Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Keyless web search and page reading for AI agents, built to get past bot walls.
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.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"searchio": {
"command": "npx",
"args": [
"-y",
"@ioserviceslabs/searchio-mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add searchio -- npx -y @ioserviceslabs/searchio-mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"searchio": {
"command": "npx",
"args": [
"-y",
"@ioserviceslabs/searchio-mcp"
]
}
}
}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": {
"searchio": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ioserviceslabs/searchio-mcp"
]
}
}
}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.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
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 7 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub IOServicesLabs, dueña del repo
Todavía no hemos leído este repo en GitHub
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
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.
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 –, y la última versión (0.1.1) llegó al registro hace 27 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Context7, Codebase Memory, Exa
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
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
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.
searchio is a tool for search, page reading, and product lookup. Use it from the command line, from Python, or over HTTP. Use it as a person or as an AI agent. Search and reading need no API key.
An AI agent can set up searchio for its user in one pass. Follow these steps in order. Stop when the verify step passes.
1. Use Docker when Docker runs. Try:
docker run -i --rm ghcr.io/ioserviceslabs/searchio-mcp
The image holds the full system: fetch ladder, Rust engine sidecar, and a Chromium for the challenge tier. No other install step. Go to step 3.
2. No Docker: install from source. Needs Python 3.10 or newer.
pip install "searchio[mcp]"
curl -fsSL https://raw.githubusercontent.com/IOServicesLabs/searchio-engine/main/scripts/install.sh | sh
The pip command installs the searchio package with the MCP server from PyPI. The curl command adds the Rust engine sidecar at ~/.searchio/bin/se-serve (linux, macOS, Windows with Git Bash). The sidecar is the fast tier-2 backend. It is optional but recommended. For the latest development build use pip install "searchio[mcp] @ git+https://github.com/IOServicesLabs/searchio".
3. Add the MCP server to the client. Claude Desktop and Cursor use this config shape.
Docker:
{
"mcpServers": { "searchio": { "command": "docker", "args": ["run", "-i", "--rm", "ghcr.io/ioserviceslabs/searchio-mcp"] } } }
Source install:
{
"mcpServers": { "searchio": { "command": "searchio-mcp" } } }
4. Verify. Restart the client. Ask: "Use searchio to search the web for the current time in Tokyo." Real results mean the setup works. An error about a missing command means the config points at a path that does not exist; fix the config and retry.
Note. npx @ioserviceslabs/searchio-mcp also works as the client command in step 3. The launcher starts the Docker image when Docker runs. Without Docker it starts the local install from step 2.
You need Python 3.10 or newer.
Path A -- pip (one command):
pip install "searchio[mcp]"
searchio-mcp # MCP server on stdio, ready for an LLM client
searchio doctor checks the install. The Rust engine sidecar is optional. Install it with the curl command in the quick start, step 2.
```
Path B -- Docker (no Python needed):
docker run -i --rm ghcr.io/ioserviceslabs/searchio-mcp # stdio MCP
docker run -p 8080:8080 ghcr.io/ioserviceslabs/searchio-mcp \ searchio-mcp --transport sse --host 0.0.0.0 --port 8080
No prebuilt image for your platform? Build from source:
docker build -t searchio https://github.com/IOServicesLabs/searchio.git
The image holds the full ladder: the Python tiers, the Rust engine sidecar, and a Chromium for the challenge tier. State (clearance, sessions) persists in a volume at /root/.searchio:
docker run -i --rm -v searchio-state:/root/.searchio ghcr.io/ioserviceslabs/searchio-mcp
Path C -- from source:
git clone https://github.com/IOServicesLabs/searchio.git
cd searchio python -m venv .venv .venv\Scripts\activate # Windows
pip install -e '.[mcp]' searchio doctor
searchio doctor shows which fetch tiers work on your machine. Tier 0 is plain HTTP. Tier 1 adds TLS impersonation. Tier 2 adds a real browser for pages that block bots. Tier 2 starts on its own the first time it is needed.
Tier 2 uses the Rust [searchio-engine]( https://github.com/IOServicesLabs/searchio-engine) sidecar when a se-serve binary is present. It boots in milliseconds and has no Chromium. The Docker image includes it. On a host install, one command adds it:
curl -fsSL https://raw.githubusercontent.com/IOServicesLabs/searchio-engine/main/scripts/install.sh | sh
The installer drops the binary at ~/.searchio/bin/se-serve and searchio finds it on its own. You can also set SEARCHIO_ENGINE_BIN to any binary.
Point any MCP client at the server:
{
"mcpServers": { "searchio": { "command": "searchio-mcp" } } }
With Docker, use "command": "docker", "args": ["run", "-i", "--rm", "searchio"].
No Python at all? The npm wrapper picks a backend for you (Docker when present, else a local install):
{
"mcpServers": { "searchio": { "command": "npx", "args": ["-y", "@ioserviceslabs/searchio-mcp"] } } }
searchio search "best noise cancelling headphones 2026"
searchio search "rust async runtime comparison" --intent code searchio search "artemis mission" --domains nasa.gov searchio search "heat pump retrofit" --k 20 --json
You can set an intent: web (default), news, video, shopping, academic, code, forum, reference, local. You can also put site:nasa.gov in the query.
searchio read https://example.com/some/article
searchio read https://example.com/paper.pdf --chars 20000
The command prints the readable text. It climbs walls, bot checks, and JavaScript-only pages on its own. If a page cannot be read, you get a clear reason. You do not get an empty page.
searchio items "sony wh-1000xm5" # one row per product across retailers
searchio items "dyson v15" --domains bestbuy.com --json searchio marketplace "ford f-150" --city seattle # Facebook Marketplace, browser-backed
This part costs money. Set one key:
set ANTHROPIC_API_KEY=... # Windows
searchio research "What changed in the EU battery regulation this year, and who is affected?"
To use DeepSeek instead, set SEARCHIO_LLM_PROVIDER=deepseek and DEEPSEEK_API_KEY=.... The answer comes back with findings. Each finding cites pages the workers read.
searchio is also an MCP (Model Context Protocol) server. AI agents call it over MCP. The same tools that the CLI uses are available to the agent.
Install the MCP extra:
pip install -e '.[mcp]'
Start the server over stdio:
searchio mcp
python -m searchio.mcp
Start the server over HTTP:
searchio mcp --transport streamable-http --host 127.0.0.1 --port 8080 --path /mcp
The server has these tools:
| Tool | What it does |
|---|---|
search | Fused, ranked web search across keyless providers. |
search_items | Product listings with prices, merged across retailers. |
search_local | Facebook Marketplace classifieds near a city (browser-backed). |
youtube_transcript | Caption transcript of one YouTube video: timed segments + full text. |
read_url | Read one URL through the blocking-resistant ladder. Returns markdown. |
research | Run the multi-step research swarm on a question (needs an LLM key). |
stats | Ladder and provider health (per-domain learned tiers, block rates). |
The server also has one resource: searchio://health returns service health as a small JSON object.
The MCP server has a payment gate. It uses the x402 shape. When the gate is on, a billable tool returns a 402 (Payment Required) with an X-Payment challenge until the caller pays.
Set the gate with SEARCHIO_MCP_PAYMENT:
| Value | Result |
|---|---|
(empty, default) | Gate open. Nothing is charged. For local and dev use. |
static: | The caller must send Authorization: Bearer . Without it the tool returns a 402 + X-Payment challenge. |
This is the shape a real verifier uses. It has no ledger. When you host the server for others, put a real x402 verifier in front. The verifier checks payment, then the tool runs. This is how searchio is hosted next to SwarmIO for search and research credits.
searchio serve --port 8080
| Endpoint | What it does |
|---|---|
GET /search?q=...&intent=web&k=10&domains=a.com,b.org&freshness=week&locale=de-DE | Search results |
GET /read?url=...&session=me | Readable text of one page |
GET /items?q=...&domains=... | Product listings merged per product |
GET /marketplace?q=...&city=seattle | Facebook Marketplace listings |
GET /transcript?url=...&lang=en | YouTube caption transcript (400 not-a-video · 404 no captions · 451 refused) |
POST /research {"question": "..."} | The AI research swarm |
GET /providers, GET /stats, GET /healthz | Status |
Errors are honest status codes: 422 for a bad request, 400 for a target the policy refuses, 451 for a page blocked by an anti-bot wall, 502 or 503 when providers failed or are all cooling down after failures. A search answer lists providers_used, providers_failed and providers_skipped (breakers open, with the cooldown left).
If several people or agents share one server, give each its own session on /read. Then cookies from one never ride along on another's requests.
The Dockerfile in this repo builds the full ladder: Python tiers, the Rust engine sidecar, and a Chromium for the challenge tier. See Path B in section 1 for the build and run commands. docker compose up does the same with the state volume wired up.
Inside the container, tier 2 is the engine sidecar first; the patchright Chromium takes the pages that need a real browser (the challenge tier). Cache, learned per-domain tiers, and clearance cookies live in the searchio-state volume, so they survive restarts. Keys for the research swarm go in as environment variables, the same names as in section 10.
import asyncio
from searchio.engine import Engine
async def main(): eng = Engine() res = await eng.search("lithium iron phosphate battery lifespan", intent="web", k=5) for doc in res.docs: print(doc.title, doc.url)
doc = await eng.read("https://example.com/article") print(doc.text[:500])
items = await eng.find_items("sony wh-1000xm5") for it in items: print(it.title, it.price, it.url)
await eng.close()
asyncio.run(main())
Everything is an environment variable with the SEARCHIO_ prefix, or a line in a .env file next to where you run it. The ones people change most:
| Variable | Default | Meaning |
|---|---|---|
SEARCHIO_MAX_TIER | 2 | 0 HTTP only, 1 adds TLS impersonation, 2 adds the browser |
SEARCHIO_ROBOTS_POLICY | warn | enforce, warn, or off |
SEARCHIO_PROXY | none | Proxy URL for the HTTP tiers |
SEARCHIO_FACEBOOK_CITY | none | Default city for Marketplace searches |
SEARCHIO_CACHE_ENABLED | true | Cache fetched pages for an hour |
SEARCHIO_STATE_DIR | ~/.searchio | Where cache, profiles and clearance cookies live |
SEARCHIO_MCP_PAYMENT | | MCP payment gate (see 6.2) |
SEARCHIO_MCP_BEARER | none | Bearer token the MCP gate checks |
research calls a paid model.searchio providers lists every source and whether it is configured.searchio doctor is the first thing to run when something looks off.