Query Markup Documents - On-device hybrid search for markdown files with BM25, vector search, and LLM reranking
Nothing to run from here
Zengram is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
96/100
trust score
1
downloads a day
local
runs on your machine
68
downloads in 30d
What it does
Persistent memory for AI agents — pgvector + BM25 keyword search with RRF fusion, credential scrubbing, auto-consolidation.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user zengram-mcp '--' npx -y @zensystemai/zengram-mcp
# 2 — ask your agent for something these tools do
# brain_store, brain_search, brain_briefing and 10 more
Zengram costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
ZEAll 1 server
@zensystemai1 server
1 category
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. This one runs on your machine: your client starts Zengram as a process under your own user, with your files and your network, so the tool surface below is what it can do to your computer rather than to a server somewhere else. Its scan result is on the Security tab — read the surface before you approve it.
Claude Codedeclared by this listing
This is a public server: you run it yourself and this marketplace is not in the path. You need Node.js — the current LTS (24) is the safe choice; npx comes with it. Claude Code registers it in one command; --scope user makes it available in every project. The '--' is in quotes so that PowerShell passes it on.
claude mcp add --scope user zengram-mcp '--' npx -y @zensystemai/zengram-mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it runs from its publisher’s own package: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it writes
One config entry your client uses to start the process locally. A local server runs with your file system and your network, which is why it is priced without metering.
What you approve
13 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
Store a memory in the Shared Brain. Use this to record events (something happened), facts (persistent knowledge), decisions (choices made and why), or status updates (current state of systems/workflows). All agents share this memory. Duplicate content is automatically detected and deduplicated.…
No parameter schema on file.
Multi-path search across all shared memories. Runs vector (semantic) and full-text keyword retrieval in parallel, merged with Reciprocal Rank Fusion into a blended ranking. Returns compact format by default to save tokens. Use format="full" to see which retrieval paths contributed to each result.
No parameter schema on file.
Get a session briefing: what happened since a given time across all agents. Excludes entries from the requesting agent by default. Returns compact format (truncated content) to save tokens — use format="full" only when you need complete content.
No parameter schema on file.
Structured query of shared memories via the database. Query facts by key, statuses by subject, or events by time range. Use brain_search for semantic queries instead.
No parameter schema on file.
Get memory health stats: total memories, active vs superseded, consolidated, decayed, breakdown by type. Use this to understand the state of the shared brain.
No parameter schema on file.
Trigger a memory consolidation run. An LLM analyzes unconsolidated memories to find duplicates to merge, contradictions to flag, connections between memories, and cross-memory insights. Runs automatically on a schedule, but can be triggered manually. Default action "run" is async (returns a job_id…
No parameter schema on file.
Query the entity graph. Entities are automatically extracted from memories — clients, people, technologies, workflows, domains, agents. Use this to find all entities, get details about one, or list all memories linked to an entity.
No parameter schema on file.
Soft-delete a memory by ID (marks it inactive). The memory remains in storage but is excluded from search results. Agent-scoped keys can only delete their own memories. Use this for compliance or to remove incorrect/sensitive memories.
No parameter schema on file.
Update an existing memory's content, importance, or category without creating a new version. Re-embeds and re-indexes if content changes. Agents can only update their own memories.
No parameter schema on file.
Export shared memories as JSON for backup or migration. Returns memory payloads (no vectors). WARNING: Can return very large responses — use limit, client_id, type, or since filters to avoid exceeding MCP message size limits. Default limit is 500.
No parameter schema on file.
Operator-only: import memories from JSON (e.g. from a brain_export backup). Mutates the memory store, re-embeds with current provider, and deduplicates by content hash. Requires operator_approved=true. Max 500 records per call.
No parameter schema on file.
Reflect on a topic by synthesizing patterns across stored memories. Searches relevant memories using multi-path retrieval, then uses LLM to analyze and produce insights about patterns, timeline evolution, contradictions, and knowledge gaps. Use this for "what do we know about X?", "what patterns…
No parameter schema on file.
Agentic, iterate-until-sufficient retrieval for HARD MULTI-HOP questions only (e.g. "find the decision that superseded X, then what status it left Y in"). Runs a loop: retrieve -> judge whether the gathered memories actually answer the question -> if not, search the named gap -> repeat (bounded)…
No parameter schema on file.
Free
$0 forever
- Every tool, no call limit
- No card, and mcprush charges nothing for it
- Source published under MIT
- Runs on your machine — nothing of it reaches our gateway
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install Zengram or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
—
—
Alert at 80% · then the tool returns a normal MCP error
Used this month: —
ATrust score 96 / 100
Release 4.5.0 · scanned 15 Oct 2026
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
Threat score100
Client score96
Findings0 threat · 0 capability
Capabilitymoderate
Coveragesource
Verificationrepo
Surface digest59bb0ce680308e875062a4c27871e25b2dd9d598c0f928c4b8f0a45e89a812c4
Score vectorthreat 100 → client 96
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | moderate | capability blast radius (moderate) — client exposure if the model is manipulated | −3 |
| verification-discount | repo | publisher verification (public source) — no provenance, but the source is public and inspectable | −1 |
| coverage-honesty | source | inspection depth (source) — how much of the target the scan could see | −0 |
score = clamp(round(threatScore − E_cap − E_ver − E_cov), 0, 100). No model, no opinion, no network call at scoring time: the same methodology version against the same bytes always produces this number, and every line above is a field of the report rather than a summary of it.
What the scan could actually read
A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.
Tool surfaceread
Implementation sourceread
Package metadataread
Live transportnot available
Tools were statically extracted from the published source (13 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>".
Capability — what it could do if the model were manipulated
Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. moderate is the level these add up to.
| Tool | Capability tags | Why the tag was assigned |
|---|---|---|
| brain_store | state-write | state-write: declares readOnlyHint: false |
| brain_search | no tags | |
| brain_briefing | no tags | |
| brain_query | no tags | |
| brain_stats | no tags | |
| brain_consolidate | state-write | state-write: declares readOnlyHint: false |
| brain_entities | no tags | |
| brain_delete | state-write | state-write: declares readOnlyHint: false, destructiveHint: true |
| brain_update | state-write | state-write: declares readOnlyHint: false |
| brain_export | no tags | |
| brain_import | state-write | state-write: declares readOnlyHint: false, destructiveHint: true |
| brain_reflect | no tags | |
| brain_research | no tags |
Toxic-flow graph
The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.
The public result for this release does not print the flow graph, so there is nothing to show here. That is not the same as "no paths were found": what the scan did read is above, under coverage.
Supply chain and provenance
Integrityfirst-seen
Surface digest59bb0ce680308e87
Scanned2026-10-15
This is the first scan of this surface here, so there is nothing yet to compare it against.
Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.
Release history
Always latest — the install command names no version, so it installs the newest release when it runs; the current release on file is 4.5.0. Pin a release below and the command asks for exactly that one.
4.5.0
undated
latestNo release note was published with this version.
—
no reviews yet
Who can review
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Sign in to write one
Any signed-in account that does not publish Zengram can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
No reviews yet
Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.