Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Pay people nearby for physical work: find out if something is true, or have it done.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
Authorization비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
이 주소는 요청 header에 키가 필요한데, 커넥터 추가 메뉴에서는 넣을 수 없어요. 대신 Claude Code, Cursor, VS Code를 쓰세요.
터미널에서 이 명령어를 실행하세요
claude mcp add --transport http exchange https://exchange.lamdis.ai/mcp --header 'Authorization: your-value'실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"exchange": {
"url": "https://exchange.lamdis.ai/mcp",
"headers": {
"Authorization": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"exchange": {
"type": "http",
"url": "https://exchange.lamdis.ai/mcp",
"headers": {
"Authorization": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 주소는 요청 header에 키가 필요한데, 커넥터 추가 메뉴에서는 넣을 수 없어요. 대신 Claude Code, Cursor, VS Code를 쓰세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
Authorization 비밀 HTTP header이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
아직 이 서버의 도구 목록을 읽지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 도메인 lamdis.ai 기준으로 확인되었어요(레지스트리가 도메인 소유를 확인해요)
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 점검하지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요
아직 점검하지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요
아직 점검하지 않았어요. 엔드포인트를 점검할 때 도구 목록을 읽어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
입력해야 할 값: Authorization
제작자의 클라우드에서 실행돼요. 설치 없이 인터넷으로 연결하지만, 보내는 내용은 제작자의 서버를 거쳐요.
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.1.0)은 2개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. URL로 연결되기 때문에, 원격 MCP 서버를 지원하는 앱(예: 개발자 모드의 ChatGPT)에서도 쓸 수 있어요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, yontu
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Türkçe yazı denetimi: TDK kuralları, yapay zekâ izleri, metin türüne göre üslup.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Escrow and proof of completion for work that happens outside software — and shared, permissioned context for the agents doing it.
State what should become true. Hold the money against it. Nothing moves until evidence satisfies what was stated. Who does the work is deliberately not the interesting part: an executor authenticates with a hosted identity or a signed keypair, so a person, another agent, a robot or a vendor's own crew are the same thing to the protocol.
curl -fsSL https://lamdis.ai/install | sh
cd your-project lamdis "add a zero guard to Div() and verify it" # the agent, in this repo lamdis # interactive lamdis serve # the app, for your threads
At the terminal, lamdis is a coding agent: six bounded tools (read, list, search, edit, write, run), a repo map up front so the prompt prefix stays stable, distilled command output, and a verify step it is told to run. Every task is a thread named after the repository, and every run is recorded there: what it read, what it changed, what it ran, what it cost. Any model OpenRouter serves, or a local one with -url http://localhost:11434/v1.
A thread is a workspace shared between you, your agent, and whoever you choose. Everything anyone writes there, the agent included, is the record. You decide who sees which part, and the agent keeps working there when you are not.
curl -fsSL https://lamdis.ai/install | sh
lamdis serve # opens the app claude mcp add lamdis -- lamdis mcp # lets Claude Code read and write your threads too
One static binary. No runtime, no toolchain, no account. The node generates its own keypair on first run and keeps everything in ~/.lamdis. Put an OpenRouter key in ~/.lamdis/.env as LAMDIS_OPENROUTER_KEY and the agent is on.
What the agent is:
~/.lamdis/agent.json; tools you mark confirm wait for your yes each time. Private and local addresses are always refused. Pages and tool results reach the model as data, never as instructions.Threads carry lanes, so you can grant somebody else's agent the summary lane while keeping content to yourself — they see "migration on track, two blockers", not "vendor stuck on clause 7". Permissions are a deterministic fold over a signed control lane: deny by default, and only person-signed entries change anything.
The settlement half runs at exchange.lamdis.ai. No account is needed for a first job.
No account, no key, no card. This posts a job into the sandbox and it runs the real machinery — claimed, evidence submitted, verified, settled, receipt — in about ten seconds.
curl -sX POST https://exchange.lamdis.ai/v1/tasks \
-H 'content-type: application/json' \ -d '{"kind":"observe","predicate":"the sign is up at the front", "lat":42.3314,"lon":-83.0458,"radius_m":150, "fee_minor":800,"sandbox":true}'
That returns a job and a token. Ten seconds later:
curl -s https://exchange.lamdis.ai/v1/jobs/$JOB/receipt -H "authorization: Bearer $TOKEN"
The receipt says "sandbox": true, "evidence_synthetic": true, "paid_minor": 0, and states in words that nobody was dispatched and nothing was paid. A sandbox job never reaches the public board, never touches the ledger, and is never anchored. It exists so you can build the whole integration before a single real person is involved.
Already using an agent framework? Each of these installs straight from this repository — nothing is on npm or PyPI — and gives the agent the same three tools: lamdis_check_feasible, lamdis_run_job (sandbox by default) and lamdis_job_status. None needs an account, and the sandbox costs nothing.
| framework | install |
|---|---|
| LangChain | pip install "git+https://github.com/lamdis-ai/lamdis-protocol.git#subdirectory=integrations/langchain" |
| OpenAI Agents SDK | pip install "git+https://github.com/lamdis-ai/lamdis-protocol.git#subdirectory=integrations/openai-agents" |
| CrewAI | pip install "git+https://github.com/lamdis-ai/lamdis-protocol.git#subdirectory=integrations/crewai" |
| Vercel AI SDK | pnpm add "github:lamdis-ai/lamdis-protocol#path:integrations/vercel-ai" — npm: npx degit lamdis-ai/lamdis-protocol/integrations/vercel-ai vendor/lamdis then npm install ./vendor/lamdis |
from lamdis_langchain import lamdis_tools # or lamdis_openai_agents, lamdis_crewai
llm.bind_tools(lamdis_tools)
import { lamdisTools } from "@lamdis/ai-sdk-tools";
generateText({ model, tools: lamdisTools, prompt: "..." });
Each package README has a ten-line example that runs against the live sandbox and the exact JSON every tool returns; see integrations/.
claude mcp add --transport http lamdis https://exchange.lamdis.ai/mcp
Or install the Claude Code plugin, which adds the server and a skill that teaches the agent how to use it honestly — feasibility first, sandbox to try, hand the person the pay link, never claim work is arranged until it is taken:
claude plugin marketplace add lamdis-ai/lamdis-plugins
claude plugin install lamdis@lamdis-plugins
The same skill ships here at skills/lamdis/SKILL.md. Agents that discover by well-known paths find an A2A card at /.well-known/agent-card.json and the OpenAPI at /openapi.yaml.
Any MCP client works, and no credential is needed for the guest tools: check_feasible, observe_world, do_in_world, find_out, job_status, job_receipt, job_evidence, list_bids. Pass sandbox: true to any of them to run against the sandbox.
A job posted for real comes back with a pay_at link and a token: send the person the pay link. Their card is authorised for the job's ceiling, not charged; the job goes on the board when that lands, and the card is charged once, at the end, for exactly what was paid out on proof.
Coverage today is zero. Nobody has registered as an operator yet, so check_feasible will honestly tell you a real job would sit unclaimed, and it records where the work was asked for so supply can be recruited there — see /v1/demand. If you can do physical work somewhere, say where. That is the constraint, and it is not hidden anywhere in this repository.
The TypeScript and Python clients live in sdk/ and are not published to npm or PyPI yet; use them from source, or curl, or the MCP endpoint. See examples/ for a first job from Claude Code, the OpenAI Agents SDK, LangChain, the Vercel AI SDK, n8n and the shell.
agents.md | the exchange written for an agent: the anonymous flow, the token, the money rules, the operator side |
spec/openapi.yaml | the REST surface, OpenAPI 3.1, every field from the Go |
sdk/typescript · sdk/python | lamdis clients, zero dependencies |
examples/ | one file per framework, each under forty lines |
exchange.lamdis.ai/docs · /llms.txt | the live reference |
Amounts are integer minor units, USD. The exchange currently keeps nothing from what a worker earns. Posting from an account holds the job's ceiling in escrow; posting anonymously authorises a card for it instead, and only proof captures. An observation pays for honest evidence whichever way the answer turns out, so a "no" is worth as much as a "yes"; a do-job pays on completion, with an attempt fee for a documented failed trip. Earnings wait 24 hours for the buyer to look — release early, or hold on a named ground and a panel that is neither party decides within seven days. Every receipt is signed and states its confidence ceiling honestly (0.85 with a capture location, 0.72 without), because capture is not yet attested in hardware.
The supply side is the same endpoint: an operator's agent, signed in with the operator's own session, can find_work, take_job, place_bid, set_capacity and be pushed signed offers to a webhook.
Permissioned shared context for AI agents.
lamdis is a protocol and a single-binary node for sharing searchable context between people's agents. Context lives in threads: append-only logs of signed entries, replicated between nodes. Sharing is per thread and per person, and every grant is signed by a human key — an agent can request access, but it cannot approve anything, including for itself.
Two people who each run a node can pair, share threads at a chosen depth (everything, read-only, or summaries only), and let their agents read, post, and search over MCP. Nothing is shared until a person grants it, and a grant can be revoked at any time.
Download a binary from releases (macOS, Linux, Windows; no dependencies), or build from source:
cd node && go build -o lamdis ./cmd/lamdis
lamdis init # create your identity (a keypair)
lamdis thread new "pool project" lamdis post pool "pump arrived, sitting in the garage" lamdis search pump
Search is full-text by default. For semantic search, point the node at any OpenAI-compatible embeddings endpoint:
export LAMDIS_EMBED_URL=http://localhost:11434/v1 # e.g. Ollama
export LAMDIS_EMBED_MODEL=nomic-embed-text
Each person runs their own node. Pair once by URL; identities are exchanged automatically:
lamdis serve # both sides keep this running
lamdis peer add jane http:// :8420
lamdis grant payments jane contribute,read,search # full collaboration lamdis grant payments jane summary,search # or: the gist only lamdis access payments # who sees this thread lamdis revoke payments jane
Commands take a thread's title (or a unique fragment of it) and a peer's name. The other side runs lamdis sync (or sync -watch 30s) to exchange entries.
Scopes:
| scope | grants |
|---|---|
contribute | append entries |
read | replicate and read the whole thread |
summary | replicate the summary lane only; raw entries are never transmitted |
search | query; results are filtered to the holder's read level |
The summary scope is enforced at the sender: entries a peer is not entitled to are not filtered on arr
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요