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Osiris

作者:MlkBlk-IT版本 0.1.0i★ –i在你的电脑上运行
注册名称io.github.MlkBlk-IT/osiris

安装

概览

作者提供的说明(英文原文)

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 会计、财务与税务

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: OSIRIS_HOME, OSIRIS_ROOTS, OSIRIS_CACHE_TTL_S, OSIRIS_PRUNE_THRESHOLD_TOKENS, OSIRIS_DISPATCH_ONLY

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 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"
      }
    }
  }
}

在终端中运行这条命令

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

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.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"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "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"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • 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).

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 MlkBlk-IT 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“会计、财务与税务”中的其他服务器比较i

  • 星标数多于此分类中其他 1722 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 1722 个服务器的 86% 更新
  • 基础检查结果等于或优于此分类中其他 1722 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Osiris 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.1.0)于5天前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它?

同一分类中有:Finance Toolkit, SolidInvoice, Korea Tax Calculator

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Finance Toolkit

作者:JerBouma

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

电脑或云端★ 5,496MIT昨天更新
安装

SolidInvoice

作者:SolidInvoice

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

在云端运行★ 981MIT7天前更新
安装

Korea Tax Calculator

作者:UpstageAI

Korean tax calculation and tax-audit judgment MCP: 96 tools with statutory citations.

在你的电脑上运行★ 97MIT今天更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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

只显示了开头部分;其余内容请在仓库中阅读

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