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chore: update registry metadata (stars and pulls) (#1229)
* chore: update registry metadata (stars and pulls) * chore: update tool lists for MCP servers (server.json)\n\nUpdated servers:\n- browserbase\n- pagerduty\n- redis\n\nAutomatically updated using 'catalog update-tools' command.\n\nCo-authored-by: toolhive-release-app[bot] <toolhive-release-app[bot]@users.noreply.github.com> * chore: update registry metadata (stars and pulls) --------- Co-authored-by: toolhive-release-app[bot] <280093410+toolhive-release-app[bot]@users.noreply.github.com>
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registries/official/servers/browserbase/server.json

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"io.github.stacklok": {
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"ghcr.io/stacklok/dockyard/npx/browserbase-mcp-server:2.4.3": {
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"metadata": {
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"last_updated": "2026-04-07T03:03:37Z",
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"stars": 3230
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"last_updated": "2026-05-22T03:21:50Z",
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"stars": 3348
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},
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"overview": "## Browserbase MCP Server\n\nThe browserbase MCP server is a Model Context Protocol (MCP) server that gives AI assistants full, programmable control of real web browsers running in the cloud. Powered by Browserbase, it enables AI-driven workflows to load pages, execute JavaScript, interact with dynamic UIs, and capture screenshots or HTML — making it possible to automate and reason over modern, JavaScript-heavy websites that traditional HTTP-based tools can't handle. This server is ideal for web automation, end-to-end testing, live research, and data extraction workflows that require a real browser environment.",
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"permissions": {

registries/official/servers/cloudflare-remote/server.json

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"license": "Apache-2.0"
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},
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"metadata": {
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"last_updated": "2026-04-08T03:03:51Z",
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"stars": 326
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"last_updated": "2026-05-29T03:21:49Z",
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"stars": 484
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},
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"overview": "## Cloudflare API (Remote)\n\nThe Cloudflare API remote MCP server is Cloudflare's official managed Model Context Protocol server providing token-efficient access to the entire Cloudflare API. Powered by Code Mode, it exposes over 2,500 API endpoints across Workers, KV, R2, D1, Pages, DNS, Firewall, Load Balancers, Stream, Images, AI Gateway, Vectorize, Access, Zero Trust, and more through just two tools in approximately 1,000 tokens. It supports OAuth and API token authentication via Streamable HTTP transport.",
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"status": "Active",

registries/official/servers/datadog-remote/server.json

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"license": "Proprietary"
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},
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"metadata": {
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"last_updated": "2026-04-08T03:03:50Z",
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"stars": 30
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"last_updated": "2026-05-22T03:20:53Z",
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"stars": 38
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},
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"overview": "## Datadog (Remote)\n\nThe Datadog remote MCP server is Datadog's official managed Model Context Protocol server that connects AI agents to Datadog's observability platform. It provides structured access to logs, metrics, traces, dashboards, monitors, incidents, hosts, services, events, and notebooks through toolsets that can be selectively enabled. The server uses Streamable HTTP transport with API key and application key authentication, and supports regional endpoints for different Datadog sites.",
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"status": "Active",

registries/official/servers/google-maps-remote/server.json

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"license": "Proprietary"
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},
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"metadata": {
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"last_updated": "2026-04-08T03:03:51Z",
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"stars": 3507
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"last_updated": "2026-05-29T03:21:49Z",
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"stars": 4134
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},
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"overview": "## Google Maps Grounding Lite (Remote)\n\nThe Google Maps Grounding Lite remote MCP server is Google's official managed Model Context Protocol server providing access to Google Maps Platform geospatial data. It offers three tools for place search with summaries and Google Maps links, weather lookups with current conditions and forecasts, and route computation with distance and duration data. Authentication requires a Google Maps API key passed via the X-Goog-Api-Key header. The server is currently experimental and available at no charge during the pre-GA preview period.",
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"status": "Active",

registries/official/servers/graphlit/server.json

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"io.github.stacklok": {
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"ghcr.io/stacklok/dockyard/npx/graphlit-mcp-server:1.0.20260112001": {
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"metadata": {
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"last_updated": "2026-04-08T03:03:51Z",
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"stars": 373
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"last_updated": "2026-05-29T03:21:49Z",
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"stars": 376
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},
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"overview": "## Graphlit MCP Server\n\nThe graphlit MCP server is a Model Context Protocol (MCP) server that enables AI assistants and agents to interact directly with Graphlit, a platform for ingesting, processing, organizing, and querying unstructured content using knowledge graphs and embeddings. It allows AI-driven workflows to load documents, media, and web content into Graphlit and then reason over that content using structured queries — without building custom ingestion or retrieval pipelines. This server is especially useful for knowledge management, content intelligence, retrieval-augmented generation (RAG), and research workflows that span large, heterogeneous content collections.",
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"permissions": {

registries/official/servers/pagerduty/server.json

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"--enable-write-tools"
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],
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"metadata": {
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"last_updated": "2026-04-08T03:03:51Z",
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"stars": 58
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"last_updated": "2026-05-22T03:22:20Z",
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"stars": 69
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},
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"overview": "## PagerDuty MCP Server\n\nThe pagerduty MCP server is a Model Context Protocol (MCP) server that enables AI assistants and agents to interact directly with PagerDuty, the incident management and on-call platform. It allows AI-driven workflows to access incidents, services, schedules, and on-call status information, bringing real-time incident response context into AI-assisted operations. This server is especially useful for incident triage, on-call support, operational awareness, and post-incident analysis where PagerDuty serves as the primary system of record.",
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"permissions": {

registries/official/servers/redis/server.json

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"io.github.stacklok": {
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"docker.io/mcp/redis:latest": {
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"metadata": {
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"last_updated": "2026-04-07T03:03:37Z",
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"stars": 474
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"last_updated": "2026-05-22T03:21:55Z",
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"stars": 511
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},
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"overview": "## Redis MCP Server\n\nThe redis MCP server enables LLMs to interact with Redis key-value databases through a set of standardized tools. The server connects to Redis databases and provides MCP tools for basic operations (CRUD operations, database management), data structures (full support for strings, hashes, lists, sets, sorted sets, JSON), vector search functionality, and streams/Pub/Sub features for messaging and time-series data handling.",
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"permissions": {
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},
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"name": "hset"
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},
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{
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"annotations": {},
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"description": "\nPerform a hybrid search combining a Redis filter expression with KNN vector similarity.\n\nHybrid search pre-filters documents by metadata before ranking by vector similarity —\nthe standard pattern for production RAG and semantic search pipelines.\n\nFilter expression examples:\n \"*\" → no filter, pure vector search (same as vector_search_hash)\n \"@category:{news}\" → tag filter\n \"@year:[2020 2024]\" → numeric range\n \"@lang:{en} @year:[2022 +inf]\" → combined tag + range\n \"@title:redis\" → full-text match on a text field\n\nFull filter syntax: https://redis.io/docs/latest/develop/interact/search-and-query/query/\n\nArgs:\n query_vector: List of floats to use as the query vector.\n filter_expression: Redis filter expression to restrict candidates before KNN ranking.\n Defaults to '*' (no filter).\n index_name: Name of the Redis index (default: 'vector_index').\n vector_field: Name of the indexed vector field (default: 'vector').\n k: Number of nearest neighbors to return.\n return_fields: Additional fields to include in results (optional).\n\nReturns:\n A list of matched documents with their similarity score, or an error message.\n",
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"inputSchema": {
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"properties": {
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"filter_expression": {
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"default": "*",
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"title": "Filter Expression",
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"type": "string"
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},
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"index_name": {
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"default": "vector_index",
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"title": "Index Name",
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"type": "string"
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},
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"k": {
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"default": 5,
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"title": "K",
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"type": "integer"
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},
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"query_vector": {
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"items": {
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"type": "number"
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},
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"title": "Query Vector",
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"type": "array"
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},
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"return_fields": {
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"anyOf": [
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{
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"items": {
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"type": "string"
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},
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"type": "array"
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},
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{
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"type": "null"
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}
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],
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"default": null,
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"title": "Return Fields"
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},
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"vector_field": {
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"default": "vector",
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"title": "Vector Field",
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"type": "string"
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}
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},
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"required": [
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"query_vector"
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],
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"type": "object"
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},
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"name": "hybrid_search"
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},
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{
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"annotations": {},
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"description": "Get Redis server information and statistics.\n\nArgs:\n section: The section of the info command (default, memory, cpu, etc.).\n\nReturns:\n A dictionary of server information or an error message.\n",
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},
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"name": "vector_search_hash"
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},
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{
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"annotations": {},
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"description": "Acknowledge entries that were processed by a consumer group.\n\nArgs:\n key (str): The stream key.\n group_name (str): The consumer group name.\n entry_ids (List[str]): Entry IDs to acknowledge.\n\nReturns:\n str: Confirmation message or an error message.\n",
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"inputSchema": {
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"properties": {
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"entry_ids": {
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"items": {
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"type": "string"
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},
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"title": "Entry Ids",
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"type": "array"
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},
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"group_name": {
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"title": "Group Name",
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"type": "string"
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},
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"key": {
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"title": "Key",
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"type": "string"
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}
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},
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"required": [
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"key",
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"group_name",
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"entry_ids"
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],
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"type": "object"
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},
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"name": "xack"
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},
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{
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"annotations": {},
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"description": "Add an entry to a Redis stream with an optional expiration time.\n\nArgs:\n key (str): The stream key.\n fields (dict): The fields and values for the stream entry.\n expiration (int, optional): Expiration time in seconds.\n\nReturns:\n str: The ID of the added entry or an error message.\n",
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},
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"name": "xdel"
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},
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{
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"annotations": {},
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"description": "Create a consumer group for a Redis stream.\n\nArgs:\n key (str): The stream key.\n group_name (str): The consumer group name.\n start_id (str, optional): Stream ID from which the group starts consuming.\n mkstream (bool, optional): Create the stream if it does not exist.\n\nReturns:\n str: Confirmation message or an error message.\n",
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"inputSchema": {
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"properties": {
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"group_name": {
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"title": "Group Name",
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"type": "string"
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},
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"key": {
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"title": "Key",
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"type": "string"
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},
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"mkstream": {
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"default": true,
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"title": "Mkstream",
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"type": "boolean"
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},
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"start_id": {
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"default": "$",
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"title": "Start Id",
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"type": "string"
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}
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},
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"required": [
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"key",
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"group_name"
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],
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"type": "object"
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},
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"name": "xgroup_create"
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},
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{
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"annotations": {},
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"description": "Destroy a consumer group for a Redis stream.\n\nArgs:\n key (str): The stream key.\n group_name (str): The consumer group name.\n\nReturns:\n str: Confirmation message or an error message.\n",
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"inputSchema": {
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"properties": {
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"group_name": {
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"title": "Group Name",
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"type": "string"
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},
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"key": {
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"title": "Key",
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"type": "string"
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}
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},
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"required": [
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"key",
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"group_name"
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],
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"type": "object"
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},
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"name": "xgroup_destroy"
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},
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{
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"annotations": {},
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"description": "Read entries from a Redis stream.\n\nArgs:\n key (str): The stream key.\n count (int, optional): Number of entries to retrieve.\n\nReturns:\n str: The retrieved stream entries or an error message.\n",
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},
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"name": "xrange"
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"annotations": {},
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"description": "Read entries from a Redis stream using a consumer group.\n\nArgs:\n key (str): The stream key.\n group_name (str): The consumer group name.\n consumer_name (str): The consumer name.\n count (int, optional): Maximum number of entries to retrieve.\n block_ms (int, optional): Maximum time to block waiting for entries.\n Use None for a non-blocking read. 0 is rejected because Redis treats\n BLOCK 0 as an indefinite wait.\n stream_id (str, optional): Stream ID to read from. Use \"\u003e\" for new messages.\n\nReturns:\n str: The retrieved stream entries or an error message.\n",
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"inputSchema": {
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"properties": {
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"block_ms": {
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"anyOf": [
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{
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"type": "integer"
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{
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"type": "null"
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}
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],
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"default": null,
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"title": "Block Ms"
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},
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"consumer_name": {
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"title": "Consumer Name",
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"type": "string"
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},
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"count": {
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"default": 1,
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"title": "Count",
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"type": "integer"
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},
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"group_name": {
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"title": "Group Name",
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"type": "string"
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"key": {
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"title": "Key",
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"type": "string"
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"stream_id": {
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"default": "\u003e",
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"title": "Stream Id",
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"type": "string"
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}
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},
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"required": [
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"key",
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"group_name",
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"consumer_name"
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],
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"type": "object"
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},
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"name": "xreadgroup"
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{
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"annotations": {},
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"description": "Add a member to a Redis sorted set with an optional expiration time.\n\nArgs:\n key (str): The sorted set key.\n score (float): The score of the member.\n member (str): The member to add.\n expiration (int, optional): Expiration time in seconds.\n\nReturns:\n str: Confirmation message or an error message.\n",
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"hget",
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"hgetall",
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"hset",
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"hybrid_search",
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"info",
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"json_del",
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"json_get",
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"type",
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"unsubscribe",
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"vector_search_hash",
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"xack",
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"xadd",
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"xdel",
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"xgroup_create",
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"xgroup_destroy",
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"xrange",
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"xreadgroup",
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"zadd",
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"zrem"

registries/toolhive/servers/cloudflare-remote/server.json

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"license": "Apache-2.0"
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},
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"metadata": {
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"last_updated": "2026-04-08T03:03:51Z",
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"stars": 326
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"last_updated": "2026-05-29T03:21:48Z",
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"stars": 484
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},
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"overview": "## Cloudflare API (Remote)\n\nThe Cloudflare API remote MCP server is Cloudflare's official managed Model Context Protocol server providing token-efficient access to the entire Cloudflare API. Powered by Code Mode, it exposes over 2,500 API endpoints across Workers, KV, R2, D1, Pages, DNS, Firewall, Load Balancers, Stream, Images, AI Gateway, Vectorize, Access, Zero Trust, and more through just two tools in approximately 1,000 tokens. It supports OAuth and API token authentication via Streamable HTTP transport.",
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"status": "Active",

registries/toolhive/servers/datadog-remote/server.json

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"license": "Proprietary"
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"last_updated": "2026-05-22T03:20:52Z",
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"stars": 38
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},
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"overview": "## Datadog (Remote)\n\nThe Datadog remote MCP server is Datadog's official managed Model Context Protocol server that connects AI agents to Datadog's observability platform. It provides structured access to logs, metrics, traces, dashboards, monitors, incidents, hosts, services, events, and notebooks through toolsets that can be selectively enabled. The server uses Streamable HTTP transport with API key and application key authentication, and supports regional endpoints for different Datadog sites.",
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"status": "Active",

registries/toolhive/servers/google-maps-remote/server.json

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"metadata": {
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"stars": 4134
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},
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"overview": "## Google Maps Grounding Lite (Remote)\n\nThe Google Maps Grounding Lite remote MCP server is Google's official managed Model Context Protocol server providing access to Google Maps Platform geospatial data. It offers three tools for place search with summaries and Google Maps links, weather lookups with current conditions and forecasts, and route computation with distance and duration data. Authentication requires a Google Maps API key passed via the X-Goog-Api-Key header. The server is currently experimental and available at no charge during the pre-GA preview period.",
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"status": "Active",

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