Finance Toolkit
500+ transparent financial methods calculated from raw statements, not third-party endpoints.
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.
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。
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。
选择你使用的 AI 应用,然后复制设置。
OSIRIS_HOME, OSIRIS_ROOTS, OSIRIS_CACHE_TTL_S, OSIRIS_PRUNE_THRESHOLD_TOKENS, OSIRIS_DISPATCH_ONLY你需要
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。
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。
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。
OSIRIS_HOME envOSIRIS_ROOTS envOSIRIS_CACHE_TTL_S envOSIRIS_PRUNE_THRESHOLD_TOKENS envOSIRIS_DISPATCH_ONLY env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。
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
注册名称已通过 GitHub 账户 MlkBlk-IT 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
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。
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。
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)。
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。
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。
同一分类中有:Finance Toolkit, SolidInvoice, Korea Tax Calculator
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
500+ transparent financial methods calculated from raw statements, not third-party endpoints.
Create and send invoices and quotes, manage clients, and track payments in SolidInvoice.
Korean tax calculation and tax-audit judgment MCP: 96 tools with statutory citations.
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。
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.
| Change | What it means |
|---|---|
| P0–P5 landed | Adaptive thresholds, conditional schema, token calibration, reference pruning, loop credit and the net-gain module. |
| Dashboard rebuilt | Liquid UI, live updates, plus concurrency and ledger tests. |
| Stale task pointer cleared | get_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 GitHub | Validated 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.
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 want | What a standard MCP server can actually do |
|---|---|
| Write directly into the prompt | Produce canonical state and have the agent carry it |
| Erase past conversation turns | Decide what to externalize for future turns |
| Hide another MCP server's tools | Recommend via Tool Compass |
| Hard-cap prompt tokens | Block an action (invariants, budget enforcement) |
The promise is honest: OSIRIS provides stable state and an enforceable safety contract, not a client prompt builder.
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.
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
| Change | Verdict | Cache Cost |
|---|---|---|
head_hash | CRITICAL DRIFT | Full re-prefill |
zoneB_hash | Minor drift | Re-prefill delta only |
tail_hash (append) | Normal growth | Re-prefill delta only |
| Pinned item rewritten | CRITICAL DRIFT (semantic) | Cache intact, task state corrupted |
Appending a constraint or decision changes
tail_hashbut notprefix_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).
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.
| Tool | Role | Typical Response |
|---|---|---|
osiris.plan | Start / update / resume / status of a task; produces sealed manifest | ~160 tok |
osiris.precheck | Authorize or deny an action (invariants, budget, deduplication) | ~70 tok |
osiris.observe | Log actions, loop detection, offload large data, update memory | ~50 tok |
osiris.recall | Rehydrate refs (server-side filtered: grep, range, outline) | variable |
osiris.checkpoint | Snapshot, resume, completion report, reversible compaction | ~80 tok |
osiris.handoff | Executable 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.
| Call | Behavior | ||||||
|---|---|---|---|---|---|---|---|
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_alternative | Active collision resolution for duplicate operations |
osiris://task/{task_id}/manifest — Active zones, drift status, objectiveosiris://task/{task_id}/status — Real-time context pressureosiris://ref/{ref} — Raw byte content of any referenced payloadosiris/task-resume — Clean agent recovery template after crashes┌──────────────────────────────────────────────────────────────┐
│ 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/
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.
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)
| Mode | Trigger | Evicts | Cache Impact | Use Case |
|---|---|---|---|---|
cache_ttl (default) | Δt > TTL | C/D, and A/B only if cache expired | Neutral | Recommended default |
conservative | Very high threshold | Working zone (C/D) only | Zero | High-cost API models |
balanced | Moderate threshold | C/D + cold historical turns | Zero | Standard agent workflows |
aggressive | Low threshold | Everything rehydratable | Minor prefill risk | Fast local LLMs |
fingerprint = sha256(tool ‖ canonical_args ‖ result_signature)
| Detection | Trigger Signal | Verdict |
|---|---|---|
| Exact duplicate | Identical fingerprint | suggest_alternative |
| Repeated failure | Same error signature (≥3 times) | block + required_action |
| Loop pattern | Same error across varying args | warn |
| Oscillation | Alternating A/B/A/B pattern | warn |
| Runaway | N calls with zero state progress | warn / block |
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"
OSIRIS guarantees full auditability for offloaded data:
mem://sha256: . Reads re-verify data integrity against the hash before returning. 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": []}
Real measurements from python bench\run_bench.py running an automated coding workflow:
| Payload Size | Baseline Context | Fixed OSIRIS | Adaptive OSIRIS | Net Improvement | OSIRIS Mode | Overhead |
|---|---|---|---|---|---|---|
| 200 tok | 1,583 | 2,669 | 1,649 | −4.17% | bystander | 33 tok |
| 800 tok | 3,851 | 3,751 | 3,255 | +15.48% | light | 1,808 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