Kagi MCP Server vs Linear MCP Server

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Kagi MCP Server MCP Servers	Linear MCP Server MCP Servers
Tagline	Official Kagi Search MCP server: search and extract the web from any MCP-compatible agent.	Open-source MCP server bridging LLM agents to Linear's issue tracker (now deprecated in favour of Linear's official remote MCP)
Category	MCP Servers	MCP Servers
Pricing	Paid· Free: $0 USD · Team: $4 USD per user/month · Enterprise: $21 USD per user/month	Free· Free and open source (MIT). Requires a Linear account and personal API key; Linear itself is free for small teams, $8/user/mo Standard, $14/user/mo Plus.
Model	—	—
Editorial score	—	—
Use cases	agent web search groundingClaude Code documentation lookupdeep-research pipelinesURL to Markdown extraction for RAGnews and video search from an LLMKagi lens-scoped queries in agentscitation gathering for research assistantscoding agent error-message lookups	Filing bugs from Claude Desktop or Cursor without leaving the editorAgent-driven triage of the inbox projectBulk relabeling or reprioritising issues via natural languageStandup summaries of a user's assigned Linear workAdding comments to tickets from a coding agent's PR reviewReference implementation for building a custom Linear MCP server
Pros	Official, first-party MCP server maintained by Kagi (not a community wrapper) Two clean tools that cover the 90% agent case: search and full-page Markdown extraction Both hosted (mcp.kagi.com) and local (uvx/Docker) deployment modes Documented setup recipes for Claude Desktop, Claude Code, Codex CLI, Kiro, OpenCode, Smithery MIT-licensed Python code, easy to fork or audit Rides Kagi's ad-free, spam-de-boosted index rather than generic SERP APIs Supports Kagi lenses and filters so agents can scope searches to news, docs, etc.	Open source (MIT) and small enough to read end-to-end in an afternoon One-line install via Smithery or straight npx in Claude Desktop config Covers the create / update / search / comment loop that 90% of ticket workflows need Works with any MCP client, not just Claude Desktop (Cursor, Zed, Continue, custom agents) Clear tool schemas make it a good reference implementation for building your own MCP servers Uses Linear's official GraphQL API, so permissions and audit trails stay intact
Cons	Requires a paid Kagi API key with pay-per-use billing; not usable free FastGPT and Universal Summarizer tools are currently absent (rewrite in progress) Python + uv toolchain assumption raises the bar vs. a single-binary MCP server Small surface area: no built-in caching, batching, or spend controls beyond Kagi's invoice cap Hosted endpoint means your queries and Bearer token traverse Kagi infra, not a concern for everyone but worth noting for regulated workloads	Officially deprecated by its author in favour of Linear's own remote MCP server at mcp.linear.app/sse Runs as a local stdio process, so it has to be installed on every workstation and cannot be shared across a team Uses a personal API key, meaning every action shows up as the key owner rather than the actual LLM user Tool coverage is narrower than the official server: no cycles, projects, roadmaps, or workflow state management Error handling and rate-limit backoff are minimal; heavy agent loops can trip Linear's API limits
Website	github.com	github.com

Pick Kagi MCP Server if

✅ Official, first-party MCP server maintained by Kagi (not a community wrapper)
✅ Two clean tools that cover the 90% agent case: search and full-page Markdown extraction
✅ Both hosted (mcp.kagi.com) and local (uvx/Docker) deployment modes
✅ Documented setup recipes for Claude Desktop, Claude Code, Codex CLI, Kiro, OpenCode, Smithery

Pick Linear MCP Server if

✅ Open source (MIT) and small enough to read end-to-end in an afternoon
✅ One-line install via Smithery or straight npx in Claude Desktop config
✅ Covers the create / update / search / comment loop that 90% of ticket workflows need
✅ Works with any MCP client, not just Claude Desktop (Cursor, Zed, Continue, custom agents)

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