Field-by-field comparison

Compare the evidence before choosing an MCP.

Each signal stays separate, missing facts remain visible, and the column order follows your selection.

MCPs in this comparison

Kagi Search MCPHugging Face MCP

Uncheck an MCP to remove it. Comparing two or three keeps each signal in its own row.

Comparable alternatives

Azure MCP ServerMCP100 RecommendedChrome DevTools MCPMCP100 RecommendedContext7 MCPMCP100 RecommendedGrafana Cloud MCPMCP100 RecommendedGoogle MCP Toolbox for DatabasesMCP100 RecommendedGoogle Search Console MCP Server for SEOsMCP100 Recommended

Shown only when capability or industry context overlaps.

Side-by-side MCP evidence comparison
Evidence field	Kagi Search MCPMCP100 Recommended	Hugging Face MCPMCP100 Recommended
Task fit
Best for	Web/news/media search with Kagi's index Fetching page content as Markdown through a focused two-tool surface	Searching models, datasets, Spaces, papers, and Hugging Face documentation Exploring Hub metadata from an MCP-compatible assistant Running explicitly selected Hugging Face tools in controlled research workflows
Not ideal for	Teams that require OAuth instead of bearer API keys Workloads that need browser automation or site mutation	Highly regulated data or confidential prompts sent to unreviewed community Spaces Unattended Jobs, sandbox execution, or repository writes
Avoid when	You cannot safely store or pass a Kagi API key You need a broader crawl/browser automation toolset	Dynamic community Space discovery is enabled without tool-by-tool review The Hugging Face token grants more repository or compute access than needed Model, dataset, or Space content can directly trigger privileged tools
Provenance
Provenance details	First-party MCP reviewed-detail:kagi-search-mcp · Publisher source ↗	First-party MCP Hugging Face · Publisher source ↗
Maintenance
Maintenance details	Repo: Jul 7, 2026 Package: Not documented	Repo: Sep 23, 2026 Package: Not documented
Popularity evidence
GitHub stars	527 GitHub stars · checked 2026-09-24T04:14:47.715000Z	296 GitHub stars · checked 2026-09-24T04:14:16.295000Z
Package downloads / 30 days	4,745 package downloads / 30 days · checked 2026-09-24T04:18:14.564000Z	No reliable download data
Client coverage
Client coverage details	Not documented	Claude Code✓ Streamable HTTP Claude Desktop✓ Streamable HTTP Codex✓ Streamable HTTP VS Code✓ Streamable HTTP Cursor✓ Streamable HTTP OpenCode✓ Streamable HTTP
Client coverage note	Local stdio runs on your computer. Streamable HTTP connects to a remotely hosted MCP server.
Access and data
Authentication	Hosted and local modes use a Kagi API key; hosted HTTP uses Authorization: Bearer <key>. OAuth2 is not supported yet.	The hosted server supports the current documented Hugging Face OAuth or bearer-token paths. Tool advertisement or discovery configuration is not a server-side authorization boundary for a caller that already knows a tool name; actual authority remains bounded by the authenticated Hugging Face account and token permissions.
Cost	Kagi API billing is separate from a Kagi subscription and depends on API usage.	Hub search and the open-source server have no separately documented MCP fee. Hugging Face Jobs, sandboxes, inference, storage, hardware-backed Spaces, and the AI client can incur normal account charges.
Permissions	The MCP exposes Kagi search and page extraction only; it does not provide browser-control or general write actions.	Base tools search and navigate Hugging Face Hub resources and documentation. Optional tools can create and write repositories, manage sandbox files, run or schedule Jobs, and invoke selected or dynamically discovered community Spaces, bounded by the authenticated Hugging Face token and configured tool set.
Data handling	Search queries and extraction URLs are sent to Kagi; returned web content should be treated as untrusted external data.	Queries and results pass through Hugging Face's hosted MCP service. Community Space calls send their arguments to the selected Space and return its outputs to the client. Jobs and sandboxes can process files and compute inputs on Hugging Face infrastructure.
Limitations
Tradeoffs	Small tool surface reduces complexity, but it is narrower than full web-automation MCPs Hosted and self-hosted modes still send search/extraction requests to Kagi APIs	The hosted endpoint makes Hub discovery easy, while optional community Gradio tools have independent publishers, behavior, and data handling. Built-in Jobs, sandboxes, and repository contribution tools add useful compute and write capabilities but materially increase cost and impact. Tool selection is configurable in Hugging Face MCP settings and should remain minimal.
Risk context	Medium. Main concerns are API-key handling, query privacy, untrusted web content, and cost/rate usage rather than destructive side effects.	Begin with search-only tools and a least-privilege token. Disable dynamic Spaces, repository writes, Jobs, and sandboxes unless separately approved; review every community Space as third-party code and never send secrets or regulated data. This documentary review does not claim MCP100 execution testing.
Evidence date
Editorial review	2026-09-11	2026-07-24
Candidate evidence	2026-09-24T04:25:05.554391Z	2026-09-24T04:25:05.554391Z

Popularity, maintenance, fit, permissions, and client support are independent evidence fields.

Read the evidence method →