ipdata MCP by usefulapi
Look up location, network, company and threat data for IP addresses and ASNs in ipdata.
Descripción del creador (original en inglés)
Local MCP proxy: collapses N servers' tools into 4 meta-tools and compresses large tool outputs.
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": {
"context-firewall": {
"command": "npx",
"args": [
"-y",
"context-firewall",
"--config",
"your-value"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add context-firewall -- npx -y context-firewall --config your-valueEste necesita valores en su línea de comandos, así que copia la configuración y complétalos.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"context-firewall": {
"command": "npx",
"args": [
"-y",
"context-firewall",
"--config",
"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.
Este necesita valores en su línea de comandos, así que copia la configuración y complétalos.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"context-firewall": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"context-firewall",
"--config",
"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.
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 Alepha188838884, 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 3 meses.
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: ipdata MCP by usefulapi, MaxMind minFraud MCP by usefulapi, Lambda Labs MCP by usefulapi
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
Look up location, network, company and threat data for IP addresses and ASNs in ipdata.
MCP server for MaxMind minFraud — score transactions for fraud risk from your AI assistant.
Read GPU instances, types, images, filesystems and firewall rules; launch and terminate instances.
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.
中文文档
Shrink large MCP tool outputs by 60–95% before they reach your model's context window — and collapse 50+ tool definitions into 4. Real HTML/JSON, measured — see benchmarks. Works with any MCP client, any model. Anything still over your configured token budget after compression is hard-truncated to that budget, with the full original always retrievable via read_more.
*The session report printed on shutdown — this one from a real 3-call session (two large file reads, one echo). Every number measured, none simulated.*
Context Firewall is a local MCP proxy that sits between your AI agent (Claude Code, Claude Desktop, Cursor, Cline, ...) and every downstream MCP server you've configured. Large tool outputs (raw HTML, base64 blobs, giant JSON) are compressed before they ever reach the model's context window, and the client sees exactly 4 tools no matter how many the downstream servers actually have.
| Metric | Result |
|---|---|
| Output compression | 70–94% on real HTML pages, ~97% on large structured JSON (smart stages alone, before budget truncation) — e.g. a live Wikipedia page via the fetch tool: 232,391 → 6,907 chars (97.0%, measured); a GitHub issues JSON payload via the jsonSummary stage: 186,810 → 3,480 chars (98.1%, measured) |
| Tool collapse | 122 → 4 exposed meta-tools (5 real downstream servers incl. official GitHub github-mcp-server, 85 tools) |
| Tool-definition savings | ~28,600 tokens *(estimated, chars ÷ 3.5)* — 102,158 raw definition chars vs. 2,146 exposed |
All figures measured against real downstream MCP servers, not synthetic data — full methodology and tables in docs/BENCHMARKS.md.
list_tool_categories, search_tools, invoke_tool, read_more) and only pays the token cost of a tool's full schema when it actually searches for it.read_more(handle, offset, length).npx context-firewall --config context-firewall.json
Minimal context-firewall.json (the downstreams block mirrors the mcpServers format you already know):
{
"downstreams": { "filesystem": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/project"] }, "github": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"], "env": { "GITHUB_PERSONAL_ACCESS_TOKEN": "${GITHUB_TOKEN}" } } } }
(${GITHUB_TOKEN} above is just this example's environment variable *name* — pick whatever's already set in your shell; it gets expanded into GITHUB_PERSONAL_ACCESS_TOKEN, the env var name the downstream server itself actually reads.)
Which GitHub server? Two options, different tool counts:
npx -y line, zero extra setup. Archived/no longer maintained upstream, but still functional.GITHUB_TOOLSETS=all). Ships as a Go binary or Docker image, not an npm package: "github": {
"command": "docker", "args": ["run", "-i", "--rm", "-e", "GITHUB_PERSONAL_ACCESS_TOKEN", "ghcr.io/github/github-mcp-server"], "env": { "GITHUB_PERSONAL_ACCESS_TOKEN": "${GITHUB_TOKEN}" } }
or, running a locally-built/downloaded binary directly: "command": "/path/to/github-mcp-server", "args": ["stdio"] (same env block; add GITHUB_TOOLSETS to scope which of the 85 tools are exposed).
Per-tool allow/deny policy. Add allowTools/denyTools (array of exact names or * globs) to any downstream entry to restrict which of its tools can be invoked:
"github": {
"command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"], "denyTools": ["delete_*"] }
Deny always wins over allow. When allowTools is set, only matching tools are permitted; everything else on that server is blocked. An empty allowTools: [] is treated the same as omitting it (allow everything), not "deny everything". Blocked tools are hidden from search_tools results, and invoke_tool rejects them before dispatching to the downstream server. Tool counts in list_tool_categories and in the meta-tool descriptions are unfiltered totals — the policy is only enforced at search_tools/invoke_tool time.
Set Context Firewall as your only MCP server — move every downstream server you currently configure directly (filesystem, github, everything, ...) into context-firewall.json's downstreams block instead. Your agent then sees 4 tools instead of the sum of every downstream server's tool count; pointing the client at Context Firewall *alongside* your existing servers doesn't give you the tool-collapse or compression benefit.
Each client below takes the same server entry:
{
"mcpServers": { "context-firewall": { "command": "npx", "args": ["-y", "context-firewall", "--config", "/absolute/path/to/context-firewall.json"] } } }
Project-scoped .mcp.json in your repo root (shown above), or via the CLI:
claude mcp add --transport stdio context-firewall -- npx -y context-firewall --config /absolute/path/to/context-firewall.json
claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json; Windows: %APPDATA%\Claude\claude_desktop_config.json) — same mcpServers block as above.
.cursor/mcp.json (project-scoped) or ~/.cursor/mcp.json (global) — same mcpServers block as above.
cline_mcp_settings.json (VS Code extension storage; macOS: ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json) — same mcpServers block as above.
| Client | Status |
|---|---|
| Claude Code | tested in real agent sessions — autonomous list → search → invoke → read_more workflow verified end-to-end |
| Claude Desktop | protocol-verified* |
| Cursor | config format documented, community testing welcome |
| Cline | config format documented, community testing welcome |
\* Verified via MCP protocol integration tests (236 automated tests, including full stdio protocol round-trips against real downstream servers, run in CI on every push). Real-client reports welcome.
downstreamsEach entry is either a stdio server (same shape as mcpServers) or a Streamable HTTP server:
{
"downstreams": { "local-tool": { "command": "npx", "args": ["-y", "some-mcp-server"], "env": { "TOKEN": "${TOKEN}" } }, "remote-tool": { "url": "https://mcp.example.com/mcp", "transport": "streamable-http" } } }
${VAR_NAME} in any string value is expanded from the environment; a missing variable fails config load with a readable error.
compressionPolicy resolution order is default < perServer < perTool (later overrides earlier, field by field):
{
"compression": { "default": { "maxOutputTokens": 2000, "htmlToMarkdown": true, "stripBase64": true, "jsonSummary": true, "llmSummary": false, "bypass": false }, "perServer": { "github": { "maxOutputTokens": 4000 } }, "perTool": { "filesystem/read_file": { "maxOutputTokens": 8000 } } } }
| Field | Type | Default | Meaning |
|---|---|---|---|
maxOutputTokens | number | 2000 | Soft budget (chars ≈ tokens × 3.5) a compressed output is truncated to as a last resort. |
htmlToMarkdown | boolean | true | Convert detected HTML to Markdown, extracting the main content region (nav/header/footer chrome stripped) when one is recognizable. |
stripBase64 | boolean | true | Replace base64 blobs (data URIs and bare blocks) with a read_more handle. |
jsonSummary | boolean | true | Collapse homogeneous JSON arrays and trim long string fields, keeping valid JSON. |
llmSummary | boolean | false | Semantically summarize over-budget outputs with an LLM of your choice. Requires the top-level llm block - see LLM summarization (opt-in). |
bypass | boolean | false | Skip the whole pipeline for this server/tool - output passes through untouched. |
report{
"report": { "enabled": true, "markdownPath": "./context-firewall-report.md" } }
| Field | Type | Default | Meaning |
|---|---|---|---|
enabled | boolean | true | Print the session report to stderr on shutdown. |
markdownPath | string | *(none)* | If set, also write the report as a Markdown file at this path. |
callToolTimeoutMsTop-level (not nested under compression). Per-invoke_tool timeout in milliseconds passed to the downstream MCP SDK client; a downstream that hangs without responding causes invoke_tool to return an isError result once this elapses, instead of blocking. Defaults to the SDK's own default (60,000ms) when unset.
{ "callToolTimeoutMs": 30000 }
The client calls list_tool_categories() to see what's connected and what it's roughly capable of, search_tools(query) to pull the full input schema for candidate tools, invoke_tool(server, tool, args) to actually run one (compressed on the way back), and read_more(handle, offset, length) to page through anything that got compressed. Compression, when it runs, always applies in the same order: strip base64 → main-content extraction + HTML to Markdown → JSON structure summary → truncate to budget (if the opt-in LLM summarization stage below is enabled, it runs right before truncation). Content extraction is conservative by design: it only strips chrome when a semantic content region ( / ) is recognizable or the removal clearly isn't the page's actual content, and falls back to whole
Solo se muestra el principio; lee el resto en el repo