Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Pay people nearby for physical work: find out if something is true, or have it done.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
AuthorizationLos valores marcados como secretos son llaves de tu cuenta. Nunca los compartas ni los pegues en un chat.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Esta dirección necesita una clave en un header de la solicitud, y el menú para agregar conectores no puede ponerla. Usa Claude Code, Cursor o VS Code.
Ejecuta esto en tu terminal
claude mcp add --transport http exchange https://exchange.lamdis.ai/mcp --header 'Authorization: your-value'Completa los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"exchange": {
"url": "https://exchange.lamdis.ai/mcp",
"headers": {
"Authorization": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"exchange": {
"type": "http",
"url": "https://exchange.lamdis.ai/mcp",
"headers": {
"Authorization": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Esta dirección necesita una clave en un header de la solicitud, y el menú para agregar conectores no puede ponerla. Usa Claude Code, Cursor o VS Code.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Authorization secreto HTTP headerEsta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Todavía no hemos leído la lista de herramientas de este servidor; revisamos los endpoints por turnos.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con el dominio lamdis.ai (el registro comprueba quién es dueño del dominio)
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Aún sin revisar; revisamos los endpoints por turnos
Aún sin revisar; revisamos los endpoints por turnos
Aún sin revisar; leemos la lista de herramientas cuando revisamos el endpoint
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
Debes completar: Authorization
Se ejecuta en la nube del creador. Te conectas por internet sin instalar nada, pero lo que envías pasa por los servidores del creador.
El repo de GitHub cambió por última vez –, y la última versión (0.1.0) llegó al registro hace 2 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. Se conecta por URL, así que también funcionan las apps que aceptan servidores MCP remotos, como ChatGPT en modo desarrollador.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, Reelsaw
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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.
Drive a self-hosted Reelsaw: turn long videos into vertical clips, edit, re-render and export them.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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
Solo se muestra el principio; lee el resto en el repo