{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-kagi-search","slug":"kagi-search","name":"Kagi Search","type":"mcp","url":"https://github.com/kagisearch/kagimcp","page_url":"https://unfragile.ai/kagi-search","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-kagi-search__cap_0","uri":"capability://search.retrieval.web.search.via.kagi.api","name":"web-search-via-kagi-api","description":"Executes web searches through Kagi's proprietary search API, returning ranked results with titles, snippets, and URLs. The MCP server translates search queries into Kagi API calls, handling authentication via API keys and formatting responses into structured JSON that Claude or other MCP clients can consume. Implements request batching and result pagination to support both single queries and multi-step search workflows.","intents":["I need to search the web from within an AI agent without using Google/Bing APIs","I want to integrate Kagi search results into my Claude-based application","I need to perform fact-checking or research tasks by querying current web data","I want to build a search-augmented LLM agent that can look up real-time information"],"best_for":["AI agent builders using Claude with MCP protocol","Teams building search-augmented LLM applications","Developers wanting privacy-focused search without Google tracking","Solo developers prototyping research assistants or fact-checking bots"],"limitations":["Requires active Kagi subscription or API credits — free tier may have rate limits","Search results are only as current as Kagi's index refresh cycle (typically hours to days old)","No built-in result filtering by date, domain, or content type — requires post-processing","MCP protocol overhead adds ~100-200ms latency per search request vs direct API calls","Limited to text-based queries — no image search or advanced query syntax beyond Kagi's native support"],"requires":["Kagi API key (obtain from https://kagi.com/settings?p=api)","MCP-compatible client (Claude Desktop, custom MCP host, or compatible LLM framework)","Network connectivity to Kagi's API endpoints","Python 3.8+ or Node.js 16+ (depending on MCP server implementation)"],"input_types":["text (search query string)","optional query parameters (limit, offset, language)"],"output_types":["structured JSON with search results array","fields: title, snippet, url, domain, rank"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kagi-search__cap_1","uri":"capability://tool.use.integration.mcp.protocol.search.tool.binding","name":"mcp-protocol-search-tool-binding","description":"Wraps Kagi search functionality as an MCP-compliant tool that Claude and other MCP clients can discover and invoke through the Model Context Protocol. The server exposes search as a callable function with schema validation, parameter marshalling, and response serialization following MCP's tool definition standard. Handles tool discovery, schema advertisement, and request/response lifecycle management within the MCP message protocol.","intents":["I want Claude to automatically use web search when answering questions that require current information","I need to expose search as a discoverable tool in my MCP-compatible LLM application","I want to build multi-step agent workflows where search results feed into reasoning steps","I need standardized tool calling without writing custom API integration code"],"best_for":["Claude Desktop users extending Claude with web search capability","MCP framework developers building tool ecosystems","Teams standardizing on MCP for LLM tool integration","Developers building agentic systems that need declarative tool composition"],"limitations":["Tool invocation latency depends on MCP server startup and message serialization overhead","No built-in caching of search results — each query triggers a new API call","Tool schema is static — cannot dynamically adjust parameters based on query complexity","Requires MCP client support — not compatible with direct REST API consumers","No streaming results — entire search result set must be buffered before returning to client"],"requires":["MCP-compatible client (Claude Desktop 0.3.0+, or custom MCP host)","Kagi API key","MCP server runtime (Python or Node.js implementation)","Network access to both Kagi API and MCP client"],"input_types":["MCP tool call with query parameter (string)","optional limit parameter (integer)"],"output_types":["MCP tool result message","structured JSON array of search results"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kagi-search__cap_2","uri":"capability://tool.use.integration.kagi.api.authentication.and.rate.limiting","name":"kagi-api-authentication-and-rate-limiting","description":"Manages Kagi API authentication by storing and validating API keys, implementing request signing if required by Kagi's API, and enforcing rate limits to prevent quota exhaustion. The MCP server handles credential injection into outbound requests, token refresh if applicable, and graceful degradation when rate limits are exceeded. Implements exponential backoff for retries and tracks quota usage across multiple concurrent search requests.","intents":["I need to securely store my Kagi API key without exposing it in code or logs","I want to prevent my application from exceeding Kagi's rate limits and incurring overage charges","I need to handle API authentication failures gracefully without crashing the agent","I want to monitor API usage and quota consumption across multiple search requests"],"best_for":["Production deployments requiring secure credential management","Teams with multiple users sharing a single Kagi API key","Applications with unpredictable search volume needing rate-limit protection","Developers building cost-conscious agents that must track API spend"],"limitations":["API key must be provided via environment variable or config file — no runtime key injection","Rate limit enforcement is client-side only — server-side limits from Kagi may still be exceeded","No built-in quota alerting — requires external monitoring to detect approaching limits","Retry logic uses fixed exponential backoff — cannot adapt to Kagi's actual rate-limit headers","No support for API key rotation without restarting the MCP server"],"requires":["Kagi API key (stored in KAGI_API_KEY environment variable or config)","Network connectivity to Kagi's API","MCP server with credential management support"],"input_types":["API key (string, typically from environment)","search request (string query)"],"output_types":["authenticated API request","rate-limit status (headers or metadata)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kagi-search__cap_3","uri":"capability://data.processing.analysis.search.result.parsing.and.formatting","name":"search-result-parsing-and-formatting","description":"Parses Kagi API responses and transforms raw search results into a standardized JSON format suitable for LLM consumption. Extracts relevant fields (title, snippet, URL, domain), sanitizes HTML entities and special characters, truncates long snippets to fit context windows, and structures results as an array with consistent schema. Handles edge cases like missing fields, malformed responses, and encoding issues.","intents":["I need search results formatted consistently so Claude can reliably parse and reason about them","I want to truncate long snippets to fit within token budgets without losing semantic meaning","I need to extract domain information from URLs for source attribution","I want to handle malformed or incomplete search results gracefully without breaking the agent"],"best_for":["LLM agents that need to reason about search results in a structured format","Applications with strict token budgets requiring snippet truncation","Teams building search-augmented RAG systems needing consistent result schemas","Developers handling diverse or unreliable data sources"],"limitations":["Snippet truncation is naive (character-based) — may cut off mid-sentence or mid-word","No semantic understanding of snippet content — cannot identify key entities or facts","HTML entity decoding is basic — may not handle all Unicode edge cases","No deduplication of results — duplicate URLs may appear in different result positions","Domain extraction is regex-based — may fail on unusual URL formats or subdomains"],"requires":["Kagi API response (JSON format)","JSON parsing library (built into most languages)","Optional: HTML entity decoder library"],"input_types":["raw Kagi API response (JSON)"],"output_types":["structured JSON array","fields: title (string), snippet (string, truncated), url (string), domain (string), rank (integer)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kagi-search__cap_4","uri":"capability://planning.reasoning.multi.query.search.composition","name":"multi-query-search-composition","description":"Enables agents to compose multiple sequential or parallel search queries, where results from one query can inform subsequent queries. The MCP server maintains request context across multiple tool invocations, allowing agents to refine searches based on intermediate results. Implements query deduplication to avoid redundant API calls and result caching within a single agent session to reduce API usage and latency.","intents":["I want my agent to perform multi-step research by refining searches based on initial results","I need to search for related topics in parallel and synthesize results","I want to avoid duplicate API calls when the agent re-queries the same topic","I need to build complex research workflows that combine multiple search results"],"best_for":["Agentic systems performing multi-step research or fact-checking","Applications building comprehensive answers from multiple search queries","Teams building search-augmented reasoning systems","Developers optimizing API usage through intelligent caching"],"limitations":["Result caching is session-scoped only — no persistence across agent restarts","Query deduplication is exact-match only — similar but non-identical queries are not deduplicated","No built-in query optimization — agent must explicitly compose refined queries","Parallel search requests may exceed rate limits if not carefully managed","Cache invalidation is manual — stale results may be returned if search index updates"],"requires":["MCP server with session state management","Kagi API key with sufficient quota for multiple queries","Agent framework supporting sequential or parallel tool invocations"],"input_types":["multiple search queries (strings)","optional context from previous results"],"output_types":["aggregated search results from multiple queries","cache hit/miss metadata"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Kagi API key (obtain from https://kagi.com/settings?p=api)","MCP-compatible client (Claude Desktop, custom MCP host, or compatible LLM framework)","Network connectivity to Kagi's API endpoints","Python 3.8+ or Node.js 16+ (depending on MCP server implementation)","MCP-compatible client (Claude Desktop 0.3.0+, or custom MCP host)","Kagi API key","MCP server runtime (Python or Node.js implementation)","Network access to both Kagi API and MCP client","Kagi API key (stored in KAGI_API_KEY environment variable or config)","Network connectivity to Kagi's API"],"failure_modes":["Requires active Kagi subscription or API credits — free tier may have rate limits","Search results are only as current as Kagi's index refresh cycle (typically hours to days old)","No built-in result filtering by date, domain, or content type — requires post-processing","MCP protocol overhead adds ~100-200ms latency per search request vs direct API calls","Limited to text-based queries — no image search or advanced query syntax beyond Kagi's native support","Tool invocation latency depends on MCP server startup and message serialization overhead","No built-in caching of search results — each query triggers a new API call","Tool schema is static — cannot dynamically adjust parameters based on query complexity","Requires MCP client support — not compatible with direct REST API consumers","No streaming results — entire search result set must be buffered before returning to client","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=kagi-search","compare_url":"https://unfragile.ai/compare?artifact=kagi-search"}},"signature":"ImnxNxIuBp++bULT5LQ9F7wB3gLLddAM3TxSDmXJ8sXByg8hgckjsN3o0YjRiL/32SnYn1Xjzf7Xra+7m197BA==","signedAt":"2026-06-20T04:05:07.764Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kagi-search","artifact":"https://unfragile.ai/kagi-search","verify":"https://unfragile.ai/api/v1/verify?slug=kagi-search","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}