fava-trails (MachineWisdomAI/fava-trails) is an MCP server listed on the M8ven Trust Index. It scores 74 out of 100, grade C. It declares 17 tools. No publisher has claimed this listing.

Back to MCP Trust IndexM8ven
C
Emerging
74/100

fava-trails

🫛👣 FAVA Trails — Git-native, curated memory for AI agents via MCP. Draft isolation, promotion gate, thought lifecycle hooks, memory curation protocols, supersession chains.

→ MachineWisdomAI/fava-trails· listed on github_repo_search

Emerging. No concerning findings. Grades remain capped until the project builds reputation through adoption. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

How we verified

⚡ Live Monitored: not connected

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Who stands behind it

MachineWisdomAI

Source: github_repo_search

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Install from

GitHub

The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.

// key findings
✅
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
✅
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
// environment variables
To run this server yourself, you supply these values. They go in your own MCP client configuration and stay on your machine. The secret label means the value is sensitive, not that the server mishandles it.
configFAVA_TRAILS_LOG_DIR
configFAVA_TRAILS_SCOPE_HINT(none)
configFAVA_TRAILS_DATA_REPO~/.fava-trails
configJJ_VERSION
configINSTALL_DIR
configXDG_CACHE_HOME
configHF_HOME
configTRANSFORMERS_CACHE
configXDG_CONFIG_HOME
configFAVA_TRAILS_DIR$FAVA_TRAILS_DATA_REPO/trails
configFAVA_TRAILS_AGENT_IDThe operator configures on a dedicated process; caller
configFAVA_TRAILS_OPERATOR1 only on a separate operator-controlled endpoint.
configSHELL
configXDG_STATE_HOME
// quality suggestions

Tool annotations

No tools have read-only/destructive annotations

Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.

All four hints declared on every tool

17/17 tools missing one or more hints — start_thought (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); save_thought (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_thought (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +14 more. OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.

For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.

Claim the listing to review these findings one by one and send us a correction where you disagree, straight to the team. Claiming also means we tell you when the grade moves, and reach you first if we find anything urgent.

// full audit trail
The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.
// improvement guidance — verified publishers only
We have 2 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
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commit: 10f689f7455c0c5c5898f2a2e6bc8cf4fe84a6d7
code hash: 15b0995ec08330c1589136eba9ef0bee1448aee3b28b9449f880621ced7fd1f6
view raw JSON →
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