Compatible with every major AI agent and IDE
What is the Meilisearch MCP Server?
Connect your Meilisearch instance to any AI agent to automate your search engine management and data indexing workflows.
What you can do
- Index Management — Create, list, update, and delete indexes. Perform atomic swaps between indexes for zero-downtime deployments.
- Document Operations — Add, update, or replace documents in bulk. Retrieve specific documents by ID or list them with advanced filtering and sorting.
- Granular Deletion — Remove documents individually, in batches, or by applying complex filter expressions to clean up your data.
- Metadata Inspection — Fetch detailed metadata for your indexes and documents to monitor your search engine's state.
How it works
- Subscribe to this server
- Enter your Meilisearch Instance URL and API Key
- Start managing your search data from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Developers — Index new data or debug search results directly from the code editor.
- Data Engineers — Automate index maintenance and document synchronization tasks.
- Content Managers — Quickly verify if specific content is correctly indexed without using a dedicated dashboard.
Built-in capabilities (44)
Add or replace documents in an index
Cancel pending or processing tasks
Request a chat completion from a workspace
Enable or disable experimental features
Trigger the creation of a Meilisearch dump
Create a new index
Create a new API key
Trigger the creation of a Meilisearch snapshot
Delete all documents in an index
Delete a single document
Delete multiple documents by ID
Delete documents matching a filter
Delete a dynamic search rule
Delete an index
Delete an API key
Delete finished tasks
Get details of a specific batch
Get a specific document by ID
Check the health of the Meilisearch instance
Get metadata for a specific index
Get stats of a specific index
Get details of a specific API key
Get all settings of an index
Get stats of all indexes and database size
Get details of a specific task
Get the version of the Meilisearch instance
List task batches
List chat workspaces
List documents in an index
List dynamic search rules for an index
List the status of experimental features
List all Meilisearch indexes
List API keys
List asynchronous tasks
Perform multiple search queries in a single call
Reset all settings of an index to defaults
Search for documents in an index
Create or update a dynamic search rule
Find documents similar to a given document ID
Swap multiple indexes atomically
Add or update documents (partial update)
Update an index primary key
Update an API key name or description
Update settings of an index
Why LlamaIndex?
LlamaIndex agents combine Meilisearch tool responses with indexed documents for comprehensive, grounded answers. Connect 44 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
- —
Data-first architecture: LlamaIndex agents combine Meilisearch tool responses with indexed documents for comprehensive, grounded answers
- —
Query pipeline framework lets you chain Meilisearch tool calls with transformations, filters, and re-rankers in a typed pipeline
- —
Multi-source reasoning: agents can query Meilisearch, a vector store, and a SQL database in a single turn and synthesize results
- —
Observability integrations show exactly what Meilisearch tools were called, what data was returned, and how it influenced the final answer
Meilisearch in LlamaIndex
Meilisearch and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Meilisearch to LlamaIndex through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Meilisearch in LlamaIndex
The Meilisearch MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 44 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in LlamaIndex only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
How Vinkius secures
Meilisearch for LlamaIndex
Every tool call from LlamaIndex to the Meilisearch MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I delete documents based on a specific condition or category?
Yes! You can use the delete_documents_by_filter tool. Just provide the index UID and a filter expression (e.g., 'category = electronics') to remove all matching documents at once.
How do I perform a zero-downtime index update?
You can use the swap_indexes tool. This allows you to swap the names of two or more indexes atomically, ensuring your search remains active while you switch to a newly populated index.
Is it possible to retrieve only specific fields from a document?
Absolutely. When using get_document or list_documents, you can provide a comma-separated list of fields to return, reducing the payload size and focusing on the data you need.
How does LlamaIndex connect to MCP servers?
Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.
Can I combine MCP tools with vector stores?
Yes. LlamaIndex agents can query Meilisearch tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.
Does LlamaIndex support async MCP calls?
Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.
BasicMCPClient not found
Install: pip install llama-index-tools-mcp
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