Qdrant MCP Server vs Sentry MCP

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Qdrant MCP Server MCP Servers	Sentry MCP MCP Servers
Tagline	Official Qdrant MCP server that turns a vector database into a semantic memory layer for Claude, Cursor, Windsurf, and any MCP client.	Official Sentry MCP server: give Claude Code, Cursor, and other AI agents real access to your errors, traces, and triage.
Category	MCP Servers	MCP Servers
Pricing	Free· Free and open source (Apache-2.0). Qdrant itself can be self-hosted for free or used via Qdrant Cloud (free tier available, paid plans from ~$25/mo for managed clusters).	Freemium· Free: $0 USD · Team: $4 USD per user/month · Enterprise: $21 USD per user/month
Model	FastEmbed (default: sentence-transformers/all-MiniLM-L6-v2); pairs with any MCP-capable LLM such as Claude 3.5/4, GPT-4o, or local models	Bring-your-own LLM (OpenAI, Anthropic, Azure OpenAI, or OpenRouter) for natural-language search skills; agent-side model is whatever your MCP client runs.
Editorial score	—	—
Use cases	Persistent memory for Claude Desktop agentsSemantic code snippet search in Cursor and WindsurfPrivate documentation retrieval for internal LLM copilotsTeam knowledge base backed by Qdrant CloudLocal offline vector memory via QDRANT_LOCAL_PATHRead-only knowledge lookup skill for customer-support agentsCross-session context store for autonomous coding agents	Debug production error from IDE agentRoot-cause a latency regression via trace lookupTriage new issues (assign, resolve, comment) from chatCorrelate a failing test with recent Sentry eventsNatural-language search across eventsPost-deploy error-rate investigationPull stack trace and open the offending file for a fixSummarize top issues for a standupQuery self-hosted Sentry from an internal coding agent
Pros	Official, actively maintained by the Qdrant team with 1.4k+ stars and frequent releases Two-tool surface (store/find) is small enough that models actually use it correctly Bundled FastEmbed means no separate OpenAI/Cohere embedding key is required to get started Configurable tool descriptions let you rebrand the same server as memory, code search, or docs lookup Works across the major MCP clients: Claude Desktop, Cursor, Windsurf, VS Code, and custom agents Apache-2.0 with local, Docker, and uvx install paths including a fully offline QDRANT_LOCAL_PATH mode Read-only mode makes it safe to expose a curated knowledge base without letting the model write to it	Official first-party server from Sentry — kept in step with API changes, not a community wrapper Both a hosted remote endpoint (mcp.sentry.dev, OAuth or Bearer) and a local stdio binary via npx Works with self-hosted Sentry, not only sentry.io One-line install for Claude Code through the Sentry plugin marketplace; also documented for Cursor and MCP Inspector Fine-grained skill toggles let you scope which tools the agent can call Covers the full debugging loop: issues, events, traces, search, and triage actions like assign/resolve/comment Open source, so the tool surface and prompts can be audited or extended
Cons	Only FastEmbed is supported today, so you cannot plug in OpenAI, Cohere, or Voyage embeddings without forking Just two tools: no filtering, hybrid search, payload updates, or collection management surfaced to the model Single active collection per server process; multi-collection agents need multiple server instances or wrapping Assumes you already run and secure a Qdrant instance (self-hosted or Cloud) — not a turnkey managed product Chunking, ingestion pipelines, and re-ranking are entirely your problem; this is a thin bridge, not a RAG framework	You still need a paid Sentry plan to get useful volumes of events, retention, and org seats Natural-language search tools require you to bring your own OpenAI/Anthropic/Azure/OpenRouter key — extra cost and setup Value collapses if your team is not already invested in Sentry as its error/APM backend Remote server is single-tenant per token — sharing across a team means each engineer wires their own auth MCP itself is still young; some clients handle remote servers with headers imperfectly and stdio is often the fallback Read/write tools mean a misbehaving agent can resolve or reassign real issues — human-in-the-loop is not optional
Website	github.com	github.com

Pick Qdrant MCP Server if

✅ Official, actively maintained by the Qdrant team with 1.4k+ stars and frequent releases
✅ Two-tool surface (store/find) is small enough that models actually use it correctly
✅ Bundled FastEmbed means no separate OpenAI/Cohere embedding key is required to get started
✅ Configurable tool descriptions let you rebrand the same server as memory, code search, or docs lookup

Pick Sentry MCP if

✅ Official first-party server from Sentry — kept in step with API changes, not a community wrapper
✅ Both a hosted remote endpoint (mcp.sentry.dev, OAuth or Bearer) and a local stdio binary via npx
✅ Works with self-hosted Sentry, not only sentry.io
✅ One-line install for Claude Code through the Sentry plugin marketplace; also documented for Cursor and MCP Inspector

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