Puter MCP
Puter MCP enables AI tools to interact with Puter: manage files, websites, workers, and more
Descripción del creador (original en inglés)
Content-addressed contracts and a verification cache that cut agent context by skipping proven code.
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.
Pasó 2/2 revisiones · Funciona en tu computadora
Primero: ¿qué IA usas?
Elige tu app y los pasos aparecen abajo.
Funciona con Claude, pero hay que preparar algo antes
Instala Python (uv) en tu computadora primero (una sola vez) · Solo en la app de Claude para computadora (Windows o Mac), no en la web ni en el móvil
Una sola vez; sirve para todos los servidores que necesitan Python (uv)
SettingsDeveloperEdit Config
{
"mcpServers": {
"heddle": {
"command": "uvx",
"args": [
"heddle-mcp"
]
}
}
}Usa Heddle y dime qué puedes hacer por mí; luego muéstrame una cosa✓ Si Claude dice que está usando Heddle o muestra una herramienta de Heddle en la respuesta, ya está conectado
Nombres de menú comprobados con la ayuda de Anthropic el 11 nov 2026 · tu pantalla puede variar un poco según la versión
ChatGPT todavía no puede conectar Heddle
Heddle se instala en tu computadora, pero ChatGPT solo conecta servidores que funcionan en la web
A · Usa una app que pueda conectar Heddle
Funciona con Claude Code, pero hay que preparar algo antes
Instala Python (uv) en tu computadora primero (una sola vez)
Una sola vez; sirve para todos los servidores que necesitan Python (uv)
Mac: pulsa Command+Espacio y escribe Terminal · Windows: abre PowerShell
claude mcp add heddle -- uvx heddle-mcpDeberías ver Heddle en la lista como conectado
Usa Heddle y dime qué puedes hacer por mí; luego muéstrame una cosa✓ Si Claude Code dice que está usando Heddle o muestra una herramienta de Heddle en la respuesta, ya está conectado
Nombres de menú comprobados con la ayuda de Anthropic el 11 nov 2026 · tu pantalla puede variar un poco según la versión
Funciona con Cursor, pero hay que preparar algo antes
Instala Python (uv) en tu computadora primero (una sola vez)
Una sola vez; sirve para todos los servidores que necesitan Python (uv)
Usa Heddle y dime qué puedes hacer por mí; luego muéstrame una cosa✓ Si Cursor dice que está usando Heddle o muestra una herramienta de Heddle en la respuesta, ya está conectado
Nombres de menú comprobados con la ayuda de Cursor el 11 nov 2026 · tu pantalla puede variar un poco según la versión
Funciona con VS Code, pero hay que preparar algo antes
Instala Python (uv) en tu computadora primero (una sola vez)
Una sola vez; sirve para todos los servidores que necesitan Python (uv)
Usa Heddle y dime qué puedes hacer por mí; luego muéstrame una cosa✓ Si VS Code dice que está usando Heddle o muestra una herramienta de Heddle en la respuesta, ya está conectado
Nombres de menú comprobados con la ayuda de VS Code el 11 nov 2026 · tu pantalla puede variar un poco según la versión
Otras apps que usan MCP, como Windsurf, Zed o Gemini CLI, añaden servidores de forma distinta. Míralo en la lista de apps
Ver todas las apps que usan MCP →
La configuración JSON está en “Para desarrolladores”, abajo
Claude Code
claude mcp add heddle -- uvx heddle-mcpClaude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"heddle": {
"command": "uvx",
"args": [
"heddle-mcp"
]
}
}
}Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"heddle": {
"command": "uvx",
"args": [
"heddle-mcp"
]
}
}
}VS Code (mcp.json)
{
"servers": {
"heddle": {
"type": "stdio",
"command": "uvx",
"args": [
"heddle-mcp"
]
}
}
}Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
pypi: heddle-mcpSegún cómo se conecta este servidor y la documentación de cada app, comprobado el 10 nov 2026.
Se ejecutan en tu computadora en
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 11 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub davet47, dueña del repo
Último cambio de código: hace 2 meses
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Pregunta, comparte un consejo o avisa de que no funciona. Ordenado por lo que la gente marcó como útil.
Aún no hay publicaciones. Sé la primera persona en preguntar o compartir.
Lo publicado aquí lo escriben usuarios, no el equipo de PopNx.
Aún no hay reseñas
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 2 de 2 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.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez hace 2 meses, y la última versión (0.3.3) llegó al registro hace 4 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.
En la misma categoría: Puter MCP, XcodeBuildMCP, XcodeBuildMCP
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:↓ Descargas por semanai★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
Puter MCP enables AI tools to interact with Puter: manage files, websites, workers, and more
XcodeBuildMCP provides tools for Xcode project management, simulator management, and app utilities.
XcodeBuildMCP provides tools for Xcode project management, simulator management, and app utilities.
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.
Hashloom treats software units as content-addressed contracts rather than files. An MCP server that makes agent regeneration loops cheap.
Because contracts are content-addressed and dependency-aware, agents reuse verification, compute blast radius precisely, and regenerate code from a few hundred tokens of context instead of re-reading whole files. Build systems ask which files changed. Hashloom asks which software obligations changed.
The name is a coinage that says exactly what it is: a loom keyed by hashes. On a loom, the warp threads are the fixed, durable strands, and the shuttle weaves disposable weft through them. Contracts are warp. Code is weft. (Until 0.3.3 this project shipped as heddle-mcp; the engine is unchanged.)
Agents repeatedly pay to rediscover software structure. Spec-driven development tools made specs the durable artifact and code regenerable, but they run on plain files, so every regeneration loop re-derives what the project already knows:
Hashloom treats each software unit as a content-addressed contract with explicit dependencies, not a file. A contract is a small YAML spec (signature, invariants, examples, dependency names); the implementation behind it is regenerable weft. Because every contract is hashed and its dependencies are named, the structure an agent keeps re-deriving from files becomes something hashloom computes once and serves.
The model buys three things, all mechanical:
Same three regeneration tasks on a 20-contract sample project, once with raw file reads, once through hashloom (tiktoken cl100k, reproduce with uv run python bench/benchmark.py):
| task | raw files | hashloom | reduction |
|---|---|---|---|
| revenue_by_region | 1,925 | 369 | 5.2x |
| top_customers | 2,137 | 337 | 6.3x |
| revenue_by_category | 1,942 | 396 | 4.9x |
| total | 6,004 | 1,102 | 5.4x |
Raw mode counts what a file-based agent reads per task: the unit's spec file, every transitive dep's spec file, every source module in the dep closure, the unit's test file, and the output of running the suite. It is deliberately generous to the baseline: it assumes the agent already knows the exact dependency closure, which is precisely the thing hashloom computes for you.
The same methodology sweeps *every* unit of all five example projects — Python, Go, TypeScript, Java, and C# — via bench/sweep.py, and works on any hashloom project including yours. Full sweeps average lower than the gate (they count the leaf types that barely benefit); the ratio tracks dependency depth, so deeper projects score higher. All the numbers, their distributions, and the honest caveats live in docs/benchmarks.md.
pip install hashloom
cd your-project hashloom init # creates .hashloom/ and contracts/ hashloom index # builds the store from contracts/
Point Claude Code at it:
claude mcp add hashloom -- hashloom serve
(Stdio transport; the server resolves the project by walking up from its working directory to the nearest .hashloom/.)
New to the workflow? docs/getting-started.md walks through building a package contract-first with an agent — the working rules to give it, the review loop, and the verify gate.
One YAML file per unit in contracts/. Minimal, hand-writable, hashable:
name: revenue_by_region
signature: "(sales: list[Sale]) -> dict[Region, float]" deps: [Sale, Region] # other contract names invariants:
examples:
tests: [tests/test_revenue.py::test_revenue_by_region] # pytest node IDs impl: src/revenue.py::revenue_by_region # current woven weft status: inferred # reverse-engineered, not yet human-reviewed; omit once confirmed
Subdirectories are namespaces: contracts/billing/invoice.yaml is the contract billing/invoice, so the same short name can live in different folders. A contract's name must match its path under contracts/.
A contract belongs on a stable seam: an interface other units depend on and that you expect to outlive its current implementation. The implementation behind it is disposable weft, regenerated freely. Dropping a contract where it does not earn that place is correct use, not a failure. The failure mode is the opposite, over-pinning interiors you would happily rewrite, which turns the durable layer into busywork.
Contracts are reviewed artifacts. Authoring one is cheap and getting cheaper, so the real cost is reviewing it, not writing it. A wrong contract is worse than no contract, because the durable artifact now lies: agents will regenerate code to satisfy a spec that is itself incorrect. Review a contract the way you review an interface, not the way you skim generated code.
A contract an agent reverse-engineers from existing code can declare that it hasn't earned that review yet: status: inferred. Tools then flag — by default, never refuse — any blast-radius or verification answer that rests on it (inferred: true on dependents, an inferred list on verify results, a review queue in status). Teams that want unvetted contracts to hard-fail can opt in to strict provenance mode (.hashloom/config.json → {"strict_provenance": true}): verify then refuses such units with a structured inferred_contract error — no tests run, no verdict cached — until they're reviewed; reads and writes stay advisory so drafts can still land and be inspected. Absent means confirmed, and confirming an inferred contract after review is free: status is provenance, not meaning, so the flip invalidates nothing (under strict mode, a pre-existing green simply revives on confirm).
impl, tests, invariants, and status are excluded, so relocating files never invalidates, rewording an invariant is free, and confirming an inferred contract never invalidates anything. Invariants are documentation, not a machine obligation; the real check is the tests, whose source is in the verification key.(contract hash, impl hash, test-source hash, toolchain identity, transitive dep contract hashes). Hashloom caches verification results, keyed so that a change to any contract in the closure, to the implementation, to a test's own source, or to the toolchain identity forces a re-run. The identity is the toolchain version (python 3.11.7, go 1.21.5, node ts , java 21.0.3, dotnet 9.0.303) plus, when the project commits a dependency source at its root (uv.lock, go.sum, package-lock.json, pom.xml, ...), a deps = digest of it — so a green from an environment with a different declared dependency set is never trusted, and a 3.11 pass is never served to 3.13. The grain is the *committed declared* set (never OS/arch — CI greens still serve every platform); a venv that disagrees with its own lockfile is outside the key, which is one of the things shared-cache revocation exists for. Failures are never served from cache. For Python, the test-source hash covers each test's *fixture closure* too — the fixtures it requests (arguments, usefixtures, literal getfixturevalue), autouse fixtures in scope, and fixtures of fixtures, statically resolved through the test's module and conftest chain (nearest wins) — so editing only a conftest fixture busts exactly the tests that lean on it; fixture use the static walk cannot resolve degrades to hashing the whole chain, over-busting rather than under-busting. Two caveats. A cached pass assumes deterministic tests, so a green result that depended on wall-clock time, network, or randomness can outlive the condition that made it pass. And plain helper functions a test imports and calls (and non-Python test helpers) are still outside the hash — only the fixture graph is covered (see ISSUES.md).| tool | does |
|---|---|
get_contract | the ~300-token context packet: contract + hash + one-line dep signatures + caller list |
put_contract | validate, write contracts/ .yaml, return new hash, a semantic diff of what changed, and every invalidated dependent |
get_dependents | blast-radius query, direct or transitive, names + hashes; inferred (unreviewed) contracts flagged |
verify | per-unit cached-pass / pass / fail plus a top-level ok gate bit; radius=true widens each name to its full blast radius; runs tests only on cache misses; failures come back as a ≤40-token assertion summary, never a traceback; inferred contracts in the closure flagged (refused under strict provenance mode) |
status | dirty contracts, stale verifications, cache hit-rate, resolved verify interpreter, cumulative token counters |
Every tool returns structured errors — {"error": {"code": "unknown_dep", "message": "'Regoin' not found — nearest: 'Region'"}} — never a stack trace.
verify runs your tests with the project's own python, resolved in order: hashloom serve --python PATH → .hashloom/config.json ({"python": "..."}) → an auto-detected /.venv → the interpreter running hashloom. So a globally-installed hashloom can verify a project against its own virtualenv without being installed into it; hashloom status shows which interpreter it resolved.
.hashloom/config.json also takes verify_timeout (seconds per pytest run, default 300) for suites that need longer than the default, pycache_trust (default true); set pycache_trust: false — or pass --no-pycache-trust — to clear the project's __pycache__ before each verify run, so a stale .pyc can never shadow the current source, and strict_provenance (default false) to make verify refuse units whose closure contains inferred contracts.
The impl's file extension picks the language adapter — each