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URL: https://glama.ai/mcp/servers/integrations/upstash

⇱ Upstash | Glama

  • Why this server?

    Developed by Upstash as indicated by the package name @upstash/context7-mcp

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    A Model Context Protocol server that fetches up-to-date, version-specific documentation and code examples from libraries directly into LLM prompts, helping developers get accurate answers without outdated or hallucinated information.
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    MIT

  • Why this server?

    Provides Upstash-based backend for checkpointing and state persistence.

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    Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.
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    Apache 2.0

  • Why this server?

    Provides access to up-to-date, version-specific documentation and code examples for libraries and frameworks, with tools for resolving library names and fetching documentation by library ID or topic.

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    Provides up-to-date, version-specific documentation and code examples for libraries and frameworks directly into AI prompts, eliminating outdated code generation and hallucinated APIs.
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    1,495,956
    MIT

  • Why this server?

    Provides access to up-to-date, version-specific documentation and code examples for libraries and frameworks, helping developers generate accurate code without hallucinated or outdated APIs.

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    Provides LLMs with up-to-date, version-specific documentation and code examples directly from library sources, eliminating outdated training data and hallucinated APIs by fetching current documentation at prompt time.
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    1,495,956
    MIT

  • Why this server?

    Provides persistent context storage and semantic search capabilities using Upstash Vector DB, including support for metadata filtering and batch operations.

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    Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.
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    MIT

  • Why this server?

    Integrates with Upstash Developer API to manage Upstash resources through natural language commands, such as creating databases, listing resources, running commands, and viewing metrics.

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    Model Context Protocol (MCP) is a new, standardized protocol for managing context between large language models (LLMs) and external systems. In this repository, we provide an installer as well as an MCP Server for Upstash Developer API's.
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    MIT

  • Why this server?

    Integrates with the Upstash Context7 MCP server to provide autonomous retrieval and synthesis of external library documentation.

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    An autonomous MCP server providing project-centric context awareness through keyword analysis, relationship graphs, and structured metadata retrieval. It enables intelligent codebase understanding and task management workflows by continuously learning from development patterns and conversations.
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    MIT

  • Why this server?

    Offers serverless Redis for caching and state management, listed as a detailed resource in the compute domain.

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    An agentic system that turns free-tier cloud resources into working outcomes. It orchestrates six specialist agents to plan, acquire, and assemble free services into deployable applications.
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    MIT

  • Why this server?

    Allows storage and management of Upstash API keys for Redis and Kafka services, with secure injection into applications via environment variables or proxy calls.

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    Encrypted credential vault with a 21-tool MCP server. Store and manage LLM API keys, service API keys, and OAuth credentials — then let your AI agent list, reveal, rotate, rename, pause, and proxy calls through them.
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    MIT