0

Behavior4/5

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

With no annotations, the description bears full burden. It explains the search behavior, pagination (current_page, total_pages), and the need to handle 'next page' prompts. It also notes the match_logic preference for 'OR'. However, it does not disclose potential side effects, but as a read-only search, this is adequate.

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

Conciseness2/5

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

The description is excessively long and mixes parameter documentation with LLM instructions (citation format, pagination prompting). While all content is valuable, it is not concise and could be restructured to separate behavioral guidelines from parameter details.

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

Completeness4/5

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

Given the tool's complexity (8 parameters, no output schema), the description covers return fields, pagination behavior, and citation requirements. It lacks details on error handling or empty results, but overall is fairly complete for invocation.

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

Parameters5/5

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

Schema description coverage is 0%, but the description compensates fully by explaining each parameter's purpose, type, default, and usage notes (e.g., match_logic choices, keywords as list). It adds significant value beyond the bare schema, making parameters self-explanatory.

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 purpose: querying report detail pages with multiple condition combinations. It uses a specific verb (查询) and resource (报告详情页面). While it does not explicitly distinguish from siblings, the purpose is distinct from search_report_profile and get_similar_content_by_rag.

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 based on user input to extract parameters and provides detailed instructions for presenting results (citation, pagination). However, it does not specify when not to use this tool or compare to alternatives, lacking explicit usage context.

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

search_report_profileA

该方法用于查询多条件组合的报告整体概况。LLM需根据用户输入的消息(user_message)提炼出以下参数。 ️⚠️注意：当LLM引用该方法返回的结果时，必须用markdown格式明确、醒目告知用户引自哪篇报告和具体访问地址！比如“观点引自《Open source technology in the age of AI》。(查看完整报告)[<如果"download_url"不为空，填入download_url>]” ！！！注意每份报告单独列举 download_url，不要笼统指向某一个可能不存在的地址。

参数：
    keywords: List[str] = None, 整篇报告的关键词。
    title: str = "", 报告标题包含词。
    content: str = "", 报告内容包含词。
    publisher: str = "", 报告发布者。
    start_date: Optional[datetime] = None, 报告查询开始日期。
    end_date: Optional[datetime] = None, 报告查询结束日期。
    match_logic: str = "OR", 匹配逻辑。"OR" 或者 "AND"，二选一，**优先用 "OR"**。

返回：
    results：报告概览
      - file_name: 报告名称
      - topic: 报告主题
      - content: 报告整体摘要
      - published_by: 发布机构
      - published_date: 发布日期
      - local_path: 报告存放于本地地址
      - download_url: 报告网络链接
      - matched_keywords: 匹配关键词组
    current_page：当前页码。⚠️当前页码小于总页码时，LLM需在结尾处提示用户可输入“下一页”查询更多记录。
    total_pages： 总页数
    total_matches： 总匹配记录条数

LLM需将该方法返回结果组织成通畅的语言传达给用户。

ParametersJSON Schema

Name	Required	Default
`title`	No
`content`	No
`end_date`	No
`keywords`	No
`publisher`	No
`page_index`	No
`start_date`	No
`match_logic`	No	OR

TDQS

A4/5.0

Behavior3/5

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

No annotations provided, so description must carry the burden. It discloses that it's a read-only search and pagination behavior, but doesn't mention authentication, rate limits, or side effects. Adequate but not comprehensive.

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 lengthy and mixes parameter definitions, usage instructions, and output format. While structured, it could be more concise. The markdown citation instructions add verbosity.

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

Completeness4/5

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

Given 8 parameters and no output schema, the description covers the return structure, pagination, and parameter usage. It lacks error handling or no results guidance, but is fairly complete for an LLM-oriented tool.

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

Parameters4/5

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

The description explains most parameters (keywords, title, content, publisher, dates, match_logic) with details like default and priority for match_logic. The page_index parameter is not described, but the overall coverage is high.

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 is for querying overall report profiles with multiple conditions. It distinguishes itself from sibling tools like search_content_detail by focusing on summary profiles.

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

Usage Guidelines4/5

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

The description provides explicit instructions for LLM to extract parameters from user messages, and how to cite results and handle pagination. It does not explicitly state when not to use, but the context is clear.

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 updatev1.0.1Jul 29, 2026
- Addedget_similar_content_by_rag
2 tool updatesv1.0.0Aug 15, 2025
- First observedsearch_content_detail
- First observedsearch_report_profile

Install Server

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

TDQS

A4/5.0

Disambiguation5/5

Each tool targets a distinct retrieval operation: report overviews (search_report_profile), page-level details (search_content_detail), and semantic similarity (get_similar_content_by_rag). There is no ambiguity in their purposes.

Naming Consistency4/5

All tool names use snake_case, with two starting with 'search_' and one with 'get_'. While the 'get_' prefix differs, the pattern is still predictable and descriptive. Minor inconsistency in verb choice.

Tool Count4/5

Three tools is appropriate for a focused knowledge base retrieval server. It covers the core query types without being excessive or insufficient for the stated purpose.

Completeness4/5

The tool set covers structured search by report profile and content, plus semantic search. For a read-only knowledge base, this is reasonably complete. Missing write operations are acceptable given the domain.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

Need Help?

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MCP directory API

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