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◇MCP serverData & ETLFree

Qdrant MCP Server

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

@ai-supply
Installs41k
↗ Source repository
← More Data & ETLData & ETL leaderboard →How we grade security →Source ↗

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.

Rating rank
#1
of 24 in Data & ETL
Install rank
#18
of 24 in Data & ETL
Security score
100/100 · A
safe
Security rank
#1
of 24 in Data & ETL
Installs
41k
cat avg 164k
This listing vs category average
Installs
this
cat avg
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✓ Security: Safe · 100100/100 · grade Ascanned 2h ago

Only compromise signals — malicious or tampered code (leaked secrets, backdoors, path traversal, a dropped executable) — reduce the score. Dangerous-by-capability traits (shell, network, injection strings, pickle) are shown as risk surface: expected for some capabilities — a security tool ships offensive code on purpose — so they do not sink the grade.

Compromise signals
None — no malicious or tampered code detected in the scanned source.
What this capability can do · low confidence (static)
egress → qdrant.example.com
Risk surface (7)
External endpoints declaredlowexpected for this capabilityqdrant-mcp-server-qdrant-f1a4d04/.github/workflows/pypi-publish.yaml
1 distinct host(s)
External endpoints declaredlowexpected for this capabilityqdrant-mcp-server-qdrant-f1a4d04/.gitignore
3 distinct host(s)
Broad capability surfacelowqdrant-mcp-server-qdrant-f1a4d04/README.md
3 high-impact capability categories referenced — verify least-privilege
Possible obfuscationmediumqdrant-mcp-server-qdrant-f1a4d04/README.md
very long lines
External endpoints declaredmediumexpected for this capabilityqdrant-mcp-server-qdrant-f1a4d04/README.md
13 distinct host(s)
External endpoints declaredlowexpected for this capabilityqdrant-mcp-server-qdrant-f1a4d04/tests/test_settings.py
2 distinct host(s)
Vulnerable dependenciescritical
77 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
✔ 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:

OWASP AI control mapping
10passed
3flagged
2runtime-enforced
5governance

Evaluated against the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any control to see the findings.

OWASP Top 10 for LLM Applications
✓LLM01Prompt InjectionPassed
✓LLM02Sensitive Information DisclosurePassed
⚠LLM03Supply Chaincritical
Vulnerable/compromised dependencies, models or archives in the artifact.
•Vulnerable dependencies — 77 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)
✓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
⚠LLM06Excessive Agencymedium
Over-broad tool/permission surface or unrestricted egress.
•External endpoints declared — 1 distinct host(s)
•External endpoints declared — 3 distinct host(s)
•Broad capability surface — 3 high-impact capability categories referenced — verify least-privilege (CWE-272)
•External endpoints declared — 13 distinct host(s)
•External endpoints declared — 2 distinct host(s)
✓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.
§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.
OWASP Machine Learning Security Top 10
§ML01Input Manipulation (Adversarial)Governance
Models vulnerable to adversarial perturbations.
Requires runtime robustness evaluation; addressed via publisher robustness attestation.
✓ML02Data PoisoningPassed
Poisoned training datasets with triggers or anomalous distributions.
Static check covers trigger phrasing, PII and label skew; full poisoning detection is runtime.
§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.
✓ML05Model TheftPassed
Unlicensed re-distribution / license-incompatible derivatives.
Static check verifies license declaration; extraction throttling is runtime.
⚠ML06AI Supply Chaincritical
Compromised PyPI/npm packages, typosquats, unsafe serialized models.
•Vulnerable dependencies — 77 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)
✓ML07Transfer Learning AttackPassed
Backdoored base models / LoRA adapters propagating to derivatives.
Backdoor detection needs behavioral probing; static check covers unsafe serialization + provenance.
§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.
✓ML10Model Poisoning (Weights)Passed
Tampered model weight files; integrity must be verifiable.
Static check enforces safe formats + records a content hash for downstream verification.

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 →
vlatest
✓ Security: Safe · 1002h ago

Curated mirror — latest upstream source. See the repository for tagged releases.

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