Memory (NeetPatel) MCP Server
mcp-memory-server
PyPI
v1.5.0
Published by neetpatel — no publish provenance, so origin is unverified, but the source is public: the repository link below is self-declared yet readable, so you can inspect the code before adopting it.
MCP server that exposes Project AI Memory (memory.md) as resources and tools for Cursor, Claude, and other MCP clients
The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.
1. Threat score — 100 − 0 = 100. What the published surface and source actually contain:
The deterministic scan raised no scored threat in the surface it inspected — the threat score stayed at 100. Capability observations and advisory notes are recorded but never lower it.
2. Client adoption risk — 100 − 11 = 89. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:
| Points | Adoption-risk factor |
|---|---|
| −10 | capability blast radius (critical) — client exposure if the model is manipulated |
| −1 | publisher verification (public source) — no provenance, but the source is public and inspectable |
Capability observations and info notes are shown under Findings but never scored.
Open any row's finding below for the file, line and evidence behind a deduction.
Findings 2
critical Completed toxic-flow trifecta across toolsMTC-FLOW-002
This server (without client built-ins) exposes a complete data-exfiltration chain: get_memory_section → dba.query → prometheus.query. Untrusted input is ingested, private data is read, and it can be sent to an external sink via the agent composing the tools (→). Static analysis proves the primitive exists, not that a specific run will occur.
Fix: Remove one leg of the trifecta: isolate untrusted-input tools from secret-reading tools and from egress tools, or require human approval between them.
Location: flow get_memory_section → dba.query → prometheus.query
high Shell/command execution in server code (mcp_memory_server/server.py)MTC-SRC-002
In the server's implementation (`mcp_memory_server/server.py:40`): Spawning a shell/process is command-execution capability; with unsanitized tool input it is command injection / RCE. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: nv or {})} result = subprocess.run( cmd, cwd=cwd, env=env, capture_output=True,
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server mcp_memory_server/server.py
Tools 22
Each tool and what it can reach — statically extracted from the published source.
dba.queryreads sensitive dataget_memory_sectioningests untrusted inputprometheus.querynetwork egressdocker.imagesno sensitive capabilitydrift.checkno sensitive capabilityget_code_standardsno sensitive capabilityget_security_checklistno sensitive capabilitygithub.actionno sensitive capabilitygitlab_pipeline_statusno sensitive capabilityjenkins.buildno sensitive capability
Show 12 more tools ↓
jenkins.jobsno sensitive capabilityk8s.driftno sensitive capabilityk8s.podsno sensitive capabilitymcp.healthno sensitive capabilitymcp.toolsno sensitive capabilityregistry.imagesno sensitive capabilitysecurity.scanno sensitive capabilitysystem.cpuno sensitive capabilitysystem.diskno sensitive capabilitysystem.memoryno sensitive capabilityterraform.planno sensitive capabilitytools.versionsno sensitive capability
Toxic flows 1
Cross-tool combinations that form a data-exfiltration primitive (untrusted input → sensitive source → external sink).
What this scan could not see
- Tools were statically extracted from the published source (22 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Versions 1
Scan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.
| Version | Score | Findings | Engine | Scanned |
|---|---|---|---|---|
v1.5.0 latest |
B 89/100 | 2 | 1.13.0 | 2026-08-25 |
Embed this score
Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.
Verify this score yourself
The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.
npx mcptrustchecker scan mcp-memory-server --online --registry pypiOther implementations of Memory 38
Independent packages implementing the same tool, scanned with the same engine. Compare all 39 side by side →
The official knowledge-graph memory server: persistent entities, relations and observations.
The agentage Memory MCP server: exposes your local vaults as the frozen 6 memory__* tools over stdio. The open, cross-vendor counterpart to @modelcontextprotocol/server-memory.
Semantic memory and knowledge management for Claude Code.
MCP server for enabling memory for Claude through a knowledge graph
MCP server for enabling memory for Claude through a knowledge graph
MCP server for enabling memory for Claude through a knowledge graph
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