qdrant-mcp-pi5

Provides a Model Context Protocol (MCP) server integration for persistent semantic memory on Raspberry Pi 5 using local Qdrant storage. Agents can store and retrieve facts via semantic similarity (embeddings) through mcporter (MCP client/bridge), optionally with an OpenClaw plugin that hard-enforces memory recall before responses.

Evaluated Mar 30, 2026 (21d ago)
Repo ↗ Ai Ml ai-ml vector-database mcp semantic-search raspberry-pi local-data memory embeddings openclaw
⚙ Agent Friendliness
58
/ 100
Can an agent use this?
🔒 Security
29
/ 100
Is it safe for agents?
⚡ Reliability
26
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
75
Documentation
55
Error Messages
0
Auth Simplicity
95
Rate Limits
10

🔒 Security

TLS Enforcement
20
Auth Strength
20
Scope Granularity
10
Dep. Hygiene
40
Secret Handling
60

Local mode implies data stays on-device, but no explicit authentication/authorization is documented for MCP calls or remote Qdrant access. If running Qdrant in server mode (Docker/k3s), transport security (TLS), network exposure, and access controls are not described. Embedding provider usage (Gemini) may transmit data off-device depending on implementation. Secret handling is not discussed; environment variables are used for config paths/collection, but no guidance on API keys or logging hygiene is provided.

⚡ Reliability

Uptime/SLA
0
Version Stability
45
Breaking Changes
30
Error Recovery
30
AF Security Reliability

Best When

Running on-device (Raspberry Pi) or other local environments where data must stay local and a simple MCP-tool bridge workflow is acceptable.

Avoid When

When you need a long-running, network-exposed API with robust built-in auth/observability, or when you cannot manage embedding model downloads/updates and local storage configuration.

Use Cases

  • Local/edge AI agent memory that persists across reboots
  • Semantic recall of facts by meaning (not keywords)
  • Personal or household knowledge base with vector search
  • Project/team context retrieval (meeting notes, decisions)
  • Hard-enforced memory injection into OpenClaw agent prompts

Not For

  • Multi-tenant production systems needing strong network access controls out of the box
  • Use cases requiring fine-grained, standards-based query authorization and auditing
  • Real-time low-latency retrieval at scale without batching/caching (model load and embedding cost may be material)

Interface

REST API
No
GraphQL
No
gRPC
No
MCP Server
Yes
SDK
No
Webhooks
No

Authentication

Methods: Local process/STDIO MCP via mcporter (no explicit auth described) Optional embedding provider access for Gemini embeddings (not fully specified)
OAuth: No Scopes: No

No authentication/authorization is documented for the MCP server/tool calls. When using remote Qdrant via QDRANT_URL, the README implies HTTP access but does not describe auth (API keys, basic auth, TLS requirements, or network restrictions).

Pricing

Free tier: No
Requires CC: No

Claims local/private/$0 cost apply to storage/DB; embedding model provider costs are not clearly bounded in the provided content.

Agent Metadata

Pagination
none
Idempotent
False
Retry Guidance
Not documented

Known Gotchas

  • First call slower due to embedding model load; agents may time out if they expect warm calls
  • Environment variable expansion caveat for tilde (~) in mcporter env values
  • Do not use QDRANT_URL and QDRANT_LOCAL_PATH simultaneously (configuration conflict)
  • OpenClaw gateway config may not support mcpServers; README suggests using mcporter bridge instead

Alternatives

Full Evaluation Report

Comprehensive deep-dive: security analysis, reliability audit, agent experience review, cost modeling, competitive positioning, and improvement roadmap for qdrant-mcp-pi5.

AI-powered analysis · PDF + markdown · Delivered within 30 minutes

$99

Package Brief

Quick verdict, integration guide, cost projections, gotchas with workarounds, and alternatives comparison.

Delivered within 10 minutes

$3

Score Monitoring

Get alerted when this package's AF, security, or reliability scores change significantly. Stay ahead of regressions.

Continuous monitoring

$3/mo

Scores are editorial opinions as of 2026-03-30.

8642
Packages Evaluated
17761
Need Evaluation
586
Need Re-evaluation
Community Powered