Claude Context (hamzaamjad) MCP Server
mcp-claude-context
PyPI
v0.5.4
Published by hamzaamjad — 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 for Claude conversation extraction with enhanced export and search
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 − 6.3 = 94. What the published surface and source actually contain:
| Points | What was found | Category |
|---|---|---|
| −6.3 | Unsafe deserialization MTC-SRC-007 | permissions |
2. Client adoption risk — 94 − 4 = 90. 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 |
|---|---|
| −3 | capability blast radius (moderate) — 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
medium Unsafe deserialization (src/search/semantic_search.py)MTC-SRC-007
In the server's implementation (`src/search/semantic_search.py:63`): Deserializing untrusted data with these APIs can execute arbitrary code (a well-known RCE gadget class). This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: f.conversation_id_map = pickle.load(f) with open(msg_map_file, 'rb') as f: self
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 src/search/semantic_search.py
Tools 14
Each tool and what it can reach — statically extracted from the published source.
export_to_obsidiannetwork egressbulk_operationsno sensitive capabilityexport_conversationsno sensitive capabilityget_analyticsno sensitive capabilityget_conversationno sensitive capabilityget_conversation_messagesno sensitive capabilityget_rate_limit_metricsno sensitive capabilitylist_conversationsno sensitive capabilitymigrate_to_databaseno sensitive capabilityrebuild_search_indexno sensitive capability
Show 4 more tools ↓
search_conversationsno sensitive capabilitysearch_messagesno sensitive capabilitysemantic_searchno sensitive capabilityupdate_sessionno 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 |
|---|---|---|---|---|
v0.5.4 latest |
A 90/100 | 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.
npx mcptrustchecker scan mcp-claude-context --online --registry pypiOther implementations of Claude Context 4
Independent packages implementing the same tool, scanned with the same engine. Compare all 5 side by side →
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Model Context Protocol integration for Claude Context
Model Context Protocol integration for Claude Context
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