Finance Toolkit
500+ transparent financial methods calculated from raw statements, not third-party endpoints.
Description from the maker (original English)
Cache-aware context engine for AI agents: server-side payload filtering, honest context ledger.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
OSIRIS_HOME, OSIRIS_ROOTS, OSIRIS_CACHE_TTL_S, OSIRIS_PRUNE_THRESHOLD_TOKENS, OSIRIS_DISPATCH_ONLYYou need
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-mcpFill in the empty values before running it.
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.
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.
OSIRIS_HOME envOSIRIS_ROOTS envOSIRIS_CACHE_TTL_S envOSIRIS_PRUNE_THRESHOLD_TOKENS envOSIRIS_DISPATCH_ONLY envThese settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
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
The registry name was verified with the GitHub account MlkBlk-IT, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
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.
The registry does not list a required key, though some servers ask you to sign in on first use.
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).
The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 5 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Finance Toolkit, SolidInvoice, Korea Tax Calculator
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
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.
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.
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
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