MCP server from Zilliz for connecting Claude to Milvus vector database collections, text search, vector search, hybrid search, inserts, deletes, indexes, collection loading, database switching, and collection metadata.
Milvus MCP can read collection metadata, query collections, and run text, vector, text-similarity, multi-vector, and hybrid searches., Write-capable tools can create collections, insert data, upsert data, delete entities, create indexes, bulk insert records, load collections, release collections, and switch databases., The README notes that the environment file has higher priority than command-line arguments, so stale or unexpected environment settings can silently change the target Milvus instance., SSE and Streamable HTTP modes can expose database operations over HTTP and should be network-restricted., Remote Milvus or Zilliz Cloud credentials should be scoped to the collections and databases Claude is allowed to access.
Privacy notes
Milvus collections can contain embeddings, sparse vectors, scalar fields, IDs, document chunks, metadata, image or multimodal references, query logs, and retrieval results that reveal sensitive project or user data., Milvus URI, tokens, database names, collection names, vector payloads, filter expressions, and retrieved records should stay out of prompts, issues, logs, screenshots, and committed files., Search results can include private source content that may be re-exposed in model transcripts or downstream tickets., HTTP transports, debug tools, query traces, failed-search artifacts, backups, and benchmark datasets need retention and access-control review.
Author
Zilliz
Submitted by
oktofeesh1
Claim status
unclaimed
Last verified
2026-06-06
Decision playbook
Review trust signals before you adopt
Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.
Compare context
Selected
0
Current score
63
Baseline
—
Delta
No baseline selected
No major trust-signal divergence detected in the current selection.
Source and provenance checks
Needs review
Confirm ownership and provenance before trusting install instructions.
Source link availableRequired
Open the canonical repository and verify ownership.
Done
Source provenance statusRequired
Marked as source-backed.
Done
Metadata reviewed
No reviewed flag detected in metadata.
Pending
Safety and privacy checks
Complete
Validate risk disclosures before installation or API wiring.
Safety notes presentRequired
Review the listed safety guidance before running commands.
Done
Privacy notes presentRequired
Review data handling notes before connecting accounts or secrets.
Done
Trust level risk gateRequired
Trust level does not block evaluation.
Done
Package and install checks
Needs review
Check package metadata and artifact integrity signals.
Install payload available
Install or copy payload is available for review.
Done
Package verification flag
No package verification flag provided.
Pending
Checksum metadata
No checksum provided for downloaded artifact.
Pending
Compare-driven decision checks
Needs review
Use compare context to validate trade-offs before adoption.
Compare tray has multiple entries
Add at least one more entry to compare trust differences.
5 safety and 4 privacy notes across 6 risk areas. Review closely: credentials & tokens, network access.
6 areas
SafetyExecution & processesMilvus MCP can read collection metadata, query collections, and run text, vector, text-similarity, multi-vector, and hybrid searches.
SafetyLocal filesThe README notes that the environment file has higher priority than command-line arguments, so stale or unexpected environment settings can silently change the target Milvus instance.
SafetyNetwork accessSSE and Streamable HTTP modes can expose database operations over HTTP and should be network-restricted.
SafetyCredentials & tokensRemote Milvus or Zilliz Cloud credentials should be scoped to the collections and databases Claude is allowed to access.
PrivacyData retentionMilvus collections can contain embeddings, sparse vectors, scalar fields, IDs, document chunks, metadata, image or multimodal references, query logs, and retrieval results that reveal sensitive project or user data.
PrivacyCredentials & tokensMilvus URI, tokens, database names, collection names, vector payloads, filter expressions, and retrieved records should stay out of prompts, issues, logs, screenshots, and committed files.
PrivacyExecution & processesSearch results can include private source content that may be re-exposed in model transcripts or downstream tickets.
PrivacyNetwork accessHTTP transports, debug tools, query traces, failed-search artifacts, backups, and benchmark datasets need retention and access-control review.
Safety notes
Milvus MCP can read collection metadata, query collections, and run text, vector, text-similarity, multi-vector, and hybrid searches.
The README notes that the environment file has higher priority than command-line arguments, so stale or unexpected environment settings can silently change the target Milvus instance.
SSE and Streamable HTTP modes can expose database operations over HTTP and should be network-restricted.
Remote Milvus or Zilliz Cloud credentials should be scoped to the collections and databases Claude is allowed to access.
Privacy notes
Milvus collections can contain embeddings, sparse vectors, scalar fields, IDs, document chunks, metadata, image or multimodal references, query logs, and retrieval results that reveal sensitive project or user data.
Milvus URI, tokens, database names, collection names, vector payloads, filter expressions, and retrieved records should stay out of prompts, issues, logs, screenshots, and committed files.
Search results can include private source content that may be re-exposed in model transcripts or downstream tickets.
HTTP transports, debug tools, query traces, failed-search artifacts, backups, and benchmark datasets need retention and access-control review.
Prerequisites
Python 3.10 or newer.
uv installed for the README's recommended run path.
Running local or remote Milvus instance.
Milvus URI, token, and database name selected for the target environment.
Review of whether stdio, SSE, or Streamable HTTP mode is appropriate for the MCP client.
Human approval policy for collection creation, inserts, upserts, deletes, index changes, database switching, and collection loading or release operations.
Milvus MCP Server is a Zilliz-maintained Model Context Protocol server for
Milvus vector database workflows. It gives Claude and other MCP clients access
to Milvus collections, text search, vector search, hybrid search, query filters,
collection metadata, index operations, inserts, deletes, database switching, and
collection load or release operations.
The README documents stdio, SSE, and Streamable HTTP modes. For local Claude or
Cursor use, stdio is the safest starting point because the server process stays
attached to the MCP client instead of exposing HTTP endpoints.
These sources were reviewed on 2026-06-06. The public Milvus website and
docs blocked command-line URL checks, and PyPI metadata for the package name
points to a different homepage, so this entry intentionally uses only reachable
GitHub source evidence from zilliztech/mcp-server-milvus.
Features
Zilliz-maintained MCP server for Milvus.
Stdio, SSE, and Streamable HTTP modes.
Collection listing and collection information tools.
Text search, vector search, text-similarity search, multi-vector search, and hybrid search.
Filtered query operations.
Collection creation with schema and vector configuration.
Insert, upsert, bulk insert, and delete-entity operations.
Index creation and index information tools.
Collection stats, loading progress, load, and release tools.
Database listing and database switching.
Environment-file and command-line configuration for Milvus URI, token, and database.
Installation
Clone the repository and run the server with uv as documented in the README:
git clone https://github.com/zilliztech/mcp-server-milvus.git
cd mcp-server-milvus
uv run src/mcp_server_milvus/server.py --milvus-uri <milvus-uri>
For an MCP client configuration that launches from a checked-out copy:
Review the target URI, token, database, and environment file before starting the
server. The README says the environment file takes priority over command-line
arguments.
Use Cases
Ask Claude to list Milvus collections before selecting a retrieval source.
Inspect collection metadata and stats while debugging ingestion.
Run vector, text, or hybrid searches with explicit limits and output fields.
Query collections with filter expressions.
Create an experimental collection for a local evaluation.
Insert or upsert curated records after reviewing the target collection and schema.
Delete entities with a reviewed filter expression.
Load or release collections while troubleshooting serving state.
Safety and Privacy
Milvus MCP is a database control surface. It can retrieve sensitive indexed
content and can also mutate collections, indexes, entities, and database
selection. Require human approval for writes, deletes, index creation, database
switching, load, and release operations.
Treat vectors and metadata as sensitive even when source text is not returned.
Embeddings, IDs, filters, collection names, and retrieval results can reveal
private context. Restrict SSE and Streamable HTTP modes to trusted networks and
avoid leaking Milvus tokens or query results into prompts, logs, or committed
configuration.
Duplicate Check
Existing content includes a general Milvus tools entry in content/tools, but
no Milvus MCP Server entry was found in content/mcp. This submission is scoped
to zilliztech/mcp-server-milvus, the MCP server, and does not change the
existing Milvus database listing.
Show that Milvus MCP Server is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.
[](https://heyclau.de/entry/mcp/milvus-mcp-server)
How it compares
Milvus MCP Server side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
1 trust signal differ across this comparison (Submitter).
MCP server from Zilliz for connecting Claude to Milvus vector database collections, text search, vector search, hybrid search, inserts, deletes, indexes, collection loading, database switching, and collection metadata.
Official Pinecone Developer MCP server that connects Claude and other MCP clients to Pinecone projects and documentation for index management, record upserts, semantic search, cascading multi-index search, reranking, and documentation lookup over integrated-inference indexes.
Connect Claude to a Weaviate vector database — run hybrid search, inspect collection config, list tenants, and upsert objects — using Weaviate's built-in Model Context Protocol server.
Official Chroma MCP server for connecting Claude to Chroma collections, documents, semantic search, full-text search, metadata filtering, persistent storage, self-hosted Chroma, and Chroma Cloud.
✓Milvus MCP can read collection metadata, query collections, and run text, vector, text-similarity, multi-vector, and hybrid searches.
Write-capable tools can create collections, insert data, upsert data, delete entities, create indexes, bulk insert records, load collections, release collections, and switch databases.
The README notes that the environment file has higher priority than command-line arguments, so stale or unexpected environment settings can silently change the target Milvus instance.
SSE and Streamable HTTP modes can expose database operations over HTTP and should be network-restricted.
Remote Milvus or Zilliz Cloud credentials should be scoped to the collections and databases Claude is allowed to access.
✓The server can create indexes and upsert records, so an agent with a write-capable API key can change live Pinecone project state.
Run the npm package `@pinecone-database/mcp` through `npx`, which downloads and executes the published package on each launch; pin to a trusted version if reproducibility matters.
Scope the Pinecone API key to the intended project and use read-only or least-privilege keys when index creation and writes are not needed.
Require human review before `create-index-for-model` and `upsert-records` runs that mutate production indexes.
✓The MCP server runs inside your Weaviate instance and respects its existing RBAC; scope the API key to least privilege.
The object-upsert tool writes data — restrict write access to the collections Claude should modify.
✓Chroma MCP can create, modify, and delete collections.
Document tools can add, update, query, retrieve, and delete documents, metadata, custom IDs, and embeddings.
Persistent, HTTP, and cloud modes can mutate durable retrieval stores rather than temporary test collections.
External embedding functions can send document text or image-derived content to third-party embedding providers.
Chroma Cloud and self-hosted HTTP modes require careful handling of tenants, databases, API keys, custom auth credentials, SSL settings, and network exposure.
Retrieved context can influence Claude output even when stale, irrelevant, over-broad, or not authorized for the task.
Privacy notes
✓Milvus collections can contain embeddings, sparse vectors, scalar fields, IDs, document chunks, metadata, image or multimodal references, query logs, and retrieval results that reveal sensitive project or user data.
Milvus URI, tokens, database names, collection names, vector payloads, filter expressions, and retrieved records should stay out of prompts, issues, logs, screenshots, and committed files.
Search results can include private source content that may be re-exposed in model transcripts or downstream tickets.
HTTP transports, debug tools, query traces, failed-search artifacts, backups, and benchmark datasets need retention and access-control review.
✓The `PINECONE_API_KEY` is read from the MCP client environment and grants access to the associated Pinecone project; keep it out of prompts, notes, and committed files.
Index names, configurations, namespaces, statistics, record contents, and search queries can be exposed to the MCP client and model provider.
Records and search text may contain embedded documents, customer data, or proprietary content, so review what is sent into indexes and returned by searches.
Documentation search and tool calls reach Pinecone endpoints such as api.pinecone.io, so network access and request metadata leave the local machine.
✓Query text, retrieved objects, and collection metadata enter the MCP client context and the model's prompt.
The Weaviate endpoint URL and API key are secrets — keep them in the client config or environment, not in shared repositories.
✓Collections can store source documents, chunks, embeddings, metadata, IDs, filters, and query results that reveal private project, customer, or research data.
Query text, retrieved documents, metadata filters, and embedding inputs may expose sensitive information to Chroma Cloud, self-hosted operators, embedding providers, logs, or downstream model providers.
CHROMA_API_KEY, custom auth credentials, embedding provider API keys, tenant IDs, database names, hostnames, and dotenv files should stay out of prompts, issues, logs, screenshots, and committed files.
Persistent data directories and exported collections need the same access control, backup, encryption, and retention review as the source documents they index.
Prerequisites
Python 3.10 or newer.
uv installed for the README's recommended run path.
Running local or remote Milvus instance.
Milvus URI, token, and database name selected for the target environment.
Node.js 18 or newer with `npx` available.
Pinecone account and API key generated from the Pinecone console at app.pinecone.io.
MCP client that supports stdio server configuration, such as Claude Desktop or Cursor.
Awareness that index management and record tools require integrated-inference indexes.
A Weaviate instance on v1.37.1 or later (self-hosted or Weaviate Cloud).
The MCP server enabled on that instance via MCP_SERVER_ENABLED=true.
A Weaviate API key with the RBAC permissions for the collections Claude should reach.
An MCP client such as Claude Code or Claude Desktop.
Python 3.10 or newer available through uv for local stdio usage.
[object Object]
Storage, backup, and retention plan for persistent collections before enabling write tools.
Chroma Cloud tenant, database, and API key or self-hosted Chroma host credentials if using remote modes.
Install
git clone https://github.com/zilliztech/mcp-server-milvus.git && cd mcp-server-milvus && uv run src/mcp_server_milvus/server.py --milvus-uri <milvus-uri>
npx -y @pinecone-database/mcp
claude mcp add --transport http weaviate https://<your-weaviate-host>/v1/mcp