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Osiris

by MlkBlk-ITversion 0.1.0i★ –iRuns on your computer
Registry nameio.github.MlkBlk-IT/osiris

Install

Overview

Description from the maker (original English)

Cache-aware context engine for AI agents: server-side payload filtering, honest context ledger.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Accounting, finance & tax

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: OSIRIS_HOME, OSIRIS_ROOTS, OSIRIS_CACHE_TTL_S, OSIRIS_PRUNE_THRESHOLD_TOKENS, OSIRIS_DISPATCH_ONLY

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "osiris": {
      "command": "npx",
      "args": [
        "-y",
        "osiris-mcp"
      ],
      "env": {
        "OSIRIS_HOME": "your-value",
        "OSIRIS_ROOTS": "your-value",
        "OSIRIS_CACHE_TTL_S": "your-value",
        "OSIRIS_PRUNE_THRESHOLD_TOKENS": "your-value",
        "OSIRIS_DISPATCH_ONLY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add osiris --env OSIRIS_HOME=your-value --env OSIRIS_ROOTS=your-value --env OSIRIS_CACHE_TTL_S=your-value --env OSIRIS_PRUNE_THRESHOLD_TOKENS=your-value --env OSIRIS_DISPATCH_ONLY=your-value -- npx -y osiris-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "osiris": {
      "command": "npx",
      "args": [
        "-y",
        "osiris-mcp"
      ],
      "env": {
        "OSIRIS_HOME": "your-value",
        "OSIRIS_ROOTS": "your-value",
        "OSIRIS_CACHE_TTL_S": "your-value",
        "OSIRIS_PRUNE_THRESHOLD_TOKENS": "your-value",
        "OSIRIS_DISPATCH_ONLY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "osiris": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "osiris-mcp"
      ],
      "env": {
        "OSIRIS_HOME": "your-value",
        "OSIRIS_ROOTS": "your-value",
        "OSIRIS_CACHE_TTL_S": "your-value",
        "OSIRIS_PRUNE_THRESHOLD_TOKENS": "your-value",
        "OSIRIS_DISPATCH_ONLY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • OSIRIS_HOME env
    Repertoire d'etat OSIRIS : base SQLite, rapports, caches. Defaut ~/.osiris.
  • OSIRIS_ROOTS env
    Racines autorisees pour la lecture de fichiers, separees par ';'. Vide = aucune lecture de fichier.
  • OSIRIS_CACHE_TTL_S env
    TTL du cache en secondes.
  • OSIRIS_PRUNE_THRESHOLD_TOKENS env
    Seuil de prune en tokens.
  • OSIRIS_DISPATCH_ONLY env
    1 = n'expose que l'outil osiris.call (table d'outils stable pour le prefix cache).

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account MlkBlk-IT, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Accounting, finance & taxi

  • More stars than 0% of the other 1721 servers in this category
  • Published to the registry more recently than 86% of the other 1721 servers in this category
  • Basic checks equal to or better than 100% of the other 1721 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Osiris safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 5 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Finance Toolkit, SolidInvoice, Korea Tax Calculator

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Finance Toolkit

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500+ transparent financial methods calculated from raw statements, not third-party endpoints.

Computer or cloud★ 5,496MITUpdated yesterday
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SolidInvoice

by SolidInvoice

Create and send invoices and quotes, manage clients, and track payments in SolidInvoice.

Runs in the cloud★ 981MITUpdated 7 days ago
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Korean tax calculation and tax-audit judgment MCP: 96 tools with statutory citations.

Runs on your computer★ 97MITUpdated today
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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

OSIRIS

Cache-aware context engine & state governor for AI agents. Standard MCP server, local-first, independent of any model provider.

OSIRIS makes AI agents cheaper, faster, and more reliable: fewer tokens, fewer loops, zero unnecessary tools, and zero loss of context.

What's new — 2026-11-04

ChangeWhat it means
P0–P5 landedAdaptive thresholds, conditional schema, token calibration, reference pruning, loop credit and the net-gain module.
Dashboard rebuiltLiquid UI, live updates, plus concurrency and ledger tests.
Stale task pointer clearedget_task now drops the current-task pointer when the task no longer exists.
4 bugs fixed (2026-11-04)LoopGuard escalates (warn 3 / block 5 + precheck deny), structured plan constraints enforced, handoff ref leak removed, honest unverified integrity.
v0.1.1 released on GitHubValidated 2026-11-03; tag and release v0.1.1 are published. PyPI and npm still serve 0.1.0 until the packages are uploaded.

OSIRIS does not replace your other MCP servers (filesystem, Git, GitHub, shell, browser…). It coexists alongside them and drastically cuts what they cost in prompt context.


1. The Core Architectural Constraint

An MCP server never sees the final prompt assembled by the client host. It only sees what the agent sends to it. Therefore, OSIRIS cannot:

What we would wantWhat a standard MCP server can actually do
Write directly into the promptProduce canonical state and have the agent carry it
Erase past conversation turnsDecide what to externalize for future turns
Hide another MCP server's toolsRecommend via Tool Compass
Hard-cap prompt tokensBlock an action (invariants, budget enforcement)

The promise is honest: OSIRIS provides stable state and an enforceable safety contract, not a client prompt builder.


2. Sealed Manifest — The Core Design

osiris.plan returns a deterministic block (~120 tokens) that the agent carries at a fixed position in its context window:

OSIRIS/1 task=7c3f obj="fix PowerShell profile bug" plan=3/7

inv=4 files=6 refs=9 dec=2 budget=ok SEAL=sha256:3f9a1c…c21b

The SEAL is a SHA256 digest of stable zones. The agent passes it on every turn to osiris.observe: OSIRIS recomputes and detects any state drift immediately.

This is OSIRIS's passive transport: even if the agent stops calling tools, it carries the essential state cleanly.

Three Hashes Because Prompt Cache is Prefix-Based
head_hash   = sha256(system_head ‖ rules)                       immutable

zoneB_hash = sha256(objective ‖ global_plan ‖ tool_contract) quasi-stable tail_hash = sha256(constraints ‖ pinned_decisions) append-only prefix_hash = sha256(head_hash ‖ zoneB_hash) ← cached prefix zoneA_hash = sha256(head_hash ‖ tail_hash) ← full ZONE A

ChangeVerdictCache Cost
head_hashCRITICAL DRIFTFull re-prefill
zoneB_hashMinor driftRe-prefill delta only
tail_hash (append)Normal growthRe-prefill delta only
Pinned item rewrittenCRITICAL DRIFT (semantic)Cache intact, task state corrupted

Appending a constraint or decision changes tail_hash but not prefix_hash: the prompt cache holds, only the new tokens are prefilled.

Rewriting a pinned decision is explicitly caught: OSIRIS checks collection identity, flagging a semantic break (semantic_break: true, cause: pinned_rewrite).


3. The 6 MCP Tools

Access footprint: 1,019 tokens of schemas and descriptions in model context (measured via bench/run_bench.py). Paid on task start, amortized across the session — pays off on the very first turn with ≥800 token payloads.

ToolRoleTypical Response
osiris.planStart / update / resume / status of a task; produces sealed manifest~160 tok
osiris.precheckAuthorize or deny an action (invariants, budget, deduplication)~70 tok
osiris.observeLog actions, loop detection, offload large data, update memory~50 tok
osiris.recallRehydrate refs (server-side filtered: grep, range, outline)variable
osiris.checkpointSnapshot, resume, completion report, reversible compaction~80 tok
osiris.handoffExecutable delegation contracts and structured results~200 tok

osiris.observe combines logging, loop protection, payload externalization, memory registry, and automated pruning in a single call so the agent always reports back seamlessly.

Extended Operations
CallBehavior
osiris.observe(kind="dropped", result= )Drop receipt: agent confirms payload was evicted from prompt → token savings are proven and credited
`osiris.observe(kind="head"\"contract", result=…)`Records system prompt head / client contract and detects cache-busting volatile tokens (timestamps, UUIDs)
`osiris.recall(op="grep"\"range"\"head"\"tail"\"outline"\"aggregate"\"jsonl")`Server-side filtering: only requested matches enter the context window
osiris.plan(mode="status")Context pressure breakdown (measured vs declared vs unknown)
`osiris.checkpoint(mode="compact"\"trim"\"clear")`Reversible session compaction preparation
osiris.precheck → suggest_alternativeActive collision resolution for duplicate operations
Resources & Prompts
  • osiris://task/{task_id}/manifest — Active zones, drift status, objective
  • osiris://task/{task_id}/status — Real-time context pressure
  • osiris://ref/{ref} — Raw byte content of any referenced payload
  • Prompt osiris/task-resume — Clean agent recovery template after crashes

4. Architecture

┌──────────────────────────────────────────────────────────────┐

│ Agent (Claude Code, Cursor, MiniMax Code, Codex, Cline…) │ │ Context: system │ tools │ OSIRIS MANIFEST │ history │ └───────────────────────────┬──────────────────────────────────┘ │ MCP stdio — 6 tools, ~1,019 tok schema ▼ ┌──────────────────────────────────────────────────────────────┐ │ osiris-mcp (server.py) │ │ plan · precheck · observe · recall · checkpoint · handoff │ └───────────────────────────┬──────────────────────────────────┘ │ ┌───────────────────────────▼──────────────────────────────────┐ │ core/ │ │ task_state Typed TaskState (objective, plan, decisions) │ │ memory Typed memory registry by kind and scope │ │ refs Content-addressed store (verified on read) │ │ prefix Zones, 3-hash system, drift, PrefillPlan │ │ prune Reversible externalization, 4 modes │ │ query Server-side payload filtering (grep, range...) │ │ compaction trim / compact / clear (lossless) │ │ pressure Context pressure metrics & tracking │ │ loopguard Fingerprinting, 5 loop patterns detection │ │ invariants TOML rule engine (guard rails) │ │ compass Tool Compass (dynamic relevance scoring) │ │ handoff Executable agent delegation contracts │ │ events Append-only event journal + checkpoints │ │ ledger Tokens / cost / savings / OSIRIS overhead │ │ fileops Sandboxed file inspection (roots, size limits) │ │ redact Secret scrubbing before any hashing │ │ policy Budget policies & token enforcement │ └───────────────────────────┬──────────────────────────────────┘ ▼ ~/.osiris/osiris.db + ~/.osiris/reports/


5. Memory Model

Deterministic, typed, content-addressable memory — not an opaque vector database.

TaskState

├── objective immutable ├── constraints[] append-only ├── decisions[] append-only, pinned verbatim ├── plan completed / active / remaining steps ├── files{} path, SHA-256 hash, mtime, last access, ref ├── tool_results{} cache_key, ref, size, token cost, TTL ├── failures{} error signature, tool, args hash, attempts, lesson ├── lessons[] general behavioral rules learned ├── handoffs{} cross-agent delegation contracts └── budget estimated vs consumed tokens and cost

Scopes: task → project → agent → global.


6. Cache-Aware Pruning

Security Invariant: Pruning moves payload data to content-addressed refs (`mem://sha256:…`). It NEVER deletes.

ZONE_A_pruning_cost = pruned_tokens

ZONE_C_or_D_pruning_cost = 0 ← always zero cost net_savings = pruned_tokens − (prefix_tokens × P(cache_dead) × prefix_pruned)

ModeTriggerEvictsCache ImpactUse Case
cache_ttl (default)Δt > TTLC/D, and A/B only if cache expiredNeutralRecommended default
conservativeVery high thresholdWorking zone (C/D) onlyZeroHigh-cost API models
balancedModerate thresholdC/D + cold historical turnsZeroStandard agent workflows
aggressiveLow thresholdEverything rehydratableMinor prefill riskFast local LLMs

7. LoopGuard Protection

fingerprint = sha256(tool ‖ canonical_args ‖ result_signature)
DetectionTrigger SignalVerdict
Exact duplicateIdentical fingerprintsuggest_alternative
Repeated failureSame error signature (≥3 times)block + required_action
Loop patternSame error across varying argswarn
OscillationAlternating A/B/A/B patternwarn
RunawayN calls with zero state progresswarn / block

8. Invariants (Guard Rails)

Define project safety rules in .osiris/invariants.toml:

[[invariant]]

id = "tests-before-commit" when = { tool = "git.commit" } require = { state = "tests_passed" } message = "Cannot commit while tests are failing"

[[invariant]] id = "no-test-edit" when = { path_glob = "tests/**" } verdict = "block" message = "Modifying test suites requires explicit approval"


9. Lossless Verification & Non-Loss Proof

OSIRIS guarantees full auditability for offloaded data:

  1. Self-verifying refs: mem://sha256: . Reads re-verify data integrity against the hash before returning.
  2. Zero deletion paths: data is relocated to the local store, never pruned permanently.
  3. Reproducible audit:
   python -m osiris audit --task <task_id>

Outputs cryptographic proof of non-loss:

   {"lossless": true, "items_externalized": 14, "items_verified": 14, "items_missing": 0, "integrity_violations": []}

10. Verified Benchmarks

Real measurements from python bench\run_bench.py running an automated coding workflow:

Payload SizeBaseline ContextFixed OSIRISAdaptive OSIRISNet ImprovementOSIRIS ModeOverhead
200 tok1,5832,6691,649−4.17%bystander33 tok
800 tok3,8513,7513,255+15.48%light1,808 tok

| 2,000 tok | 9,015 | 5,215

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