shared-context-cache-mcp-server
A Python MCP server that provides a shared cache for AI-agent computed results, including a trust/verification mechanism where agents can store results, confirm accuracy, and retrieve only entries that meet a configurable minimum trust threshold. Cache entries expire via TTL and basic/advanced analytics tools are exposed via MCP tools.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
No authentication/authorization is documented for cache read/write or confirmations, and the trust mechanism is described without identity verification. This is a potential integrity risk in adversarial settings (cache poisoning / false confirmations). TLS usage for any remote backend is not specified; the README only references a remote cache URL for backend, not how connections are secured. Dependency list is small (mcp, httpx), but no vulnerability/CVE or security posture details are provided.
⚡ Reliability
Best When
Multiple cooperating agents (or teams) repeatedly compute the same deterministic-ish results and you want lightweight cross-agent sharing with a consensus-style confidence score.
Avoid When
Confirmations and cache contents are influenced by untrusted or potentially malicious actors, or when you require cryptographic guarantees for provenance/authenticity.
Use Cases
- • Sharing repeated computations across multiple AI agents to reduce token cost and latency
- • Caching lookup-like tasks (e.g., weather, price checks) where results can be verified by peers
- • Maintaining higher-confidence answers via min-trust retrieval (e.g., 3+ confirmations)
- • Tracking cache effectiveness (hits/misses/cost savings) and trust distribution
Not For
- • Security-critical or adversarial environments where untrusted agents could intentionally poison the trust layer
- • Highly regulated workloads requiring strong audit trails, formal compliance guarantees, or strong tenant isolation
- • Use cases that require strong authenticity/identity verification of confirming agents
Interface
Authentication
No authentication mechanisms are described in the README. The trust layer appears to be based on confirmations and local persistence rather than authenticated agent identities.
Pricing
No pricing information provided; appears to be a self-hosted/open-source MCP server via PyPI.
Agent Metadata
Known Gotchas
- ⚠ Trust score increases via confirmations; repeated confirmations by the same agent (or duplicate events) could inflate trust unless the server deduplicates (not documented).
- ⚠ get_trusted uses a configurable threshold; ensure consistent min_trust settings across agents for expected behavior.
- ⚠ Cache key conventions are important; inconsistent keying will reduce cache hit rate.
- ⚠ Trust persistence is local to the user (~/.shared_context_cache_trust.json); running multiple deployments/users may not share trust state as expected.
Alternatives
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Scores are editorial opinions as of 2026-04-04.