When we discovered that 70% of our team was already using AI tools daily—but with wildly varying results—we knew we needed a systematic approach. Not another vague "AI transformation" initiative, but a practical framework for separating AI hype from real value.

Here's the concrete system we developed to evaluate and implement AI tools across our company and what we've learned so far.

## The surprising reality we uncovered

When we began our AI journey at Meilisearch, we expected to find pockets of adoption. Instead, we discovered something more interesting: **universal experimentation but inconsistent value**.

### The adoption paradox

Our company-wide assessment revealed that every single team member had already experimented with AI tools, with an impressive 70% incorporating them into daily workflows.

![image.png](https://unable-actionable-car.media.strapiapp.com/image_40601b4a7c.png)

![image2.png](https://unable-actionable-car.media.strapiapp.com/image2_a867a1c935.png)

But the numbers told only half the story:

Despite high adoption rates, team sentiment about AI's value showed surprising variation. Many were using the tools but questioning whether they were truly beneficial or just creating a new kind of busywork.

![image3.png](https://unable-actionable-car.media.strapiapp.com/image3_67ad5244d9.png)

![image4.png](https://unable-actionable-car.media.strapiapp.com/image4_4a6d31dcca.png)

This gap between usage and perceived value became our first critical insight.

### Four critical insights that changed our approach

<Callout intent="note">

**Previously Shared Insights:**

- 🧠 [𝐃𝐨𝐞𝐬 𝐀𝐈 𝐛𝐞𝐜𝐨𝐦𝐞 𝐥𝐞𝐬𝐬 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞 𝐚𝐬 𝐲𝐨𝐮𝐫 𝐞𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐠𝐫𝐨𝐰𝐬?](https://www.linkedin.com/posts/gillianmcauliffe_aitransformation-professionalgrowth-futureofwork-activity-7292477225889660928-7UXm/#share&utm_medium=member_desktop&rcm=ACoAAAXkmQUBijf17cl9IKQZhMWLN67r9deqvAc) How your relationship with AI changes at different levels of expertise.
- 🔍 [**Unlocking Our AI Knowledge-Sharing Blueprint:**](https://www.linkedin.com/posts/gillianmcauliffe_aitransformation-futureofwork-knowledgesharing-activity-7290061146634485762-fm1D/#share&utm_medium=member_desktop&rcm=ACoAAAXkmQUBijf17cl9IKQZhMWLN67r9deqvAc) Understanding what people want to know to get started.
- 🤝 [Preserving Human Connection in an AI-Enhanced Workplace](https://www.linkedin.com/posts/gillianmcauliffe_aitransformation-teamwork-futureofwork-activity-7287036478092632066-boTG/#share&utm_medium=member_desktop&rcm=ACoAAAXkmQUBijf17cl9IKQZhMWLN67r9deqvAc): Will we lose what's special about our teams by bringing in AI?

</Callout>

Through in-depth interviews across all departments, we uncovered patterns that wouldn't have appeared in simple usage statistics:

1. 🌿 **Environmental impact:** Our teams have raised thoughtful concerns about the environmental impact of AI.
2. **🤔 The expertise paradox:** As team members' domain expertise increases, their perception of AI's value often follows a U-curve. Beginners find AI helpful for learning, experts leverage it as a powerful accelerator, but those in the middle sometimes find it more hindrance than help.
3. **🎯 The trust dilemma:** AI's "inconsistent brilliance" creates a fundamental trust issue—sometimes transformative, sometimes bafflingly off-target—making teams reluctant to integrate it into mission-critical processes.
4. **✨ Creative acceleration:** Despite skepticism, nearly everyone reported dramatic improvements in creative processes—from eliminating writer's block to transforming editing workflows.
5. 🔭 **Prompt sophistication gap:** Most team members were still using basic prompting techniques, unaware of how dramatically their results could improve with more advanced approaches.

This investigation revealed we weren't facing an adoption problem but a **value extraction challenge**. People were using AI tools but were not consistently getting meaningful results. We needed a framework to help teams identify where AI could actually create value—and where it was just adding complexity.

## ⚖️ Our solution: the AI value scoring matrix

Based on these insights, we developed a systematic framework to cut through the AI hype cycle. Rather than chasing the latest tools or models, we created a data-driven method to identify where AI could create genuine value for our specific context.

**The value scoring matrix:** Each potential AI use case is evaluated across multiple weighted dimensions aligned with our organizational priorities. This transforms the abstract question of "where should we use AI?" into a concrete, comparable set of scores.

**Our Six Critical Dimensions:**

| **Dimension** | **Weight** | **What We Assess** | **Why It Matters** |
|--------------------|------------|--------------------------------------------------------------------|----------------------------------------------------------------------------------|
| Feasibility | 20% | Implementation difficulty with current skills/resources | Prevents us from chasing technically impressive but impractical applications |
| Revenue Impact | 25% | Potential to increase top-line growth | Ensures AI initiatives contribute to business success |
| Data Readiness | 15% | Availability and quality of necessary data | Many AI projects fail due to data issues, not technology limitations |
| Time to Value | 15% | Speed to tangible results | Builds momentum with quick wins |
| Risk | 10% | Potential challenges or downsides | Protects against unintended consequences |
| Internal Impact | 15% | Number of people/teams benefiting | Prioritizes broad improvements over narrow optimizations |

Here's how we applied this framework to evaluate potential AI initiatives:

This systematic approach cut through subjective debates about which AI tools to adopt, replacing them with evidence-based decisions that aligned with our strategic priorities.

## Early results: beyond the AI hype cycle

Our scoring matrix has already transformed how we approach AI at Meilisearch:

### 1\. From endless options to strategic focus

Instead of drowning in the constant stream of new AI products, our teams now have a clear filter: "Does this tool address one of our high-scoring use cases?" This has dramatically reduced distractions and FOMO while increasing meaningful adoption.

### 2\. Balanced investment: "Ferraris" and "cargo ships"

We're strategically balancing our implementation:

- **"Ferrari" projects:** Quick wins that generate excitement and momentum (e.g., automating our internal documentation updates)
- **"Cargo ship" initiatives:** Substantial efforts with transformative potential (e.g., reimagining our customer support workflow with AI assistance)

### 3\. Creating a dedicated space for experimentation

One crucial lesson: AI adoption requires protected time. We've integrated dedicated "AI exploration blocks" into our work schedules and upcoming offsite, focusing specifically on our highest-scoring use cases.

### 4\. Addressing real concerns

The scoring matrix naturally incorporates ethical considerations through the risk dimension. We've operationalized this by:

- Favoring smaller, more efficient models when possible
- Prioritizing open-source options that allow us to run models locally
- Building in time for human review of AI-generated content

## The framework in action: a case study

After applying our scoring matrix to use cases identified through internal discussions, we quickly surfaced high-impact opportunities. One standout example was improving our code review process—specifically, reducing time to first review for pull requests (PRs).

This use case scored highly across multiple dimensions:

- High feasibility with existing tools
- Direct impact on development velocity
- Clear time-to-value metrics
- Broad internal impact across engineering teams

We implemented CodeRabbit, an AI-powered code review assistant that provides immediate PR feedback. While maintaining our essential human review process, the tool identifies common issues and provides automated PR descriptions. After a two-week trial period, we measured both quantitative metrics and gathered team feedback.

The results validated our scoring framework's prediction:

- Decreased average time to first review
- Improved PR documentation quality
- Positive team adoption and engagement
- Cumulative time savings across the engineering organization

## What's next: your turn

We're sharing this framework because we believe it can help other organizations cut through AI hype and focus on value creation.

The key to successful AI adoption isn't about having the fanciest tools—it's about having the clearest framework for identifying where AI truly adds value in your specific context.

🚀 How we're making AI work at Meilisearch

## What is RAG, and why should you care?

Every day, millions of people interact with ChatGPT, Claude, and other large language models, fundamentally changing how we interact with computers. The appeal is obvious: instead of learning specific interfaces, users can simply communicate in natural language. The machine adapts to the human, not the other way around.

This shift has organizations scrambling to create similar experiences with their own data. Whether it's building a support chatbot that understands product documentation, crafting an assistant that writes personalized emails, or developing an internal tool to help employees access company knowledge, they all require the same foundation: giving LLMs access to proprietary information.

Enter Retrieval Augmented Generation (RAG), a workflow that's deceptively simple in concept:

1. User asks a question
2. The system retrieves relevant documents based on that question
3. LLM generates an answer using those retrieved documents as context

When implemented correctly, RAG provides more accurate, up-to-date responses while reducing hallucinations. But here's where most engineers go wrong: they default to vector databases without considering whether that's actually the optimal approach.

## The engineer's shortcut: Vector databases

The typical RAG implementation looks something like this:

```
User Question → Convert to Embedding → Query Vector DB → Retrieve Similar Documents → Generate Answer

```

It makes intuitive sense. Vector embeddings capture semantic meaning, so similar concepts cluster together in the vector space. By converting questions and documents to the same embedding space, you can find relevant information even when the exact keywords don't match.

Engineers love this approach because it seems elegant. You don't need complex query parsing or keyword matching – just transform everything to vectors and let similarity algorithms do the work.

But this shortcut creates two critical problems that significantly degrade the quality of the results.

## Problem 1: Unrefined queries lead to irrelevant results

The first major issue occurs at the initial step: we take the raw user query, convert it directly to an embedding, and use that to retrieve documents.

Think about what happens when you use Google. You don't always search with your actual question – you distill it to key terms that are more likely to yield relevant results. If you want to know "What's the best way to organize React components in a large project?", you might search for "React component organization best practices large applications."

LLMs excel at this kind of query refinement, but in the standard vector DB approach, we skip this crucial step. The embedding captures the semantic meaning of the original question, but that doesn't always align with the optimal search query.

## Problem 2: Vector search sacrifices precision for recall

The second problem is more fundamental: vector similarity search is strong at recall but often weaker at precision compared to traditional full-text search.

Vector search excels at finding conceptually related content (recall) - documents that discuss similar topics, even if they use different terminology. However, it's less effective at pinpointing exactly what you need (precision).

Full-text search, on the other hand, prioritizes precision. When exact keyword matches exist, they're usually highly relevant. The trade-off is that full-text search might miss conceptually similar content that uses different terminology.

This explains why many RAG systems that rely exclusively on vector databases end up implementing additional re-ranking steps after initial retrieval. It's also why vector databases increasingly add full-text capabilities – they recognize the inherent limitations of pure vector search.

## A more human approach to RAG

Since we're trying to build systems that mimic human intelligence, shouldn't we structure our RAG pipelines to mirror how humans actually search for information?

When people need to find information, they:

1. Reframe their question into effective search terms
2. Use search engines optimized for retrieving relevant information
3. Scan results to identify the most useful content

A better RAG design would follow this pattern:

```
User Question → LLM Refines Into Search Query → Hybrid Search (Combining Full-text and Semantic) → Retrieve Relevant Documents → LLM Generates Answer

```

This approach addresses both problems:

1. The LLM refines the user's original question into an optimized search query
2. Hybrid search combines the precision of full-text search with the recall of semantic search

## The solution: Simpler (and better) RAG with search engines

Here's my contrarian take: instead of jumping to vector databases for RAG, start with a quality search engine that combines full-text and semantic capabilities.

Modern search engines like Meilisearch offer hybrid search functionality that delivers better overall relevance than pure vector retrieval. They're also:

- Easier to deploy and maintain
- Optimized for fast queries at scale
- Designed with relevance as the primary objective
- More intuitive for debugging search results

For most RAG applications, the workflow becomes much simpler:

1. The user submits a question
2. LLM transforms that question into an effective search query
3. The search engine retrieves relevant documents using hybrid search
4. LLM generates a response using those documents as context

This approach aligns with how humans actually search for information, leveraging the strengths of both LLMs and search technology while avoiding unnecessary complexity.

## The power of simplicity in RAG: Back to search basics

Vector databases certainly have their place in the machine learning ecosystem, but they're not always the best foundation for RAG systems. By taking a step back and considering how humans search for information, we can build more effective, simpler RAG architectures.

The next time you're designing a RAG system, consider whether a good search engine might be the better choice. Your users (and your future self maintaining the system) will thank you.

Why you shouldn't use vector databases for RAG

AI-powered search: What you need to know [2025]

The 10 best AI enterprise search tools and platforms [2025]

When [Bookshop.org](http://Bookshop.org)'s small engineering team struggled to push past a 14% search conversion rate, they faced a familiar dilemma. Despite having Elasticsearch—a powerful but complex search solution—their eight-person team was spending more time maintaining search infrastructure than improving their core platform. They needed something different: a solution that could deliver better results with less overhead.

Their challenge reflects a broader reality in modern software development. Every click, every search query, and every page load directly impact business metrics, yet the tools meant to optimize these experiences often become bottlenecks themselves. For engineering teams, especially smaller ones, the complexity of search infrastructure can become a _hidden tax_ on innovation and growth.

This is where Meilisearch comes in. Built to work seamlessly out of the box, it's the solution that lets developers focus on building rather than maintaining. Today, we're taking that mission further with [the launch of Meilisearch AI](https://meilisearch.org.cn/launch-week)—our next step in making it radically easier for developers to build world-class search experiences.

In this article, we'll explore how modern search has evolved beyond simple queries, examine the technical challenges facing development teams, and introduce Meilisearch's innovative solutions—including our new AI capabilities. You'll learn how our approach to developer experience is transforming search implementation, and discover our roadmap for making advanced search features accessible to teams of all sizes.

## Redefining modern search

Modern search experiences extend far beyond the traditional search bar, encompassing two key facets: search and discovery. 

Search can take the appearance of inputs, menus, filters, or the trending UX of conversational search (i.e. chatbots). Discovery, on the other hand, plays a key role in navigating content and suggesting recommendations.

Across industries, we see search and discovery most prevalent as follows:

- **Traditional full-text search & semantic search:** standard in all industries ranging from SaaS to ecommerce, marketplaces, and content platforms
- **Conversational search:** prominent in RAG (Retrieval-Augmented Generation) and AI-enhanced search interfaces like Perplexity
- **Discovery for navigation:** common in social media and content platforms where a feed is the primary navigation
- **Recommendations:** tie in more critically with business value in ecommerce and content platforms

For early-stage startups and SMBs, implementing these search capabilities presents significant technical challenges. The complexity multiplies when adding AI integration and personalization layers. Addressing these pain points diverts the workforce from building core product value. Meilisearch is here to relieve you from this burden, allowing you to focus on what really matters. 

We want Meilisearch built-ins to handle modern use cases like [document embedding](https://meilisearch.org.cn/docs/learn/ai_powered_search/document_template_best_practices). Complex setups—such as managing [heavyweight solutions like Elasticsearch](https://meilisearch.org.cn/blog/meilisearch-vs-elasticsearch), [tweaking a Postgres instance](https://meilisearch.org.cn/blog/postgres-full-text-search-limitations), or handling hybrid search—are additional integration burdens. This ignores the security consideration related to exposing front-end accessible APIs or the analytics necessary to collect business KPIs—things that don’t come packaged with custom-made solutions.

![bookshop_meilisearch.gif](https://unable-actionable-car.media.strapiapp.com/bookshop_meilisearch_9498082583.gif)

These DX considerations impact business metrics down the road as engineering bottlenecks lead to longer time-to-market, hindering growth for companies that want to move fast. In ecommerce, for example, search accuracy directly translates to conversion rate, as shown by how we helped [Bookshop increase search-based purchases by 43%](https://meilisearch.org.cn/blog/bookshop-increases-search-based-purchases). On SaaS and content platforms, discovery enables engagement and adoption growth, both of which are key to keeping churn in check. 

## Leveraging DX to move mountains

Meilisearch is a no-configuration, plug-and-play solution that you can install and forget. At the heart of any search tool—and often the biggest challenge for developers—is ensuring relevant results. **CascadeRank** is our answer to [relevancy fine-tuning](https://meilisearch.org.cn/docs/learn/relevancy/relevancy). Our algorithm uses human-readable ranking rules that enable configuration and easy customization. With Meilisearch, simplicity is key, allowing you to set up and focus on your core tasks. 

Though customization is easy, Meilisearch comes with strong defaults to handle common requirements out of the box—things like being typo-tolerant and prioritizing word proximity. Indeed, we’ve seen [Minipouce get better relevancy with Meilisearch’s default configuration than their previous Typesense setup](https://meilisearch.org.cn/blog/minipouce-top-grade-customer-experience), leading to a 5x search volume increase. 

Scalability is a concern for small teams and enterprises alike. Performance is the name of the game for user-facing search—both for search latency and indexing time. As demonstrated by [Podscan.fm](http://Podscan.fm) [searching across 600GB of data](https://x.com/arvidkahl/status/1807889668748275802), search remains fast against large datasets. Our [next-generation indexer boasts 4x faster updates](https://meilisearch.org.cn/blog/introducing-indexer-2024), making Meilisearch suitable at scale for write-intensive workflows, too.

[![arvid-kahl-tweet.png](https://unable-actionable-car.media.strapiapp.com/arvid_kahl_tweet_0c41da4824.png)
](https://x.com/arvidkahl/status/1807889668748275802)

When it comes to security, no compromise is possible. So Meilisearch provides fundamental security features to handle simple and complex use cases. Meilisearch facilitates [API key management](https://meilisearch.org.cn/docs/learn/security/basic_security) to facilitate multi-environments. The built-in [multi-tenancy features](https://meilisearch.org.cn/docs/learn/security/multitenancy_tenant_tokens) also enable more advanced security patterns, allowing developers to customize their access rules.

Using our expertise in search requirements across various customers, we built [Meilisearch Cloud](https://meilisearch.org.cn/cloud). It’s the quickest way to get started. In addition to freeing teams from managing infrastructure, it integrates analytics and monitoring, making it [the preferred production platform for companies like HitPay](https://meilisearch.org.cn/blog/hitpay-streamlines-search). Open-source Meilisearch can be self-hosted via Docker and a plethora of hosting platforms. 

This feature set positions Meilisearch well as a simple solution to get the best of modern search experience. This includes benefiting from an easy LLM integration to leverage AI-powered solutions.

## Future-proofing your search strategy

With native [hybrid and vector search](https://meilisearch.org.cn/docs/learn/ai_powered_search/getting_started_with_ai_search) integration, Meilisearch makes it simple to implement AI-powered search capabilities. But we’re not stopping there—our 2025 roadmap is packed with features enabling businesses to uncover more value from AI while sustaining performance and scalability.

### Improved conversions with personalization

[Our research shows that personalization significantly improves conversion rates](https://meilisearch.org.cn/blog/personalization-recommendations) across diverse business types. We aim to release a personalization framework enabling the collection of end-user context on each request. This comprehensive context management system allows developers to leverage user preferences, recent interactions, and behavioral patterns to further customize the relevancy of results on a per-user basis. 

### Retrieval augmented generation without overhead 

Although Meilisearch already enables [building retrieval-augmented generation (RAG)](https://meilisearch.org.cn/blog/how-to-build-rag), we want to integrate built-in RAG capabilities in the engine, seamlessly combining full-text search, vector search, and LLM interaction. Then, we want to tackle inherent problems in state-of-the-art RAG applications by tackling the orchestration of filtering, ranking, and results merging in RAG scenarios. Of course, we want this to come with strong defaults, making it easy for developers to get started.

### Advanced ranking integrations

Our goal is to provide a native way for users to leverage AI reranking models to improve the relevancy of results. This provides a more flexible approach to configuring result relevancy by allowing real-time results reranking based on user context and behavior. We also want to enable organizations to integrate with their own custom ranking models to allow for further dynamic adjustment of results based on hand-picked factors.

### Reliability as you scale

We also plan to improve Meilisearch’s infrastructure to handle additional use cases better. In particular, we want to continue improving the distributed system capabilities for better scaling and high availability by building upon our sharding experiments with [federated remote search](https://meilisearch.org.cn/docs/learn/multi_search/implement_sharding). Another layer of performance is integrating more sophisticated caching mechanisms to improve out-of-the-box performance. Monitoring and observability are also continuously improved based on user feedback.

## Meilisearch AI, refreshingly simple search DX

The release of Meilisearch AI marks a new age for Meilisearch users, fully integrating with the modern landscape of AI-powered applications. But it’s also a continuous fulfillment of our core value—making good search UX easy to build. We believe focusing on developer experience enables engineers to achieve faster time-to-value and maintain focus on their core business.

Meilisearch ships frequently to keep packaging built-in features, enabling fast-shipping companies to do what they do best. For more information on the Meilisearch AI release, consult our [launch week page](https://meilisearch.org.cn/launch-week). 

Start with a [14-day free trial of Meilisearch Cloud](https://cloud.meilisearch.com/) or [request a demo](https://dub.sh/meili-sales) to discuss your needs with our search experts.

Building the future of search with Meilisearch AI

What are vector embeddings? A complete guide [2025]

How to choose the best model for semantic search

What is a vector database? What you need to know [2025]

What is latent semantic indexing (LSI) and how does it work?

You know what's amazing about search? It's at the heart of how people interact with your application. But let's be honest: building and managing search infrastructure can feel like you're spending more time wrestling with configuration than creating value for your users. That's why we're excited to share something that changes this completely: the Meilisearch MCP server.

## Why MCP? Building bridges between search and AI

Before we dive in, let's talk about what MCP (Model Context Protocol) is and why we're excited about it. [MCP](https://www.anthropic.com/news/model-context-protocol) is an emerging open protocol that standardizes how applications provide context to Large Language Models (LLMs). Think of it as a universal translator between your tools and AI models.

Even though MCP isn't yet fully standardized, we believe it represents the future of how developers will interact with their tools. Here's why:
- It creates a consistent way for AI models to interact with different tools and services
- It puts security and user control at the forefront
- It enables natural language interactions while maintaining precise control
- It's being built in the open, with input from the developer community

## From complex commands to natural conversations

Remember the last time you needed to update your search settings? It probably involved looking up API documentation, crafting the perfect JSON payload, and triple-checking your syntax. We've all been there. But what if you could just tell your search engine what you want, in plain English?

That's exactly what we've built. By implementing MCP, we've enabled natural conversations with Meilisearch through AI assistants like Claude. Here's what this means in practice using [our movies database](https://where2watch.meilisearch.com/) as an example:

### Before:
```bash
curl \
  -X POST 'http://localhost:7700/indexes/movies/settings' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer MASTER_KEY' \
  --data-binary '{
    "rankingRules": [
      "words",
      "typo",
      "proximity",
      "attribute",
      "sort",
      "exactness"
    ]
  }'
```

### After:
> "Update the ranking rules for the movies index to prioritize word matches and handle typos."

That's it. When paired with advanced LLMs like Claude Sonnet, the AI understands your intent and handles all the technical details behind the scenes. No more context-switching between documentation tabs.

## What can you do with it today?

Thanks to the capabilities of modern LLMs, our MCP integration already enables natural language control over your Meilisearch instance. Here's what you can do right now:

1. **Index management made simple**
```"Create a new index for our product catalog and optimize it for e-commerce search"```
The AI will guide you through setting up your index with appropriate configurations for e-commerce.

2. **Search optimization**
```"Our users are having trouble finding products when they make typos. How can we fix this?"```
Get instant guidance on configuring typo tolerance and seeing real-time results.

3. **Performance insights**
```"How is our search performance looking? Any areas we should optimize?"```
Understand your search analytics and get actionable recommendations.

## How does it work?

Our current MCP implementation exposes Meilisearch's capabilities through a set of carefully designed tools that AI assistants can use to manage your search infrastructure. When paired with advanced LLMs like [Claude Sonnet](https://www.anthropic.com/news/claude-3-5-sonnet), these tools enable natural conversations about complex search operations.

The best part? It works seamlessly with Claude Desktop and other MCP-compatible clients, giving you:
- Natural language control over your search infrastructure
- Instant access to best practices and optimization tips
- Real-time insights and analytics

## Getting started in 2 minutes

1. Install [Claude Desktop](https://claude.ai/download) (it's free!)
2. Add our MCP server to your configuration:

```json
{
  "mcpServers": {
    "meilisearch": {
      "command": "uvx",
      "args": ["-n", "meilisearch-mcp"]
    }
  }
}
```
3. Start managing your search like you're chatting with a colleague!

## Building the future of search

This is just the beginning of our mission to make search infrastructure more accessible. Here's what we're working on next:

### Smart prompts
We're developing pre-built templates for common scenarios like:
- Optimizing index settings for specific use cases (e-commerce, documentation, etc.)
- Debugging search behavior and performance issues
- Understanding and improving search analytics
- Implementing industry-specific best practices

### Enhanced capabilities
- More sophisticated analytics and monitoring tools
- Expanded AI assistant integrations
- Advanced collaborative features for teams
- Deeper integration with Meilisearch Cloud

## Open source and open for contributions

True to our values, the Meilisearch MCP server is completely open source. We believe in building in public and learning from our community. Check out our implementation, contribute, or adapt it for your needs:

[GitHub repository](https://github.com/meilisearch/meilisearch-mcp)

## Join us in simplifying search

- ⭐ Star [our repository](https://github.com/meilisearch/meilisearch)
- 💬 Join our [Discord community](https://discord.meilisearch.com/) & share your use cases (or do it on social media and tag us)
- 🛠️ Contribute to development on [GitHub](https://github.com/meilisearch/meilisearch-mcp)

We're excited to see how you'll use this to build better search experiences. Try it out and let us know what you think!

Meilisearch 最新消息和公司动态

🚀 我们如何在 Meilisearch 中让 AI 发挥作用

为什么你不应该将向量数据库用于 RAG

AI 驱动的搜索：你需要了解什么 [2025]

2025 年十佳 AI 企业搜索工具和平台

用 Meilisearch AI 构建未来的搜索

什么是向量嵌入？完整指南 [2025]

如何为语义搜索选择最佳模型

什么是向量数据库？你需要了解什么 [2025]

什么是潜在语义索引 (LSI) 及其工作原理？

我们如何让 Meilisearch 与 AI 对话：介绍我们的 MCP 服务器