Run RedisVL MCP

This guide shows how to run the RedisVL MCP server against an existing Redis index, configure its behavior, and use the MCP tools it exposes.

For the higher-level design, see RedisVL MCP.

Before You Start

RedisVL MCP assumes all of the following are already true:

  • you have Python 3.10 or newer
  • you have Redis with Search capabilities available
  • the Redis index already exists
  • you know which text field and vector field the server should use
  • you have installed the vectorizer provider dependencies your config needs

Install the MCP extra:

pip install redisvl[mcp]

If your vectorizer needs a provider extra, install that too:

pip install redisvl[mcp,openai]

Start the Server

Run the server over stdio (default):

uvx --from redisvl[mcp] rvl mcp --config /path/to/mcp.yaml

Run it over Streamable HTTP for remote MCP clients:

uvx --from redisvl[mcp] rvl mcp --config /path/to/mcp.yaml --transport streamable-http --host 0.0.0.0 --port 8000

Run it over SSE:

uvx --from redisvl[mcp] rvl mcp --config /path/to/mcp.yaml --transport sse --host 0.0.0.0 --port 9000

WARNING

Streamable HTTP and SSE endpoints are unauthenticated by default. Only bind to public interfaces (--host 0.0.0.0) on trusted networks or behind an authenticating reverse proxy. When not using --read-only, the upsert-records tool is also exposed to any client that can reach the server.

Run it in read-only mode to expose search without upsert:

uvx --from redisvl[mcp] rvl mcp --config /path/to/mcp.yaml --read-only

CLI Flags

Flag Default Purpose
--config — Path to the MCP YAML config (required)
--transport stdio Transport protocol: stdio, sse, or streamable-http
--host 127.0.0.1 Bind address (only used with sse and streamable-http)
--port 8000 Bind port (only used with sse and streamable-http)
--read-only off Disable the upsert-records tool

Environment Variables

You can also control boot settings through environment variables:

Variable Purpose
REDISVL_MCP_CONFIG Path to the MCP YAML config
REDISVL_MCP_READ_ONLY Disable upsert-records when set to true
REDISVL_MCP_TOOL_SEARCH_DESCRIPTION Override the search tool description
REDISVL_MCP_TOOL_UPSERT_DESCRIPTION Override the upsert tool description

Connect a Remote MCP Client

When using Streamable HTTP or SSE transport, point your MCP client at the server URL:

  • Streamable HTTP: http://<host>:<port>/mcp
  • SSE: http://<host>:<port>/sse

Note: <host> here is the bind address the server was started with. The default 127.0.0.1 only accepts connections from the same machine. To allow connections from other machines, start the server with --host 0.0.0.0 and use the machine’s actual IP or hostname in the client URL.

For example, to configure a remote MCP client to connect to a Streamable HTTP server running on 192.168.1.10:8000:

{
  "mcpServers": {
    "redisvl": {
      "url": "http://192.168.1.10:8000/mcp",
      "transport": "streamable-http"
    }
  }
}

Example Config

This example binds one logical MCP server to one existing Redis index called knowledge.

The config uses ${REDIS_URL} and ${OPENAI_API_KEY} as environment-variable placeholders. These values are resolved when the server starts. You can also use ${VAR:-default} to provide a fallback value.

server:
  redis_url: ${REDIS_URL}

indexes:
  knowledge:
    redis_name: knowledge

    vectorizer:
      class: OpenAITextVectorizer
      model: text-embedding-3-small
      api_config:
        api_key: ${OPENAI_API_KEY}

    schema_overrides:
      fields:
        - name: embedding
          type: vector
          attrs:
            dims: 1536
            datatype: float32

    search:
      type: hybrid
      params:
        text_scorer: BM25STD
        stopwords: english
        vector_search_method: KNN
        combination_method: LINEAR
        linear_text_weight: 0.3

    runtime:
      text_field_name: content
      vector_field_name: embedding
      default_embed_text_field: content
      default_limit: 10
      max_limit: 25
      max_result_window: 1000
      max_upsert_records: 64
      skip_embedding_if_present: true
      startup_timeout_seconds: 30
      request_timeout_seconds: 60
      max_concurrency: 16

What This Config Means

  • redis_name must point to an index that already exists in Redis
  • search.type fixes retrieval behavior for every MCP caller
  • runtime.text_field_name tells full-text and hybrid search which field to search
  • runtime.vector_field_name tells the server which vector field to use
  • runtime.default_embed_text_field tells upsert which text field to embed when a record needs embedding
  • runtime.max_result_window caps deep paging by limiting the maximum offset + limit
  • schema_overrides is only for patching incomplete field attrs discovered from Redis

Tool Contracts

RedisVL MCP exposes a small, implementation-owned contract.

search-records

Arguments:

  • query
  • limit
  • offset
  • filter
  • return_fields

Example request payload:

{
  "query": "incident response runbook",
  "limit": 2,
  "offset": 0,
  "filter": {
    "and": [
      { "field": "category", "op": "eq", "value": "operations" },
      { "field": "rating", "op": "gte", "value": 4 }
    ]
  },
  "return_fields": ["title", "content", "category", "rating"]
}

Example response payload:

{
  "search_type": "hybrid",
  "offset": 0,
  "limit": 2,
  "results": [
    {
      "id": "knowledge:runbook:eu-failover",
      "score": 0.82,
      "score_type": "hybrid_score",
      "record": {
        "title": "EU failover runbook",
        "content": "Restore traffic after a regional failover.",
        "category": "operations",
        "rating": 5
      }
    }
  ]
}

Notes:

  • search_type is response metadata, not a request argument
  • when return_fields is omitted, RedisVL MCP returns all non-vector fields
  • returning the configured vector field is rejected
  • filter accepts either a raw string or a JSON DSL object
  • offset + limit must stay within runtime.max_result_window
  • startup rejects schemas that use MCP-reserved score metadata field names: id, __key, key, score, vector_distance, __score, text_score, vector_similarity, hybrid_score

upsert-records

Arguments:

  • records
  • id_field
  • skip_embedding_if_present

Example request payload:

{
  "records": [
    {
      "doc_id": "doc-42",
      "content": "Updated operational guidance for failover handling.",
      "category": "operations",
      "rating": 5
    }
  ],
  "id_field": "doc_id"
}

Example response payload:

{
  "status": "success",
  "keys_upserted": 1,
  "keys": ["knowledge:doc-42"]
}

Notes:

  • this tool is not registered in read-only mode
  • records that need embedding must contain runtime.default_embed_text_field
  • when skip_embedding_if_present is true, records that already contain the vector field can skip re-embedding

Search Examples

Use read-only mode when assistants should only retrieve data:

uvx --from redisvl[mcp] rvl mcp --config /path/to/mcp.yaml --read-only

With a search.type of vector, callers send only the query, filters, pagination, and field projection:

{
  "query": "cache invalidation incident",
  "limit": 3,
  "return_fields": ["title", "content", "category"]
}

Raw String Filter

Pass a raw Redis filter string through unchanged:

{
  "query": "science",
  "filter": "@category:{science}",
  "return_fields": ["content", "category"]
}

JSON DSL Filter

The DSL supports logical operators and type-checked field operators:

{
  "query": "science",
  "filter": {
    "and": [
      { "field": "category", "op": "eq", "value": "science" },
      { "field": "rating", "op": "gte", "value": 4 }
    ]
  },
  "return_fields": ["content", "category", "rating"]
}

Pagination and Field Projection 

{
  "query": "science",
  "limit": 1,
  "offset": 1,
  "return_fields": ["content", "category"]
}

Hybrid Search With schema_overrides

Use schema_overrides when Redis inspection cannot recover complete vector attrs, then keep hybrid behavior in config:

schema_overrides:
  fields:
    - name: embedding
      type: vector
      attrs:
        algorithm: flat
        dims: 1536
        datatype: float32
        distance_metric: cosine

search:
  type: hybrid
  params:
    text_scorer: BM25STD
    stopwords: english
    vector_search_method: KNN
    combination_method: LINEAR
    linear_text_weight: 0.3

The MCP caller still sends the same request shape:

{
  "query": "legacy cache invalidation flow",
  "filter": { "field": "category", "op": "eq", "value": "release-notes" },
  "return_fields": ["title", "content", "release_version"]
}

Upsert Examples

Auto-Embed New Records

If a record does not include the configured vector field, RedisVL MCP embeds runtime.default_embed_text_field and writes the result:

{
  "records": [
    {
      "content": "First upserted document",
      "category": "science",
      "rating": 5
    },
    {
      "content": "Second upserted document",
      "category": "health",
      "rating": 4
    }
  ]
}

Update Existing Records With id_field 

{
  "records": [
    {
      "doc_id": "doc-1",
      "content": "Updated content",
      "category": "engineering",
      "rating": 5
    }
  ],
  "id_field": "doc_id"
}

Control Re-Embedding With skip_embedding_if_present 

{
  "records": [
    {
      "doc_id": "doc-2",
      "content": "Existing content",
      "category": "science",
      "rating": 4
    }
  ],
  "id_field": "doc_id",
  "skip_embedding_if_present": false
}

Set skip_embedding_if_present to false when you want the server to regenerate embeddings during upsert. In most cases, the caller should omit the vector field and let the server manage embeddings from runtime.default_embed_text_field.

Troubleshooting

Missing MCP Dependencies

If rvl mcp reports missing optional dependencies, install the MCP extra:

pip install redisvl[mcp]

If the configured vectorizer needs a provider SDK, install that provider extra too.

Configured Redis Index Does Not Exist

The server only binds to an existing index. Create the index first, then point indexes.<id>.redis_name at that index name.

Missing Required Environment Variables

YAML values support ${VAR} and ${VAR:-default} substitution. Missing required variables fail startup before the server registers tools.

Vectorizer Dimension Mismatch

If the vectorizer dims do not match the configured vector field dims, startup fails. Make sure the embedding model and the effective vector field dimensions are aligned.

Hybrid Config Requires Native Runtime Support

Some hybrid params depend on native hybrid support in Redis and redis-py. If your environment does not support that path, remove native-only params such as knn_ef_runtime or upgrade Redis and redis-py.

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