ToolBank

The discovery and routing layer that keeps MCP servers out of your context window until you actually need them.

---

The problem

The Model Context Protocol lets an LLM talk to any number of servers — GitHub, Slack, Postgres, your internal tools. The catch: most clients load every tool definition from every configured server at startup. Six servers can mean 70+ tool schemas and thousands of tokens spent before the conversation even starts, most of which the model never touches in a given session.

What ToolBank does

ToolBank sits in front of your MCP servers as a thin discovery layer. Instead of loading everything up front, the LLM asks for what it needs — by server or by tool — and ToolBank resolves the request and connects on demand. You keep your existing MCP servers unmodified; ToolBank only changes how (and when) their tools reach the model's context.

It works at the protocol level, so it doesn't care what's actually behind a server — GitHub, Slack, a database, or a fully custom MCP server you built yourself over a proprietary application (CAD software, a game engine editor, an internal build system). If it speaks MCP, ToolBank can discover it and route to it. The bigger and more varied your server catalog gets, the more it pays off — see At large scale in Benchmarks for real numbers at 300 tools.

Two modes cover the two ways teams actually want this to work:

	Discovery Mode	Dynamic Mode
Granularity	Whole server	Individual tool
Tools always in context	4 (mcpd_find, mcpd_list, mcpd_connect, mcpd_get_schema)	2 (find_tools, get_tool_schema)
Best for	"Connect me to GitHub" style workflows	Cherry-picking one tool from many servers
After resolution	LLM talks to the server directly — ToolBank exits the data path	ToolBank lazy-connects and stays in the loop per tool call

v1.0.0 adds Lazy Schema Loading: Discovery Mode can hand back a stub tool list (names only, no schemas) and fetch a single tool's full schema only when it's about to be called. Real, reproducible numbers (not rough estimates) are in Benchmarks below.

How it works

Discovery Mode — server-level selection

Step 1  LLM -> mcpd_find("github issues")
               ToolBank searches the registry
               returns: { id: "github", tools: ["create_issue", "search_repos", ...] }

Step 2  LLM -> mcpd_connect("github")
               ToolBank starts the GitHub MCP server
               returns: 20 tools now available as github__create_issue, etc.

Step 3  LLM -> github__create_issue({ title: "...", body: "..." })
               ToolBank proxies to the GitHub MCP server, returns the result

Real token counts for this flow are in Benchmarks — see "Discovery Mode (before connect)" and "(after connect)".

Lazy Schema Loading

No proxy, no changes to the target MCP server required:

1. mcpd_find("github") → choose a server
2. mcpd_connect("github", lazy_mode=true) → get a stub list (tool names only, no schemas)
3. mcpd_get_schema("github", "create_issue") → fetch one full schema
4. github__create_issue(...) → direct call, as always

Pass --sync-on-start so the registry has schemas cached ahead of time via toolbank-sync.

Dynamic Mode — tool-level selection

Step 1  LLM -> find_tools("create issue, post slack message")
               ToolBank searches the tool index across all servers
               returns: create_issue (github), post_message (slack)
               both tools added to tools/list

Step 2  LLM -> create_issue({ title: "Bug #42" })
               ToolBank lazy-connects to the GitHub MCP server
               executes create_issue, returns the result

Step 3  LLM -> post_message({ channel: "#eng", text: "Done" })
               ToolBank lazy-connects to the Slack MCP server
               executes post_message, returns the result

Real token counts for this flow are in Benchmarks — see "Dynamic Mode (before find)" and "(after find_tools)".

Installation

# Core — keyword search, stdio transport
pip install toolbank

# With HTTP and SSE transport (remote MCP servers)
pip install toolbank[http]

# With semantic search (sentence-transformers)
pip install toolbank[embeddings]

# With exact token counting for toolbank-benchmark (tiktoken)
pip install toolbank[benchmark]

# Full installation
pip install toolbank[all]

# Development
pip install toolbank[dev]

Quick start

1. Build your registry

The registry is a lightweight JSON catalog of your MCP servers and their tool summaries. Build it from your MCP client's config (e.g. Cursor: ~/.cursor/mcp.json, Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json):

toolbank-sync --config /path/to/your/mcp-config.json --output registry/mcpd-registry.json

2. Point your MCP client at ToolBank

Discovery Mode (4 tools, connect to one server at a time):

{
  "mcpServers": {
    "toolbank-server": {
      "command": "toolbank-server",
      "args": ["--registry", "/path/to/mcpd-registry.json", "--sync-on-start"]
    }
  }
}

Dynamic Mode (2 tools, cherry-pick tools across all servers):

{
  "mcpServers": {
    "toolbank-gateway": {
      "command": "toolbank-gateway",
      "args": ["--registry", "/path/to/mcpd-registry.json"]
    }
  }
}

Or invoke the Python module directly (avoids PATH issues):

{
  "mcpServers": {
    "toolbank-gateway": {
      "command": "python",
      "args": ["-m", "toolbank.dynamic.server", "--registry", "/path/to/mcpd-registry.json"]
    }
  }
}

3. Measure the token savings

toolbank-benchmark --registry registry/mcpd-registry.json

(Generate your registry first with toolbank-sync.) See Benchmarks for real, reproducible numbers and what they mean.

Architecture

┌─────────────────────────────────────────────────────────────┐
│                        LLM / AI Client                       │
└──────────────────────┬──────────────────────────────────────┘
                       │ MCP (stdio / JSON-RPC 2.0)
          ┌────────────┴─────────────┐
          │                          │
   ┌──────▼──────┐           ┌───────▼──────┐
   │  Discovery  │           │   Dynamic    │
   │    Mode     │           │    Mode      │
   │             │           │              │
   │ mcpd_find   │           │ find_tools   │
   │ mcpd_list   │           │              │
   │ mcpd_connect│           │ LazyPool     │
   │ mcpd_get_schema│        │              │
   └──────┬──────┘           └───────┬──────┘
          │                          │
          └────────────┬─────────────┘
                       │
          ┌────────────▼─────────────┐
          │        Shared Core        │
          │                           │
          │  Registry (mcpd-registry) │
          │  KeywordSearchEngine      │
          │  ToolSearchEngine         │
          │  HybridSearch (TF-IDF +   │
          │    sentence-transformers) │
          │  ToolBankConnector           │
          │   ├─ stdio transport      │
          │   ├─ streamable-http      │
          │   └─ SSE transport        │
          └───────────────────────────┘

Design principles

A discovery layer, not a permanent proxy. In Discovery Mode, once mcpd_connect resolves, the LLM gets direct tool access to the connected server. In Dynamic Mode, server connections stay lazy — a server process starts only when one of its tools is actually called.

Offline-first registry. Tool summaries (name, description, tags) are captured at sync time. Searches run against the cached registry with zero network traffic; full tool schemas load only on connection.

Search degrades gracefully. Keyword search (TF-IDF with synonyms) is the default — always available, no extra dependencies. Semantic search is optional (pip install toolbank[embeddings]): when installed, sentence-transformers embeddings blend with keyword results, which helps for loosely-phrased natural-language queries like "a tool for reading web pages" → Playwright. The keyword synonym table also understands multilingual input (e.g. Polish query terms resolve to the right English tool concepts). Keyword-first keeps installs frictionless when you don't need semantic search.

Benchmarks

Methodology. Every number below comes from toolbank-benchmark, which instantiates the real DiscoveryServer/DynamicServer classes and measures their actual tools/list JSON-RPC output — not hardcoded stand-ins that can drift out of sync with the real code. Tokens are counted with tiktoken's o200k_base encoding (the GPT-4o tokenizer) when installed; without it, the CLI clearly labels its output as a rougher char/4 estimate rather than presenting both with false equal precision. Reproduce any number here yourself:

pip install toolbank[benchmark]
toolbank-benchmark --registry registry/benchmark-registry.json --query "create github issue" --quality

At realistic scale

6 servers, 61 tools, every tool has a complete real schema — registry/benchmark-registry.json, not a partial catalog:

Scenario	Tools	Tokens	vs. direct load
Direct (all servers, no ToolBank)	61	3,786	—
Discovery Mode (before connect)	4	480	87% fewer
Discovery Mode (after connect: github)	18	955	75% fewer
Dynamic Mode (before find)	2	201	95% fewer
Dynamic Mode (after find_tools("create github issue"))	7	390	90% fewer

recall@k on this registry: 100% (8/8) (quality.py verifies it). Read that as what it is — eight queries against sixty-one tools. It does not generalise to a large registry, and On a real registry below shows what happens when you try.

At large scale — where ToolBank really pays off

This is the case ToolBank is actually built for: an organization with a large, growing catalog of MCP servers — internal tools, SaaS integrations, custom in-house APIs — where any one session only ever touches a handful. The meta-tool interface (mcpd_find/mcpd_list/mcpd_connect/mcpd_get_schema, or find_tools/get_tool_schema) costs a fixed number of tokens no matter how big the registry gets, while a direct load grows linearly with every tool you add. The gap only widens as you scale up. Measured on a fully-specified synthetic registry of 20 servers / 300 tools (registry/benchmark-registry-large.json):

Scenario	Tools	Tokens	vs. direct load
Direct (all servers, no ToolBank)	300	20,757	—
Discovery Mode (before connect)	4	480	98% fewer
Discovery Mode (after connect: one server)	19	997	95% fewer
Dynamic Mode (before find)	2	201	99% fewer
Dynamic Mode (after find_tools(...))	7	394	98% fewer

The static meta-tool cost (480 / 201 tokens) is identical to the 61-tool benchmark above — that's the whole mechanism. Add a 21st server, a 500th tool, it doesn't move. Only the "Direct" column keeps growing. This is the regime — many configured MCP servers, a handful used per session — where ToolBank is the strongest option in this document.

Small, fixed setups: skip the discovery layer

The same benchmark against a deliberately tiny registry (3 servers, 7 tools — the fixture in tests/conftest.py) shows the other end of the curve, and we're showing it because it's true, not because it's flattering:

Scenario	Tools	Tokens	vs. direct load
Direct (all servers, no ToolBank)	7	252	—
Discovery Mode (before connect)	4	480	90% more
Discovery Mode (after connect)	7	576	129% more
Dynamic Mode (before find)	2	201	20% fewer
Dynamic Mode (after find_tools)	3	288	14% more

Rule of thumb: if you have a small, fixed set of 2-3 MCP servers you always use, configure them directly — a discovery layer (this one or any competitor's) adds overhead you don't need. ToolBank's value curve turns sharply positive once your registry grows past a handful of servers, and keeps improving from there — see the 300-tool numbers above.

On a real registry: 448 tools somebody actually wrote

Every registry above is either small or synthetic. Synthetic is fine for measuring the mechanism — the benchmark runs the real DiscoveryServer and DynamicServer and counts their real output — but a reader is entitled to ask whether it holds up on tools a person wrote, with descriptions a person phrased.

So here is one. ToolBank-AutoCAD is a catalogue of MCP servers over AutoCAD: 38 categories, 448 tools, every description and search phrasing written by hand rather than templated. It is exactly the case named at the end of this section — a proprietary desktop application wrapped in MCP servers — and it is large enough that loading it directly is not a thing anyone would do.

That repository is not public yet, so this section does not ask you to take its existence on trust. The two registries derived from it are committed here — benchmark-registry-autocad.json and benchmark-registry-autocad-no-intent.json, 448 tools each, differing only in whether per-tool search phrasings survived the export. Every number in this section and the next was produced from those two files, and you can reproduce all of them from a clone of this repository alone. The AutoCAD source is where they came from, not something you need in order to check them.

Run the benchmark against the committed registry:

toolbank-benchmark --registry registry/benchmark-registry-autocad.json

To regenerate it from the source manifests, if you have them:

python scripts/build-registry-from-manifests.py \
    --manifests ../autocad-mcp/toolbank-manifests \
    --out registry/benchmark-registry-autocad.json

Scenario	Tools	Tokens	vs. direct load
Direct (all servers, no ToolBank)	448	34,965	—
Discovery Mode (before connect)	4	480	99% fewer
Discovery Mode (after connect: one category)	16	1,241	96% fewer
Dynamic Mode (before find)	2	201	99% fewer
Dynamic Mode (after find_tools(...))	7	576	98% fewer

Same fixed meta-tool cost — 480 and 201 tokens, identical to the 61-tool and 300-tool tables. That is the mechanism working exactly as advertised.

The half that token counts do not measure

Saving 99% of the tokens and then handing the model the wrong tool is not a win. It is a regression with a good-looking chart.

So the accuracy half is measured too, on the same 448-tool registry, with scripts/routing-quality.py: sixteen plain-language requests of the kind a person actually types — half of them in Polish, because that registry's phrasings are bilingual — each paired with the tool that should answer it.

python scripts/routing-quality.py --registry registry/benchmark-registry-autocad.json

The first run was not flattering, and fixing it took two independent changes:

Registry	keyword only	+ fusion & multilingual embeddings
This repo's own benchmark registry (61 tools)	37%	62%
ToolBank-AutoCAD, without per-tool phrasings (448 tools)	31%	37%
ToolBank-AutoCAD, as its manifests ship today	50%	75%

Top-3, sixteen plain-language requests, half of them Polish. Top-5 on the last row is 87%.

What the registry contributes. The AutoCAD side requires a plain-language Intent list on every one of its 448 tools — 2,387 phrasings, bilingual, enforced by its own build — and its manifest generator was pouring them into a single category-level bag and writing them nowhere else. Each tools_summary entry carried a name, a description and tags, so a discovery layer could tell that a request was about styles but had nothing to rank create_dimstyle above its twenty siblings with. That single omission is the 37% → 75% row.

The two AutoCAD rows are the same 448 tools, built by the same script from the same manifests — benchmark-registry-autocad.json and benchmark-registry-autocad-no-intent.json, the latter produced with --no-intent. They differ in nothing but those phrasings, which is what makes the gap evidence rather than anecdote. A discovery layer can only rank what the registry tells it; if your tool descriptions are generated from function signatures, expect the left-hand column.

Phrasings have to stay phrases. Folding them in as word-split tags scored 68% where keeping them whole scored 75%. The hybrid engine embeds name + description + tags as one string and a sentence-transformer compares sentences: ile ma metrow kwadratowych to pomieszczenie embeds close to a user asking exactly that, while the bag {metrow, kwadratowych, pomieszczenie} does not. Seven points, thrown away by tokenising too early.

What the ranker measures, and what it does not

Everything above is recall: did the expected tool appear in the list at all. That is the right thing to gate in CI, because it is a ceiling — no model can call a tool it was never handed. It is not what a user experiences. An agent does not "get it right if the answer was in the top 3"; it picks one tool, and if that turns out wrong, it searches again.

So the end-to-end path is measured too, with scripts/rerank-quality.py: the candidates find_tools really returned go to a frontier model, which picks one — or answers "none of these fit", writes its own new query, and searches again. The model is never shown the expected answer and is never told whether it was right; the second round is entered only when the model itself says nothing fits. Numbers below are gpt-5.6-luna, k=10.

Registry	ranker's own #1	one search	+ second search	recall@10
This repo's benchmark registry	25%	87%	87%	87%
ToolBank-AutoCAD, without per-tool phrasings	25%	43%	56%	50%
ToolBank-AutoCAD, as its manifests ship today	31%	75%	81%	93%

Three things this says that the recall table cannot.

The ranker's own top choice is a bad description of the system. 31% against 87% is the difference between reading the first line of the result list and reading the result list.

On two of the three registries the model lands exactly on recall@10. It extracted every tool that was there to extract. Whatever is missing at that point is missing from the catalogue, not from the reasoning — which is the registry-quality argument again, arrived at from the other direction.

Searching twice is cheap, and the model knows when to do it. On ile ma metrow kwadratowych to pomieszczenie the ranker never surfaced get_room_data; the model said none of these fit, re-queried itself with oblicz powierzchnię pomieszczenia w m², and found it. That recovery costs one more round-trip:

On the 448-tool registry	tokens	vs. loading everything
Load the whole catalogue (a plain aggregator)	51,045	—
ToolBank, before any search	237	99.5% less
ToolBank, after one search	1,526	97% less
ToolBank, after a second search	3,158	94% less

The extra search costs 1,632 tokens — 3.2% of what loading the catalogue costs once. An agent could search thirty times over and still come out ahead. A miss on the first query is a detour, not a failure, and that is the property worth designing for: it is why "did the ranker put it first" is the wrong question and "can the agent get there at all, cheaply" is the right one.

What the search contributes. Two things were wrong here and both are fixed:

• Semantic search could not recall anything. Keyword ran first, an empty keyword result returned immediately, and the semantic engine's output was then filtered down to what keyword had already found — so embeddings could only ever reorder a lexical result set. Now both engines run independently and their ranks are combined with reciprocal rank fusion, so a tool only the semantic engine found can surface.
• The default embedding model was all-MiniLM-L6-v2, which is English-only. On a registry described in another language that is not a weak signal, it is no signal. The default is now paraphrase-multilingual-MiniLM-L12-v2.

One property was deliberately preserved: keyword still decides whether anything matches at all. Embedding search has no concept of "no match" — it returns nearest neighbours for any input, and the hybrid engine normalises scores so the first of them reads 1.0 even for xyznonexistent999. Fusing that unfiltered destroys the empty result, which is a worse failure than a miss: a model handed five irrelevant tools will call one, whereas an empty result is information it can act on. So a query matching no vocabulary returns nothing; everything else gets the full fused ranking.

Semantic search is an optional extra. pip install toolbank gives you the keyword column. The right-hand column needs pip install toolbank[embeddings], which pulls in sentence-transformers and torch. The numbers above say which is which because the difference is 20+ points and a reader should not have to guess which install they are reading about.

This is why scripts/routing-quality.py is in the repository and not in a blog post: point it at your registry before you trust any percentage on this page, including the good ones.

Versus other tools on the market

One real side-by-side, run ourselves: NCP Orchestrator v2.3.1, installed fresh (npx -y @portel/ncp@latest), zero backend servers configured — its own static meta-tool interface, tokenized the exact same way:

Tool	Meta-tools exposed	Tokens
NCP v2.3.1 (find + code)	2	903
ToolBank Discovery Mode (before connect)	4	480
ToolBank Dynamic Mode (before find)	2	201

Measured 2026-07-29, tiktoken o200k_base. This is the one comparison in this section we actually ran ourselves — same tokenizer, same "before connecting anything" scenario, reproducible by anyone with Node.js installed.

For everyone else below, we're citing published numbers, not our own measurements — different registries, different tokenizers, different baselines. Treat these as directional, not as line-by-line comparable to the numbers above:

Tool	Claimed reduction	Source
Anthropic native Tool Search (Claude Code)	~85–96% (reported 134k→5k tokens internally)	community writeup
Speakeasy Dynamic Toolsets	~99% ("100x")	speakeasy.com
NCP Orchestrator (vendor-claimed)	83–97%, varies by source	arul.sg/ncp, mcp.directory

The most important line in this table isn't a percentage: Anthropic shipped this exact pattern natively into Claude Code. If you're specifically on Claude Code, check whether you need any third-party discovery layer — this one included — before reaching for one.

Verdict

ToolBank's savings scale with the size of your MCP ecosystem: at 300 tools across 20 servers, the measured numbers above hit 98-99%, and that curve keeps climbing the more servers you add — the meta-tool cost never grows. That's the regime this is built for: large, growing MCP deployments where dozens of servers are configured and only a handful get used per session. Against a live-tested competitor (NCP Orchestrator) it wins outright at the same task; against vendor-published numbers from Anthropic and Speakeasy it's in the same range, without an apples-to-apples test to say more than that.

One condition on all of it. The savings are a property of the mechanism and hold whatever your registry contains. The routing accuracy is not — it is a property of your registry. The same 448 tools, the same requests, the same frontier model end to end: 56% or 81%, depending on nothing but whether the catalogue carried the phrasings people search with. Run scripts/routing-quality.py and scripts/rerank-quality.py against your own registry before adopting this or any competitor: a router cannot rank what the catalogue does not say, and no model can call a tool it was never handed.

Best for: teams with many MCP servers — SaaS integrations, internal tools, and fully custom MCP servers you build yourself. Because ToolBank works at the protocol level, it doesn't care what's behind a server: if you wrap a proprietary application in an MCP server (CAD tools, a game engine editor, an internal build system — anything you can script), ToolBank discovers and routes to it exactly like it does GitHub or Slack. The bigger and more varied that catalog gets, the more this pays off.

Less useful for: a handful of MCP servers you always use directly, or Claude Code users who already get equivalent behavior natively — see Small, fixed setups above.

Registry format

The registry file (mcpd-registry.json) is a JSON catalog of MCP servers:

{
  "mcpd_version": "1.0",
  "metadata": {
    "name": "My MCP Registry",
    "description": "Personal registry of MCP servers"
  },
  "servers": [
    {
      "id": "github",
      "name": "GitHub MCP Server",
      "description": "Official GitHub MCP server (remote). Repositories, issues, pull requests, and code search",
      "version": "remote-2025-11",
      "transport": {
        "type": "streamable-http",
        "url": "https://api.githubcopilot.com/mcp/",
        "headers": { "Authorization": "Bearer ${GITHUB_MCP_PAT}" }
      },
      "tags": ["github", "git", "code", "issues"],
      "tools_summary": [
        {
          "name": "issue_write",
          "description": "Create or update an issue or pull request",
          "tags": ["issues", "create"]
        }
      ],
      "estimated_tools_count": 90,
      "enabled": true,
      "last_synced": "2026-06-11T00:00:00Z"
    }
  ]
}

Full schema: registry/schemas/mcpd-schema.json

Project structure

toolbank/
├── toolbank/                    # Python package
│   ├── __init__.py              # Public API and version
│   ├── models.py                # Shared dataclasses
│   ├── registry.py               # Registry loader (mcpd-registry.json)
│   ├── connector.py              # MCP connector — stdio, HTTP, SSE transports
│   ├── sync.py                   # Registry builder (sync from mcp.json)
│   ├── benchmark.py              # Token savings measurement
│   ├── search/
│   │   ├── keyword_search.py    # Server-level TF-IDF search
│   │   ├── tool_search.py       # Tool-level TF-IDF search
│   │   ├── embeddings.py        # Sentence-transformer embedding engine
│   │   └── hybrid.py            # Hybrid keyword + semantic search
│   ├── discovery/
│   │   └── server.py            # Discovery Mode MCP server
│   └── dynamic/
│       ├── server.py            # Dynamic Mode MCP server
│       ├── tool_index.py        # O(1) tool lookup index
│       └── lazy_pool.py         # On-demand connection pool
├── registry/
│   ├── mcpd-registry.example.json  # Example registry
│   ├── benchmark-registry.json  # Fully-specified registry used by the Benchmarks section
│   ├── benchmark-registry-large.json  # 20-server/300-tool registry for at-scale benchmarks
│   └── schemas/
│       └── mcpd-schema.json     # JSON Schema for registry validation
├── docs/
│   ├── specification.md         # Protocol specification
│   ├── architecture.md          # Architecture deep-dive
│   ├── registry-format.md       # Registry format reference
│   └── dynamic-mcp.md           # Dynamic Mode guide
├── examples/
│   ├── cursor-config-discovery.json
│   ├── cursor-config-dynamic.json
│   └── README.md
├── tests/                       # 509 tests, 100% coverage
└── pyproject.toml

Development

git clone https://github.com/KrzysztofAugiewicz/ToolBank.git
cd ToolBank
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=toolbank --cov-report=term-missing

# Run end-to-end integration test
python test_e2e.py

# Benchmark token savings (generate registry first with toolbank-sync)
toolbank-benchmark --registry registry/mcpd-registry.json

CLI reference

Command	Description
toolbank-server	Start the Discovery Mode MCP server
toolbank-gateway	Start the Dynamic Mode MCP server
toolbank-sync	Build or update the registry from an mcp.json config
toolbank-benchmark	Measure token savings for a given registry

All commands accept --help for the full option reference.

Transport support

Transport	Install extra	Use case
stdio	(core)	Local process-based MCP servers
Streamable HTTP	toolbank[http]	Remote HTTP MCP servers
SSE	toolbank[http]	Legacy remote servers (Server-Sent Events)

Transport type is resolved automatically from the registry entry's transport.type field.

Documentation

• Protocol Specification — Tool schemas, message formats, handshake sequence
• Architecture — Data flow, shared core, design decisions
• Registry Format — Full registry schema reference
• Dynamic Mode Guide — Dynamic Mode deep-dive

Publishing to PyPI

Releases are published automatically when a GitHub Release is created. Prerequisites:

1. Add PYPI_API_TOKEN to repository secrets (create at pypi.org/manage/account/token)
2. Create a release with a tag (e.g. v1.0.1)

The publish workflow builds and uploads to PyPI.

Contributing

Contributions are welcome. Please read CONTRIBUTING.md before opening a pull request. For bug reports and feature requests, use GitHub Issues.

Authors

• Krzysztof Augiewicz — Lead Architect & Creator — LinkedIn · GitHub
• Kacper Pisarczyk — Core Contributor, Discovery & Registry Systems — LinkedIn
• Mateusz Wiszniowski — Core Contributor — LinkedIn
• Sebastian Pawłowski — Advisory & QA Support (testing, hardware/software provisioning) — LinkedIn

Full details in AUTHORS.md.

License

MIT