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Qdrant MCP Server

Name: Qdrant MCP Server
Availability: InStock
Author: ai-supply

Official Qdrant MCP server — a semantic memory layer for storing and retrieving vectors.

@ai-supply

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⟳ upstream v0.8.1 · updated 8mo ago

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← More Data & ETL Data & ETL leaderboard →How we grade security →Source ↗

! Grade B · 75/100 · ReviewSecurity assessment

✓No compromise signals5capabilities surfaced1known CVE10of 20 OWASP controls clear

Broad capability surfaceVulnerable dependenciesExternal endpoints declared · expectedExternal endpoints declared · expected

scanned 1mo agoosv · gitleaks · opengrep · picklescan + heuristicsfull breakdown in the Security tab ↓

Qdrant MCP Server

mcp-server-qdrant is the official Model Context Protocol server for Qdrant, the open-source vector search engine. It acts as a semantic memory layer on top of a Qdrant collection.

The server exposes tools such as qdrant-store to save information (with optional metadata) and qdrant-find to retrieve semantically similar entries, letting an LLM keep and recall memories across sessions using vector search.

It is intended for developers building agents or assistants that need durable, searchable long-term memory backed by Qdrant.

! Security: Review · 7575/100 · grade Bscanned 1mo ago

✓ no compromise signals6 risk-surface · 3/20 OWASP controls flagged

Compromise signals — malicious or tampered code (leaked secrets, backdoors, a dropped executable) — reduce the score, and known dependency CVEs carry a bounded penalty (they warrant review but never QUARANTINE — update the dependency to clear). Other dangerous-by-capability traits are risk surface, expected for some capabilities. Every finding is mapped to its OWASP control below.

What this capability can do · low confidence (static)

egress → help.github.com, smithery.ai, modelcontextprotocol.io, qdrant.tech, docs.astral.sh, xyz-example.eu-central.aws.cloud.qdrant.io, qdrant.github.io, docs.cursor.com +4

Findings mapped to the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any flagged control for the exact findings — compromise reduces the score; expected/risk-surface do not, except a known CVE, which carries a small bounded penalty (high/critical → Review).

OWASP Top 10 for LLM Applications

⚠LLM03Supply Chaincritical

Vulnerable/compromised dependencies, models or archives in the artifact.

•Vulnerable dependencies — 89 known vulnerabilities in: authlib@1.6.5, cryptography@46.0.3, fastmcp@2.7.0, filelock@3.20.0, idna@3.11, pillow@11.3.0, protobuf@6.33.1, pydantic-settings@2.12.0 (CWE-1395)known CVE · -25 pts

⚠LLM06Excessive Agencymedium

Over-broad tool/permission surface or unrestricted egress.

•External endpoints declared — 1 distinct host(s) · qdrant-mcp-server-qdrant-f1a4d04/.github/workflows/pypi-publish.yamlexpected

•External endpoints declared — 3 distinct host(s) · qdrant-mcp-server-qdrant-f1a4d04/.gitignoreexpected

•Broad capability surface — 3 high-impact capability categories referenced — verify least-privilege · qdrant-mcp-server-qdrant-f1a4d04/README.md (CWE-272)risk surface

•External endpoints declared — 13 distinct host(s) · qdrant-mcp-server-qdrant-f1a4d04/README.mdexpected

•External endpoints declared — 2 distinct host(s) · qdrant-mcp-server-qdrant-f1a4d04/tests/test_settings.pyexpected

§LLM09MisinformationGovernance

Artifacts designed to produce false/deceptive output.

Detectable only by runtime behavioral evaluation; addressed via responsible-use attestation.

◷LLM10Unbounded ConsumptionRuntime-enforced

Unbounded loops/recursion causing DoS or runaway cost.

Enforced at runtime by the gateway (rate limits + spend caps + size caps); static check flags unbounded loops.

✓LLM01Prompt InjectionPassed

✓LLM02Sensitive Information DisclosurePassed

✓LLM04Data and Model PoisoningPassed

Backdoors/poisoning in training data or serialized models.

Behavioral poisoning needs model execution; static check covers unsafe serialization + dataset skew only.

✓LLM05Improper Output HandlingPassed

✓LLM07System Prompt LeakagePassed

✓LLM08Vector and Embedding WeaknessesPassed

PII or plaintext source leakage in embedding/vector exports.

Embedding inversion/poisoning is largely runtime; static check covers PII in vector exports.

OWASP Machine Learning Security Top 10

⚠ML06AI Supply Chaincritical

Compromised PyPI/npm packages, typosquats, unsafe serialized models.

§ML01Input Manipulation (Adversarial)Governance

Models vulnerable to adversarial perturbations.

Requires runtime robustness evaluation; addressed via publisher robustness attestation.

§ML03Model InversionGovernance

Training data reconstructable from a model's outputs.

Runtime/evaluation property; addressed via model-card data-provenance + DP attestation.

§ML04Membership InferenceGovernance

Determining whether a record was in the training set.

Runtime/evaluation property; addressed via overfitting disclosure + DP attestation.

§ML08Model SkewingGovernance

Models trained on skewed data producing biased output.

Requires fairness evaluation; addressed via model-card bias/limitations disclosure.

◷ML09Output IntegrityRuntime-enforced

Middleware tampering with model outputs in transit.

Gateway enforces TLS + response integrity; static check flags output-rewriting code.

✓ML02Data PoisoningPassed

Poisoned training datasets with triggers or anomalous distributions.

Static check covers trigger phrasing, PII and label skew; full poisoning detection is runtime.

✓ML05Model TheftPassed

Unlicensed re-distribution / license-incompatible derivatives.

Static check verifies license declaration; extraction throttling is runtime.

✓ML07Transfer Learning AttackPassed

Backdoored base models / LoRA adapters propagating to derivatives.

Backdoor detection needs behavioral probing; static check covers unsafe serialization + provenance.

✓ML10Model Poisoning (Weights)Passed

Tampered model weight files; integrity must be verifiable.

Static check enforces safe formats + records a content hash for downstream verification.

Other findings (3) · hygiene / uncategorized

•Unrecognized file type — '.gitignore' is not on the allowlist · qdrant-mcp-server-qdrant-f1a4d04/.gitignorerisk surface

•Unrecognized file type — '.python-version' is not on the allowlist · qdrant-mcp-server-qdrant-f1a4d04/.python-versionrisk surface

•Unrecognized file type — '.?' is not on the allowlist · qdrant-mcp-server-qdrant-f1a4d04/Dockerfilerisk surface

✔ verified source · pinned qdrant-mcp-server-qdrant-f1a4d04

Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/qdrant-mcp-server/check). Click a policy:

Consume Qdrant MCP Server programmatically. Authenticate with an API key or session — see Authorize an agent.

# Agents: CHECK BEFORE YOU INSTALL (no auth) — score, grade, level, capability manifest
curl https://ai-supply.store/api/v1/trust/qdrant-mcp-server

# Gate against your org policy (returns { pass, violations })
curl -X POST https://ai-supply.store/api/v1/trust/qdrant-mcp-server/check \
  -H "Content-Type: application/json" \
  -d '{"minGrade":"B","denyPermissions":["shell"],"denyUnknownEgress":true}'

# CLI
npx ai-supply add qdrant-mcp-server

# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/qdrant-mcp-server/install \
  -H "Authorization: Bearer $AIM_KEY"

# MCP tool
install_listing({ "slug": "qdrant-mcp-server" })

OpenAPI spec →