mnemosyne (28naem-del/mnemosyne) is an MCP server listed on the M8ven Trust Index. It scores 74 out of 100, grade C. It declares 11 tools. No publisher has claimed this listing.

Warning

74/100

mnemosyne

Item: mnemosyne
Rating: 74
Author: M8ven

Evidence-backed memory for AI agents: local-first recall, temporal history, tested skills, and gradual migration.

→ 28naem-del/mnemosyne · listed on github_topic

Warning. Serious findings were identified. Review the full report before connecting. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

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Who stands behind it

28naem-del

Source: github_topic

Is this your MCP?

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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

🚨

Code appears obfuscated

2 files are unreadable to a human reviewer. Cannot audit what they do.

🔐

You'll be asked for 1 credential: MNEMOSYNE_TOKEN

These are read from process.env at runtime. Make sure you trust where they’ll be sent.

// tools this server exposes17 tools · 6 behind config

These names and descriptions are the publisher's own, read from the source code. We print them as written. Our assessment is the findings above, not this list.

memory_recall

memory_context

Compile cited, scoped context with contradictions and abstention under a conservative byte-based token budget. Pass only packet.text as context; diagnostics are outside the budget.

memory_inspect

Inspect accessible stored evidence and its lifecycle. Inactive records are history, not current advice.

memory_resume

Read the latest accessible active checkpoint for a task. Completed steps and artifact paths are historical claims. Verify current environment and permissions before acting.

memory_source

Read a bounded UTF-8 range of the original captured transcript or extracted document. Its status and trust remain visible.

memory_model

Inspect a source-backed project model. Stale models are marked and must not be treated as current knowledge.

memory_skill

Inspect a reusable skill and its prerequisites, trial evidence and current eligibility. This does not execute the skill.

memory_jobs

Inspect bounded observation/consolidation jobs and their outcome. Only the host executes configured model workers.

memory_entity

Resolve an entity name or alias without silently merging ambiguous identities.

memory_traverse

Follow a bounded number of evidence-backed relationship hops; each hop retains source IDs.

memory_branch_preview

Inspect isolated proposed changes and source conflicts before a controller merges them.

memory_storebehind config

Record an observed fact, preference, decision or procedure with a source. Private by default; workspace visibility explicitly shares with this workspace. Agent notes cannot self-certify as verified. Similar text is never automatically merged.

memory_capturebehind config

Capture supplied transcript events with stable retry IDs. This reads no host files. Observed messages remain fallible evidence.

memory_observebehind config

Queue source-linked observations or a project model for the host to process under its configured model and budget. This does not invoke or choose a model.

memory_checkpointbehind config

Save a resumable task state, preserving constraints, rejected approaches and unfinished work. It does not perform or authorize actions.

memory_correctbehind config

Explicitly supersede your own non-verified observation; dependent advice becomes invalid. Controller-verified evidence must be corrected through the controller SDK.

memory_forgetbehind config

Purge an owned memory and content derived from it from this live local store. This cannot erase external exports, backups or context already sent to agents.

// 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.

configMNEMOSYNE_EVAL_ENGINE_REVISION

🔐 secretMNEMOSYNE_TOKEN

// quality suggestions

All four hints declared on every tool

17/17 tools missing one or more hints — memory_recall (missing: idempotentHint); memory_context (missing: idempotentHint); memory_inspect (missing: idempotentHint), +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.

Tool test coverage

11/17 tools referenced in tests (65%)

Write tests that reference each tool by name so every tool has at least one test.

Readable source code

2 files are minified or bundled, which is usually build output rather than concealment

Ship unminified, readable source.

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 3 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: efd42ec86e9d0714821dc1ddd9ffa3b171012d55

code hash: 533c5fe3d2a5bd547b2f627d0282092b32e397fc38d858d99fffe344c25aff1f

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