What can @upstash/mcp-server do?

redis-backed message queue exposure via mcp protocol, upstash qstash job scheduling exposure via mcp tools, upstash vector database semantic search via mcp tools, upstash kv (redis) key-value operations via mcp tools, mcp server lifecycle and tool schema management, credential management and authentication token handling

@upstash/mcp-server

MCP ServerFree

MCP server for Upstash

Open Source

/ 100

6 capabilities

Capabilities6 decomposed

redis-backed message queue exposure via mcp protocol

Medium confidence

Exposes Upstash Redis message queue operations (publish, subscribe, list, delete) as MCP tools that Claude and other MCP clients can invoke. Implements the Model Context Protocol server specification to translate queue operations into standardized tool schemas with JSON-RPC 2.0 transport, enabling LLM agents to interact with Redis queues without direct SDK imports.

Solves for

I want Claude to be able to publish messages to a Redis queue as part of an agent workflowI need to let an LLM read pending messages from an Upstash queue and process themI want to build an agent that manages queue state (list, delete, acknowledge messages) through natural language

Best for

AI agents and LLM applications needing async task coordination

Teams building Claude-integrated workflows with Upstash infrastructure

Developers migrating from direct SDK calls to MCP-based tool abstractions

Requires

Node.js 18+ runtime

Upstash Redis instance with valid REST API credentials (UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN)

MCP client implementation (Claude Desktop, or custom MCP client library)

Limitations

Requires Upstash Redis instance — no support for self-hosted Redis or other queue backends

MCP protocol overhead adds latency compared to direct SDK calls (typically 50-150ms per operation)

No built-in message batching — each queue operation is a separate MCP tool invocation

What makes it unique

Purpose-built MCP server specifically for Upstash Redis REST API, implementing the full MCP tool protocol with schema validation and error handling tailored to queue operations, rather than a generic Redis MCP wrapper

vs alternatives

Tighter integration with Upstash's REST API and managed infrastructure compared to generic Redis MCP servers, with pre-built tool schemas optimized for common queue patterns

upstash qstash job scheduling exposure via mcp tools

Medium confidence

Exposes Upstash Qstash (serverless task scheduling) operations as MCP tools, allowing LLM agents to schedule, list, and manage delayed/recurring jobs through the MCP protocol. Translates Qstash API operations (schedule job, cancel job, get job status) into standardized MCP tool schemas with automatic request signing and authentication.

Solves for

I want Claude to schedule a background job to run at a specific time or intervalI need an agent to cancel or modify scheduled tasks based on runtime conditionsI want to query the status of scheduled jobs and have Claude react to their state

Best for

LLM agents coordinating time-sensitive workflows across services

Serverless applications needing agent-driven job scheduling without manual API calls

Teams using Upstash Qstash for task orchestration and wanting Claude integration

Requires

Node.js 18+ runtime

Upstash Qstash account with valid API credentials (QSTASH_TOKEN)

MCP client with tool-calling support

Limitations

Qstash-specific — no support for other job schedulers (Bull, Temporal, etc.)

Job scheduling latency depends on Upstash infrastructure; agent-perceived latency includes MCP round-trip

No built-in job result streaming — agents see only job metadata, not execution output

What makes it unique

Integrates Upstash Qstash's REST API with MCP tool protocol, handling authentication token management and request signing transparently, enabling agents to schedule jobs without managing credentials directly

vs alternatives

Simpler than building custom job scheduling logic in agent prompts; Qstash's serverless model eliminates infrastructure management compared to self-hosted schedulers like Bull or APScheduler

upstash vector database semantic search via mcp tools

Medium confidence

Exposes Upstash Vector (serverless vector database) operations as MCP tools, enabling LLM agents to perform semantic search, upsert embeddings, and manage vector indexes through the MCP protocol. Implements schema-based tool definitions for vector operations (query, upsert, delete, fetch) with automatic embedding generation or direct vector input support.

Solves for

I want Claude to search a vector database for semantically similar documents based on a queryI need an agent to store embeddings of new documents and make them searchableI want to build a RAG agent that retrieves context from a vector index via MCP

Best for

RAG (Retrieval-Augmented Generation) agents using Upstash Vector as the backend

Teams building semantic search features integrated with Claude workflows

LLM applications needing persistent vector storage without managing infrastructure

Requires

Node.js 18+ runtime

Upstash Vector index with valid REST API credentials (UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN)

MCP client with tool-calling support

Limitations

Upstash Vector-specific — no support for other vector databases (Pinecone, Weaviate, Milvus)

Embedding generation must be handled separately (agents cannot call embedding models directly through this tool)

Vector dimension and index configuration are static per Upstash index — no dynamic schema changes via MCP

What makes it unique

Bridges Upstash Vector's REST API with MCP tool protocol, providing agents with standardized vector operations (query, upsert, delete) without requiring direct SDK integration or embedding model access

vs alternatives

Serverless vector database eliminates infrastructure overhead compared to self-hosted Milvus or Weaviate; MCP abstraction provides cleaner agent integration than raw API calls

upstash kv (redis) key-value operations via mcp tools

Medium confidence

Exposes Upstash KV (serverless Redis) operations as MCP tools, allowing LLM agents to read, write, delete, and manage key-value data through the MCP protocol. Implements tool schemas for GET, SET, DEL, INCR, EXPIRE, and other Redis commands, with automatic serialization/deserialization and TTL management.

Solves for

I want Claude to store and retrieve agent state (conversation context, user preferences) in a persistent key-value storeI need an agent to increment counters or manage rate limits using Redis atomic operationsI want to cache expensive computations or API responses with automatic expiration

Best for

Stateful LLM agents needing persistent memory across sessions

Multi-turn conversation systems requiring context persistence

Agents implementing caching, rate limiting, or session management

Requires

Node.js 18+ runtime

Upstash KV instance with valid REST API credentials (UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN)

MCP client with tool-calling support

Limitations

Upstash KV-specific — no support for other key-value stores (DynamoDB, Memcached)

No transactions or multi-key atomic operations exposed via MCP tools

Data types limited to strings and basic serialized JSON — no native support for complex data structures (sets, sorted sets, hashes)

What makes it unique

Exposes Upstash KV operations as MCP tools with automatic value serialization and TTL handling, enabling agents to treat the key-value store as a native tool rather than managing REST API calls directly

vs alternatives

Serverless KV store eliminates infrastructure management compared to self-hosted Redis; MCP integration provides cleaner agent interface than raw HTTP requests

mcp server lifecycle and tool schema management

Medium confidence

Implements the Model Context Protocol server specification, handling MCP initialization, tool schema registration, and request/response routing. Manages the JSON-RPC 2.0 transport layer, tool discovery, and error handling for all Upstash operations exposed as MCP tools. Provides automatic schema validation and type coercion for tool inputs.

Solves for

I want to set up an MCP server that exposes Upstash operations to Claude Desktop or other MCP clientsI need proper error handling and validation when agents invoke Upstash tools through MCPI want to discover available tools and their schemas programmatically

Best for

Developers building MCP servers for Upstash integration

Teams deploying Claude Desktop with Upstash backend services

MCP client implementations needing standardized tool discovery

Requires

Node.js 18+ runtime

MCP client implementation (Claude Desktop, or custom MCP client library)

Valid Upstash API credentials for the services being exposed

Limitations

MCP protocol overhead adds latency (~50-150ms per tool invocation) compared to direct SDK calls

Tool schema validation is synchronous — no async schema generation or dynamic tool registration at runtime

Error messages are constrained by MCP protocol format; detailed debugging requires server-side logging

What makes it unique

Implements the full MCP server specification with automatic tool schema generation from Upstash SDK operations, handling protocol negotiation and transport management transparently

vs alternatives

Standardized MCP implementation ensures compatibility with any MCP client (Claude, custom agents) without custom integration code

credential management and authentication token handling

Medium confidence

Manages Upstash API credentials (REST URLs and tokens) for Redis, Qstash, and Vector services, with automatic token injection into requests and secure credential isolation. Supports environment variable configuration and validates credentials at server startup, preventing tool invocations with invalid or missing credentials.

Solves for

I want to securely configure Upstash credentials for the MCP server without hardcoding themI need the server to validate credentials at startup and fail fast if they're invalidI want to rotate credentials without restarting the server

Best for

Production deployments requiring secure credential management

Teams using environment-based configuration (Docker, Kubernetes, serverless functions)

Security-conscious organizations needing credential isolation

Requires

Environment variables: UPSTASH_REDIS_REST_URL, UPSTASH_REDIS_REST_TOKEN, QSTASH_TOKEN, UPSTASH_VECTOR_REST_URL, UPSTASH_VECTOR_REST_TOKEN (as applicable)

Node.js 18+ runtime

Limitations

Credentials must be provided via environment variables — no support for external secret managers (AWS Secrets Manager, HashiCorp Vault)

No runtime credential rotation — credentials are loaded at startup and cached for the server lifetime

Credentials are passed in memory; no encryption at rest within the Node.js process

What makes it unique

Centralizes credential management for multiple Upstash services (Redis, Qstash, Vector) with startup validation, preventing tool invocations with invalid credentials

vs alternatives

Environment-based configuration is simpler than custom credential providers; startup validation catches configuration errors early compared to lazy validation

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with @upstash/mcp-server, ranked by overlap. Discovered automatically through the match graph.

MCP Server26

@upstash/mcp-server

MCP server for Upstash

mcp server initialization and protocol complianceupstash redis command execution via mcp protocolerror handling and response normalizationupstash authentication and credential management

4 shared capabilities

MCP Server38

mcp-server-qdrant

An official Qdrant Model Context Protocol (MCP) server implementation

mcp-protocol-compliant-tool-exposuremulti-collection-management-with-tool-filtering

2 shared capabilities

Platform43

Upstash

Serverless data — Redis, Kafka, Vector DB, QStash with pay-per-request and edge support.

message queue and http webhook delivery with qstash

1 shared capability

MCP Server41

supergateway

Run MCP stdio servers over SSE, Streamable HTTP or visa versa

bidirectional message routing with protocol-agnostic buffering

1 shared capability

MCP Server23

VpunaAiSearch

** - Connect to [Vpuna AI Search Service](https://aisearch.vpuna.com), a developer first platform for semantic search, summarization, and contextual chat. Each project dynamically exposes its own Remote HTTP MCP server, enabling real-time context injection from structured and unstructured data.

semantic-search-with-dynamic-mcp-exposure

1 shared capability

MCP Server25

🧲 Magg 🧲

** - A meta-MCP server that acts as a universal hub, allowing LLMs to autonomously discover, install, and orchestrate multiple MCP servers - essentially giving AI assistants the power to extend their own capabilities on-demand.

message routing and protocol proxying with semantic preservation

1 shared capability

Best For

✓AI agents and LLM applications needing async task coordination
✓Teams building Claude-integrated workflows with Upstash infrastructure
✓Developers migrating from direct SDK calls to MCP-based tool abstractions
✓LLM agents coordinating time-sensitive workflows across services
✓Serverless applications needing agent-driven job scheduling without manual API calls
✓Teams using Upstash Qstash for task orchestration and wanting Claude integration
✓RAG (Retrieval-Augmented Generation) agents using Upstash Vector as the backend
✓Teams building semantic search features integrated with Claude workflows

Known Limitations

⚠Requires Upstash Redis instance — no support for self-hosted Redis or other queue backends
⚠MCP protocol overhead adds latency compared to direct SDK calls (typically 50-150ms per operation)
⚠No built-in message batching — each queue operation is a separate MCP tool invocation
⚠Limited to operations exposed by Upstash SDK; advanced Redis features (Lua scripting, streams) not directly accessible
⚠Qstash-specific — no support for other job schedulers (Bull, Temporal, etc.)
⚠Job scheduling latency depends on Upstash infrastructure; agent-perceived latency includes MCP round-trip

Requirements

Node.js 18+ runtimeUpstash Redis instance with valid REST API credentials (UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN)MCP client implementation (Claude Desktop, or custom MCP client library)@upstash/redis npm package as peer dependencyUpstash Qstash account with valid API credentials (QSTASH_TOKEN)MCP client with tool-calling support@upstash/qstash npm package as peer dependencyUpstash Vector index with valid REST API credentials (UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN)

Input / Output

Accepts: JSON-serializable message payloads, Queue operation parameters (topic/channel names, message IDs), MCP tool invocation schemas, Job payload (JSON-serializable data), Cron expressions or delay specifications, Endpoint URLs for job callbacks, Job IDs for status/cancellation queries, Query text (for semantic search), Vector embeddings (float arrays of fixed dimension), Document IDs and metadata, Namespace/index specifications, Key names (strings), Values (strings, JSON-serializable objects), TTL/expiration times (seconds), Numeric increments, MCP initialization requests, Tool invocation requests (JSON-RPC 2.0 format), Tool discovery queries, Environment variable strings

Produces: JSON-structured queue operation results, Message payloads (strings or JSON), Queue metadata (message count, timestamps), MCP tool result objects with success/error status, Job metadata (ID, status, scheduled time, retry count), Execution results or error messages, List of scheduled jobs with filtering, Confirmation of job cancellation or modification, Search results with similarity scores and metadata, Upsert confirmation with record count, Vector metadata and index statistics, Deletion confirmation, Retrieved values (strings or parsed JSON), Operation confirmations (success/failure), Numeric results (from INCR, DECR operations), Key existence checks (boolean), MCP server capabilities advertisement, Tool schema definitions (JSON Schema format), Tool execution results (JSON-RPC 2.0 responses), Error responses with diagnostic information, Validation success/failure status, Authenticated HTTP headers for Upstash API requests

UnfragileRank

Adoption33%(30% weight)

Quality14%(25% weight)

Ecosystem40%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

6 capabilities

Visit @upstash/mcp-server→

Repository Details

Package Details

npm

Registry

0.2.3

Version

2,211

Weekly Downloads

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MCP server for Upstash

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Capabilities6 decomposed

redis-backed message queue exposure via mcp protocol

Medium confidence

Solves for

Best for

AI agents and LLM applications needing async task coordination

Teams building Claude-integrated workflows with Upstash infrastructure

Developers migrating from direct SDK calls to MCP-based tool abstractions

Requires

Node.js 18+ runtime

Upstash Redis instance with valid REST API credentials (UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN)

MCP client implementation (Claude Desktop, or custom MCP client library)

Limitations

Requires Upstash Redis instance — no support for self-hosted Redis or other queue backends

MCP protocol overhead adds latency compared to direct SDK calls (typically 50-150ms per operation)

No built-in message batching — each queue operation is a separate MCP tool invocation

What makes it unique

vs alternatives

Tighter integration with Upstash's REST API and managed infrastructure compared to generic Redis MCP servers, with pre-built tool schemas optimized for common queue patterns

upstash qstash job scheduling exposure via mcp tools

Medium confidence

Solves for

Best for

LLM agents coordinating time-sensitive workflows across services

Serverless applications needing agent-driven job scheduling without manual API calls

Teams using Upstash Qstash for task orchestration and wanting Claude integration

Requires

Node.js 18+ runtime

Upstash Qstash account with valid API credentials (QSTASH_TOKEN)

MCP client with tool-calling support

Limitations

Qstash-specific — no support for other job schedulers (Bull, Temporal, etc.)

Job scheduling latency depends on Upstash infrastructure; agent-perceived latency includes MCP round-trip

No built-in job result streaming — agents see only job metadata, not execution output

What makes it unique

vs alternatives

Simpler than building custom job scheduling logic in agent prompts; Qstash's serverless model eliminates infrastructure management compared to self-hosted schedulers like Bull or APScheduler

upstash vector database semantic search via mcp tools

Medium confidence

Solves for

Best for

RAG (Retrieval-Augmented Generation) agents using Upstash Vector as the backend

Teams building semantic search features integrated with Claude workflows

LLM applications needing persistent vector storage without managing infrastructure

Requires

Node.js 18+ runtime

Upstash Vector index with valid REST API credentials (UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN)

MCP client with tool-calling support

Limitations

Upstash Vector-specific — no support for other vector databases (Pinecone, Weaviate, Milvus)

Embedding generation must be handled separately (agents cannot call embedding models directly through this tool)

Vector dimension and index configuration are static per Upstash index — no dynamic schema changes via MCP

What makes it unique

vs alternatives

Serverless vector database eliminates infrastructure overhead compared to self-hosted Milvus or Weaviate; MCP abstraction provides cleaner agent integration than raw API calls

upstash kv (redis) key-value operations via mcp tools

Medium confidence

Solves for

Best for

Stateful LLM agents needing persistent memory across sessions

Multi-turn conversation systems requiring context persistence

Agents implementing caching, rate limiting, or session management

Requires

Node.js 18+ runtime

Upstash KV instance with valid REST API credentials (UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN)

MCP client with tool-calling support

Limitations

Upstash KV-specific — no support for other key-value stores (DynamoDB, Memcached)

No transactions or multi-key atomic operations exposed via MCP tools

Data types limited to strings and basic serialized JSON — no native support for complex data structures (sets, sorted sets, hashes)

What makes it unique

vs alternatives

Serverless KV store eliminates infrastructure management compared to self-hosted Redis; MCP integration provides cleaner agent interface than raw HTTP requests

mcp server lifecycle and tool schema management

Medium confidence

Solves for

Best for

Developers building MCP servers for Upstash integration

Teams deploying Claude Desktop with Upstash backend services

MCP client implementations needing standardized tool discovery

Requires

Node.js 18+ runtime

MCP client implementation (Claude Desktop, or custom MCP client library)

Valid Upstash API credentials for the services being exposed

Limitations

MCP protocol overhead adds latency (~50-150ms per tool invocation) compared to direct SDK calls

Tool schema validation is synchronous — no async schema generation or dynamic tool registration at runtime

Error messages are constrained by MCP protocol format; detailed debugging requires server-side logging

What makes it unique

Implements the full MCP server specification with automatic tool schema generation from Upstash SDK operations, handling protocol negotiation and transport management transparently

vs alternatives

Standardized MCP implementation ensures compatibility with any MCP client (Claude, custom agents) without custom integration code

credential management and authentication token handling

Medium confidence

Solves for

Best for

Production deployments requiring secure credential management

Teams using environment-based configuration (Docker, Kubernetes, serverless functions)

Security-conscious organizations needing credential isolation

Requires

Environment variables: UPSTASH_REDIS_REST_URL, UPSTASH_REDIS_REST_TOKEN, QSTASH_TOKEN, UPSTASH_VECTOR_REST_URL, UPSTASH_VECTOR_REST_TOKEN (as applicable)

Node.js 18+ runtime

Limitations

Credentials must be provided via environment variables — no support for external secret managers (AWS Secrets Manager, HashiCorp Vault)

No runtime credential rotation — credentials are loaded at startup and cached for the server lifetime

Credentials are passed in memory; no encryption at rest within the Node.js process

What makes it unique

Centralizes credential management for multiple Upstash services (Redis, Qstash, Vector) with startup validation, preventing tool invocations with invalid credentials

vs alternatives

Environment-based configuration is simpler than custom credential providers; startup validation catches configuration errors early compared to lazy validation

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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@upstash/mcp-server

Capabilities6 decomposed

redis-backed message queue exposure via mcp protocol

upstash qstash job scheduling exposure via mcp tools

upstash vector database semantic search via mcp tools

upstash kv (redis) key-value operations via mcp tools

mcp server lifecycle and tool schema management

credential management and authentication token handling

Related Artifactssharing capabilities

@upstash/mcp-server

mcp-server-qdrant

Upstash

supergateway

VpunaAiSearch

🧲 Magg 🧲

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to @upstash/mcp-server

Are you the builder of @upstash/mcp-server?

Get the weekly brief

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@upstash/mcp-server

Capabilities6 decomposed

redis-backed message queue exposure via mcp protocol

upstash qstash job scheduling exposure via mcp tools

upstash vector database semantic search via mcp tools

upstash kv (redis) key-value operations via mcp tools

mcp server lifecycle and tool schema management

credential management and authentication token handling

Related Artifactssharing capabilities

@upstash/mcp-server

mcp-server-qdrant

Upstash

supergateway

VpunaAiSearch

🧲 Magg 🧲

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to @upstash/mcp-server

Are you the builder of @upstash/mcp-server?

Get the weekly brief

Data Sources