Adaptive Agent MCP Server
adaptive-agent-mcp
PyPI
v0.6.2
Published by justforever17 — 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.
Self-Evolving RAG for AI Agents — A cross-app persistent memory system where agents autonomously write, retrieve, and evolve their knowledge
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 − 7 = 93. 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 |
|---|---|
| −6 | capability blast radius (high) — 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 1
high Shell/command execution in server code (adaptive_agent_mcp/src/search_engine.py)MTC-SRC-002
In the server's implementation (`adaptive_agent_mcp/src/search_engine.py:48`): 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: y result = subprocess.run( cmd, capture_output=True, t
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 adaptive_agent_mcp/src/search_engine.py
Tools 14
Each tool and what it can reach — statically extracted from the published source.
add_knowledge_relationno sensitive capabilityappend_daily_logno sensitive capabilityarchive_periodno sensitive capabilitydelete_knowledgeno sensitive capabilityextract_knowledgeno sensitive capabilityget_period_contextno sensitive capabilityinitialize_sessionno sensitive capabilitymulti_hop_queryno sensitive capabilityquery_knowledgeno sensitive capabilityquery_knowledge_graphno sensitive capability
Show 4 more tools ↓
query_memory_headersno sensitive capabilityread_memory_contentno sensitive capabilitysearch_memory_contentno sensitive capabilityupdate_preferenceno sensitive capability
What this scan could not see
- Tools were statically extracted from the published source (14 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 |
|---|---|---|---|---|
v0.6.2 latest |
A 93/100 | 1 | 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.
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