RCLL
NPM · FLEET-MEMORY-MCP
Self-hosted shared memory for a team of AI agents. Rooms, L0-L3 depth, no LLM on the read path.
Available components
Last scanned (6 hours ago)
Scores higher than 93% of npm servers
Category Memory and Knowledge
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 Security98
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- No install/post-install scripts declared.Pass
- 30 of 92 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency97
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to Holetron-lab/fleet-memory). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 69 days ago).Pass
- Security-disclosure policy not yet verified: we couldn't inspect the source repository.Unverified
Schema Quality & AI Usability44
- 0% of prompts and resources have a non-trivial description (not blank, and not just the item's name).Fail
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 702 tokens (~117/item across 6 items; 5 tools + 1 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 Management100
- No destabilizing schema changes in the last 30 days.Pass
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 5 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 6 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Checks we ran (15)
- Malware scan
- Known CVEs
- Install scripts
- Dependency health
- Source repository
- Provenance
- License
- Maintenance
- Schema quality
- Stability
- Tool coverage
- Injection markers
- Destructive annotations
- Judged manipulation
- MCP protocol
Checks we could not complete (1)
These are gaps on our side, not findings about this package. We only credit what we can confirm, so a category made up entirely of them scores 0.
- Security disclosure in Provenance & TransparencySecurity-disclosure policy not yet verified: we couldn't inspect the source repository.
Does not apply to this package (2)
- Endpoint SecurityScored on hosted endpoints only. A package has no live endpoint of its own for us to probe.
- Transport & ReachabilityScored on hosted endpoints only. A package has no live endpoint of its own for us to probe.
The score does not measure everything. What the score does not measure →
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 0
- Security disclosure: pass → unverified ▼ functional
- 3 Nov 26 0
- Security disclosure: unverified → pass ▲ functional
- 2 Nov 26 0
- Security disclosure: pass → unverified ▼ functional
- 1 Nov 26 0
- Stability: 0.97 → pass security
- 31 Oct 26 0
- Stability: pass → 0.97 functional
- 25 Oct 26 0
- Stability: 0.97 → pass security
- 24 Oct 26 0
- Stability: pass → 0.97 functional
- 18 Oct 26 0
- Stability: 0.97 → pass security
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 6 Nov 2026 · Analysed npm/fleet-memory-mcp@0.1.0
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 | npm |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | Holetron-lab/fleet-memory |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/Holetron-lab/fleet-memory/.github/workflows/publish.yml@refs/tags/rcll-v0.1.0 |
| Rekor log index | 2625770505 |
| Predicate type | SLSA build provenance https://slsa.dev/provenance/v1 |
| Subject digest | sha512:30297e25dfb406de150dd4eaa9d524fe569089d6bdb87afcf386ca13d86bc72bbfe32a71420f197f34c41179c7ed42d4eb6962a7255882c43337bbe7e |
Background: How many MCP packages publish verified provenance →
Dependencies 92 packages
| Packages resolved | 92 |
|---|---|
| Stale | 30 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
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 →
memory_bridge ~130
Create a cross-bank memory bridge (tunnel) between two related memories in different banks. Relations: same_concept, depends_on, contradicts, extends. Use when concepts in separate banks are related.
| Name | Type | Req | Description |
|---|---|---|---|
| confidence | number | – | Confidence score 0.0–1.0 (default: 0.8) |
| relation | string | yes | Relationship type |
| source_bank | string | yes | Source bank ID |
| source_memory | string | yes | UUID of the source memory |
| target_bank | string | yes | Target bank ID |
| target_memory | string | yes | UUID of the target memory |
No output schema declared.
No examples provided.
memory_compress ~132
Create compressed memory summaries (closets) from stored facts. Groups memories by room+hall and creates AI-generated summaries with source pointers. Use when a topic has accumulated many facts.
| Name | Type | Req | Description |
|---|---|---|---|
| bank_id | string | – | Memory bank ID (default: fleet-main) |
| hall | string | – | Knowledge type to compress (e.g. "fact", "decision") |
| min_sources | number | – | Min memories needed to create a closet (default: 5) |
| query | string | – | Query to guide compression focus |
| room | string | – | Topic to compress (e.g. "auth", "pipeline") |
No output schema declared.
No examples provided.
memory_recall ~138
Search long-term memory for relevant facts. Uses semantic search with optional room/hall scoping for significantly improved retrieval accuracy. Supports layer cascade (L0 results always prioritized).
| Name | Type | Req | Description |
|---|---|---|---|
| bank_id | string | – | Memory bank ID (default: fleet-main) |
| hall | – | – | Filter by hall(s): fact, event, decision, etc. |
| limit | number | – | Max results (default: 10) |
| max_layer | string | – | Max layer depth to search (default: L3 = all) |
| query | string | yes | What to search for in memory |
| room | – | – | Filter by room(s) — applied before semantic search |
No output schema declared.
No examples provided.
memory_reflect ~78
Deep reasoning over memory — synthesizes facts, finds patterns, answers complex questions with citations. Use for analysis: "What patterns emerge from recent events?" or "Summarize everything about X."
| Name | Type | Req | Description |
|---|---|---|---|
| bank_id | string | – | Memory bank ID (default: fleet-main) |
| query | string | yes | Question to reason about over stored memories |
No output schema declared.
No examples provided.
memory_retain ~219
Save a fact, observation, or document to long-term memory with automatic room/hall classification. Rooms: auth, pipeline, schema, infrastructure, ui, api, deployment, monitoring, agent, general. Halls: fact, event, decision, preference, discovery, procedure, warning. Layers: L0=Identity (always loaded), L1=Critical, L2=Session (default), L3=Deep.
| Name | Type | Req | Description |
|---|---|---|---|
| bank_id | string | – | Memory bank ID (default: fleet-main) |
| context | string | – | Context label (e.g. "meeting notes", "client call") |
| document_id | string | – | Document ID to group related facts |
| hall | string | – | Knowledge type (auto-classified if omitted) |
| layer | string | – | Priority layer (default: L2) |
| room | string | – | Topic room (auto-classified if omitted) |
| tags | array | – | Tags for categorization |
| text | string | yes | The text to memorize — a fact, observation, or document content |
No output schema declared.
No examples provided.
How do I install the RCLL MCP server?
RCLL runs locally as an npm package, launched with npx -y fleet-memory-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · fleet-memory-mcp
claude mcp add ai-rcll-fleet-memory -- npx -y fleet-memory-mcp
What is the RCLL MCP server?
RCLL is an MCP server listed in the public MCP registry as ai.rcll/fleet-memory. Self-hosted shared memory for a team of AI agents. Rooms, L0-L3 depth, no LLM on the read path. This page covers its npm package (fleet-memory-mcp).
Is the RCLL MCP server safe to use?
RCLL scores 89 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 6 November 2026. It declares no install or post-install scripts. 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 RCLL MCP server expose?
RCLL exposes 5 tools: memory_retain, memory_recall, memory_reflect, memory_compress, memory_bridge. Their descriptions and schemas cost roughly 697 tokens of context every time the server is loaded.
Is the RCLL MCP server still maintained?
RCLL is still listed as active in the MCP registry. We last reached this channel on 6 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 RCLL MCP server under?
RCLL declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.
What did VerifyMCP test on the RCLL MCP server?
VerifyMCP ran 15 checks on RCLL, covering Malware scan, Known CVEs, Install scripts, Dependency health and 11 more. One item could not be completed, a gap on our side rather than a finding about RCLL: Security disclosure. Endpoint Security and Transport & Reachability do not apply to an npm package, so they were not tested. The score does not measure everything, and the scoring documentation lists what it leaves out.