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Apiary Memory

PYPI · APIARY-MEMORY

Local-first memory for AI agents. MCP locally, HTTP from the web. Agents propose; you confirm.

Available components

+5 this week 81 Trust /100

Last scanned (1 hour ago)

Scores higher than 82% of PyPI servers

Category Memory and Knowledge

Trust breakdown (7 categories)

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →

Supply Chain Security100
  • No malware found by supply-chain analysis.Pass
  • No known CVEs affecting this package version or its production dependencies.Pass
  • Runs setuptools.build_meta at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
  • No production dependencies, so there is no dependency health to assess. View diagnostics → Pass
Provenance & Transparency100
  • Source repository is publicly reachable at the declared URL. View diagnostics → Pass
  • Cryptographically verified build provenance (signed, bound to 330899935/apiary-memory). View diagnostics → Pass
  • Clear OSI-approved license (MIT).Pass
  • Actively maintained (last published 2 days ago).Pass
  • Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability64
  • AI-judged instruction clarity (excellent).Pass
  • Context-footprint check failed: tool/resource definitions use about 1376 tokens (~229/item across 6 items; 6 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
  • Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management30
  • Stability observed for 9 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
  • 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
  • 100% of tool parameters carry a description.Pass
Tool Safety100
  • No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
  • We read all 6 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
  • An AI judge read all 7 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities20
  • Spec-recency check failed: implements MCP spec 2024-11-05; the latest is 2026-07-28. See how to fix → Fail
Changelog

Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

  • 5 Nov 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.

  • 3 Nov 26 +16
    • Malware scan: unverified → pass ▲ security
  • 2 Nov 26 −12
    • Malware scan: pass → unverified ▼ security
    • Stability: unverified → 0.20 ▲ functional
    • Package version: 0.2.0 → 0.2.2 functional
    • Package version: 0.2.0 → 0.2.1 functional
  • 28 Oct 26 +15
    • Malware scan: unverified → pass ▲ security
  • 27 Oct 26 61

    First indexed and scored.

Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 5 Nov 2026 · Analysed pypi/apiary-memory@0.2.2

Provenance Verified

A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.

Result Verified
Ecosystem pypi
Reason Verified
Discovered via Registry attestation endpoint
Source repo 330899935/apiary-memory
Certificate issuer https://token.actions.githubusercontent.com
Certificate SAN https://github.com/330899935/apiary-memory/.github/workflows/publish.yml@refs/tags/v0.2.2
Rekor log index 3638182240
Predicate type PyPI publish attestation https://docs.pypi.org/attestations/publish/v1
Subject digest sha256:c7e0763b3d7217d1faf1d7112f91e366d378212771e41a4134fc81e352f16082

Background: How many MCP packages publish verified provenance →

Install scripts 1 script
Hook Tier Command
build_backend allowlisted setuptools.build_meta

Background: Why install scripts are a supply-chain risk →

Dependencies 0 packages
Packages resolved 0
Tree resolution Complete

Background: SBOMs and build attestations, explained →

MCP tools · 6 exposed · ~1,276 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →

Tool Tokens
board ~151

Read the shared board, optionally only what arrived since your last read. Use it to poll for messages or hand-offs addressed to you. To post, use say. Pass the previous cursor as since so a poll returns only new messages instead of the whole board. Annotations declare this read-only; note that messages expire, so an empty result can mean nothing was posted or that earlier messages have aged out.

NameTypeReqDescription
limitinteger–Maximum number of messages to return. Omit for the server default.
rosterboolean–Also return which agents are currently on duty.
sincestring–Cursor from a previous call. Only messages newer than this cursor are returned; omit to read the current board.

No output schema declared.

No examples provided.

get_context ~128

Pull a short digest of recent memory from the human's active threads and open tasks, to seed a conversation before they ask anything. Call this once at session start. For answering a specific question, use recall instead, since that searches all confirmed memory rather than only what is recent. For entries still awaiting approval, use manage with action list_pending. Annotations already declare this read-only; what they cannot say is that the digest reflects the most recent activity, so two calls minutes apart may differ.

NameTypeReqDescription
limitinteger–Maximum number of context entries to return. Omit for the server default.

No output schema declared.

No examples provided.

manage ~157

Inspect or change the lifecycle of a specific stored memory, addressed by id or by queue state. Use list_pending to review what is awaiting the human's approval, and get_by_id to read one entry in full. forget soft-deletes and is reversible with restore; discard_pending permanently drops an entry that was never confirmed. To search confirmed memory by topic instead of by address, use recall. Except for discard_pending, every action here is reversible, which is why this is not marked destructive.

NameTypeReqDescription
actionstringyesWhich lifecycle operation to run. list_pending takes no id; the other four require one.
idstring–Target memory id. Required for forget, restore, get_by_id and discard_pending; ignored by list_pending.

No output schema declared.

No examples provided.

propose ~234

Submit a memory for the human to approve. It lands in a pending queue and changes no confirmed memory. Use it when the human says to remember something, or when a decision is reached that will still matter later. Do not use it as a scratchpad for transient reasoning or for data you merely fetched. This tool cannot confirm its own writes: the entry stays pending until the human accepts it inside their own Hive, so a success response means queued, not stored. Supply client_id to make retries idempotent.

NameTypeReqDescription
client_idstring–Idempotency key. Submitting twice with the same key does not create a duplicate - use this when retrying after a timeout.
contentstringyesThe memory to store, written as a standalone fact that still makes sense without this conversation - include the who and the when.
tagsarray–Free-form tags for later filtering.
titlestring–Short label shown in the human's pending queue. Helps them decide quickly.
vaultstring–Target vault name. Private vaults are rejected for cloud agents; local agents may target them.

No output schema declared.

No examples provided.

recall ~310

Search the human's confirmed memory by natural-language query and return the closest stored entries. Use this to find what was previously decided, recorded, or agreed. Do not use it at session start when you have no query yet - use get_context instead. Do not use it to review entries still awaiting approval - use manage with action list_pending. Annotations already declare this read-only, so this text covers what they cannot: results come from the confirmed store only, private vaults are omitted for agents not permitted to read them, and an empty list means no match rather than no memory.

NameTypeReqDescription
compactboolean–Return trimmed records (id, title, snippet) instead of full bodies. Saves tokens when you only need to know whether something exists.
limitinteger–Maximum number of memories to return. Omit for the server default.
querystringyesNatural-language search text, e.g. 'what did we decide about the pricing page'. Ranked by how much of it the entry's words and character bigrams cover, plus a small recency nudge and a bonus when the…
vaultsarray–Narrow the search to these vault names. Omit to search every vault this agent may read. Naming a vault the agent may not read is not an error - it returns nothing from that vault.

No output schema declared.

No examples provided.

say ~296

Post a message to the shared board so the human and other agents see what was done or what is being handed over. Use it when finishing a unit of work, or to hand work to another agent. Do not use it to report routine steps the human did not ask about - the human reads this board. To read what others have posted, use board. Identity comes from your credential, so a caller cannot post as another agent.

NameTypeReqDescription
kindstring–Card type. May be omitted when stage is given, in which case the kind is derived from the stage.
refsarray–References the reader may want: file paths, commit shas, or memory ids.
stagestring–Work event: starting, handing off, finished, or blocked. Prefixes the body so the board can be filtered and de-duplicated.
summarystringyesWhat happened, in one or two sentences. Write the outcome, not a log of steps.
task_idstring–Link this message to an existing task; the hand-off graph aggregates by it.
tostring–Who should see it: 'master' for the human (default), or another agent's id.
ttlinteger–Seconds after which the message stops being returned by board. Defaults to 24 hours, and is clamped to between 60 seconds and 30 days so no card is immortal.

No output schema declared.

No examples provided.

Install

How do I install the Apiary Memory MCP server?

Apiary Memory runs locally as a PyPI package, launched with uvx apiary-memory. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

pypi · apiary-memory

# add to Claude Code
claude mcp add 330899935-apiary-memory -- uvx apiary-memory
Common questions

What is the Apiary Memory MCP server?

Apiary Memory is an MCP server listed in the public MCP registry as io.github.330899935/apiary-memory. Local-first memory for AI agents. MCP locally, HTTP from the web. Agents propose; you confirm. This page covers its PyPI package (apiary-memory).

Is the Apiary Memory MCP server safe to use?

Apiary Memory scores 81 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

What tools does the Apiary Memory MCP server expose?

Apiary Memory exposes 6 tools: recall, propose, manage, get_context, say, board. Their descriptions and schemas cost roughly 1,276 tokens of context every time the server is loaded.

Is the Apiary Memory MCP server still maintained?

Apiary Memory is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

What licence is the Apiary Memory MCP server under?

Apiary Memory declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.