What can you do with this server?

This server provides free access to Kagi's search and summarization services using session tokens instead of a paid API key, compatible with any MCP client supporting stdio or HTTP transport. kagi_search_fetch — Perform one or more web searches simultaneously using Kagi, with results numbered continuously across queries. Supports advanced operators: * site: / -site: — include or exclude domains * filetype: / ext: — filter by file type * intitle: / inurl: — filter by title or URL content * lang: / loc: — filter by language or location * before: / after: — filter by date range * "exact phrase", +term, -term — precise term control * Optional limit parameter to control results per query kagi_summarizer — Summarize content from any URL (webpages, videos, documents, etc.) with configurable options: * Summary type: summary (prose) or takeaway (bullet points) * Engine: cecil (default), agnes, daphne, or muriel * Target language: Output in a specified language (e.g., EN) Token Management: Configure a pool of session tokens with round-robin rotation, per-token rate limiting, and automatic detection/disabling of expired tokens for increased throughput and reliability.

Which integrations are available for this server?

Provides tools for searching the web using Kagi and summarizing URLs via session tokens.

How do I use kagi-session2api-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@kagi-session2api-mcp search for quantum computing breakthroughs 2025" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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kagi-session2api-mcp

by KSroido

Overview Schema Related Servers Score Discussions

Python

Hybrid

Kagi Session2API MCP Server

Free Kagi Search MCP server — session tokens, no API key needed

PyPI version PyPI downloads GitHub stars GitHub license Python Version

English | 中文文档

Access Kagi search and summarizer via session tokens — completely free. Works with Claude Desktop, Cursor, Windsurf, Cline, Hermes and any MCP-compatible AI coding assistant/agent.

⭐ Star History

⚠️ EndUser Warning: This project uses Kagi session tokens to access search and summarizer features without the official paid API. This may violate Kagi's Terms of Service. Potential consequences include account suspension or permanent ban. Use at your own risk. The authors assume no liability for any account actions taken by Kagi.
⚠️ Security Warning: Your Kagi session token provides full account access. Treat it like a password. Never share it or commit it to version control.

Related MCP server: Kagi MCP Server

🌟 Features

Session-based access: Uses Kagi session tokens instead of the official API ($25/1000 queries) — completely free
Multi-token pool: Configure multiple tokens for higher throughput with round-robin rotation
Per-token rate limiting: 5 req/s per token with token bucket algorithm
API-compatible output: Returns results in the same format as the official kagimcp
Two tools: kagi_search_fetch (web search) and kagi_summarizer (URL summarization)
Firefox UA spoofing: Mimics a real Firefox browser for session-based access
Auto-disable expired tokens: Expired tokens are automatically detected and skipped
Search operators: Supports Kagi operators (site:, lang:, filetype:, etc.)

Compatible Platforms

Works with any MCP-compatible client:

Claude Desktop
Cursor
Windsurf
Cline
Hermes Agent
Any MCP client supporting stdio or HTTP transport

Quick Install (For AI Agents)

Just paste this prompt to your AI agent:

Install the Kagi Session2API MCP server from https://pypi.org/project/kagi-session2api-mcp/.
Then add it to my MCP:
`uvx kagi-session2api-mcp`
set env KAGI_SESSION_TOKEN to my token (ask me if you don't have it).
If I have multiple tokens, use KAGI_SESSION_TOKENS (comma-separated) instead,
or create a config file at ~/.config/kagi-session2api-mcp/config.toml with:
[kagi]
session_tokens = ["TOKEN_1", "TOKEN_2"]
summarizer_engine = "cecil"
[client]
timeout = 30
max_retries = 2
and set env KAGI_SESSION_CONFIG to that path.

Install Manually

pip install kagi-session2api-mcp

Or with uvx:

uvx kagi-session2api-mcp

Configuration

Option 1: Environment Variable (Single Token)

{
  "mcpServers": {
    "kagi-session": {
      "command": "uvx",
      "args": ["kagi-session2api-mcp"],
      "env": {
        "KAGI_SESSION_TOKEN": "YOUR_SESSION_TOKEN_HERE"
      }
    }
  }
}

Option 2: Environment Variable (Multiple Tokens)

{
  "mcpServers": {
    "kagi-session": {
      "command": "uvx",
      "args": ["kagi-session2api-mcp"],
      "env": {
        "KAGI_SESSION_TOKENS": "TOKEN_1,TOKEN_2,TOKEN_3"
      }
    }
  }
}

Option 3: Config File (Recommended for Multi-Token)

Create ~/.config/kagi-session2api-mcp/config.toml:

[kagi]
session_tokens = [
    "YOUR_TOKEN_1_HERE",
    "YOUR_TOKEN_2_HERE",
]

summarizer_engine = "cecil"

[client]
timeout = 30
max_retries = 2

Then configure:

{
  "mcpServers": {
    "kagi-session": {
      "command": "uvx",
      "args": ["kagi-session2api-mcp"],
      "env": {
        "KAGI_SESSION_CONFIG": "/path/to/config.toml"
      }
    }
  }
}

Getting Your Session Token

Log in to kagi.com
Go to Settings → Account → Session Link
Copy the token from the session URL: https://kagi.com/search?token={THIS_PART}&q=test
Use this token in your configuration

Usage

MCP Tools

`kagi_search_fetch`

Search the web using Kagi:

Search for "Python async tutorial"

Supports Kagi search operators:

site:github.com - Restrict to domain
-site:reddit.com - Exclude domain
filetype:pdf - File type filter
intitle:python - Title filter
lang:zh - Language filter
before:2024-01-01 / after:2024-01-01 - Date filters
"exact phrase" - Exact match

`kagi_summarizer`

Summarize any URL:

Summarize https://example.com/article

Options:

summary_type: "summary" (prose) or "takeaway" (bullet points)
engine: "cecil" (default), "agnes", "daphne", "muriel"
target_language: Language code (e.g., "EN")

⚠️ The summarizer is experimental — it uses Kagi's internal endpoint which may change.

Transport Modes

Stdio (default, for Claude Desktop):

kagi-session2api-mcp

HTTP (for remote access):

kagi-session2api-mcp --http --host 0.0.0.0 --port 8000

Architecture

MCP Client → FastMCP Server → TokenPool (round-robin) → httpx.AsyncClient → kagi.com
                                ↓
                          TokenBucket (5 req/s per token)
                                ↓
                          Auto-disable expired tokens

Token Pool Behavior

Config	Rate Limit	Effective Rate
1 token	5 req/s	5 req/s
2 tokens	5 req/s each	10 req/s
N tokens	5 req/s each	5×N req/s

When a token expires (detected via 401/403 or redirect to login), it is automatically disabled. Remaining tokens continue serving requests.

Differences from Official `kagimcp`

Aspect	Official `kagimcp`	`kagi-session2api-mcp`
Authentication	API key ($25/1000)	Session token (free)
Search endpoint	`/api/v0/search`	`/html/search` (HTML scraping)
Summarizer	`/api/v0/summarize`	`/mother/summary_labs` (internal)
Rate limiting	Server-side	Client-side (token bucket)
`api_balance`	Returns balance	Always `null`
Cost	Paid	Free (uses existing session)

Thank you!

Star this repo if you feel it useful, that would help me a lot :)

License

MIT

Available Tools

2 tools

kagi_search_fetchA

Fetch web results based on one or more queries using Kagi Search.

Use for general search and when the user explicitly tells you to 'fetch' results/information. Results are from all queries given. They are numbered continuously, so that a user may be able to refer to a result by a specific number.

ParametersJSON Schema

Name	Required	Description	Default
`queries`	Yes	One or more concise, keyword-focused search queries. Include essential context within each query for standalone use. Supports Kagi operators: site:, -site:, filetype:/ext:, intitle:, inurl:, lang:, loc:, before:, after:, "exact phrase", +term, -term
`limit`	No	Maximum number of results per query. Default: all available.

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.2/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions numbering and that results are from all queries, but fails to disclose other behavioral traits such as pagination, caching, or auth requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with three sentences that are front-loaded with purpose. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema, return values need not be explained. The description is adequate for basic usage, though it could mention rate limits or error handling for completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds meaning by specifying that queries should be concise and keyword-focused, with essential context. The limit parameter's default behavior is explained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it fetches web results using Kagi Search, for general search and when the user explicitly asks to 'fetch'. It distinguishes from the sibling tool kagi_summarizer by focusing on search results rather than summaries.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use (general search, explicit fetch) and notes that results from all queries are numbered continuously. While it doesn't explicitly state when not to use, the sibling context implies summarization is separate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

kagi_summarizerA

Summarize content from a URL using the Kagi Summarizer.

The Summarizer can summarize any document type (text webpage, video, audio, etc.)

Note: This tool uses Kagi's internal summarizer endpoint accessed via session token. This is experimental and may break if Kagi changes their internal API.

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes	A URL to a document to summarize.
`summary_type`	No	Type of summary to produce. Options are 'summary' for paragraph prose and 'takeaway' for a bulleted list of key points.	summary
`target_language`	No	Desired output language using language codes (e.g., 'EN' for English). If not specified, the document's original language influences the output.
`engine`	No	Summarizer engine to use. 'cecil' is the default. Note: This is an experimental feature — the summarizer endpoint may change without notice.	cecil

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full responsibility for behavioral disclosure. It clearly states the experimental nature, potential breakage, and support for various content types. It does not detail error handling or rate limits, but the explicit warning about instability adds significant transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and structured: a clear purpose statement, a brief capability note, and a critical caution. Each sentence serves a purpose, though the capability note could be integrated into the first sentence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's 4 parameters and existing output schema (not shown), the description provides enough context to understand the tool's function and risks. However, it lacks guidance on error handling, prerequisites (e.g., need for a valid session token), and typical usage scenarios, which would enhance completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, so the baseline is 3. The description adds limited value, only restating the summary_type and engine options in a mildly explanatory way. It does not introduce new meaning beyond the schema's own descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool summarizes content from a URL using Kagi Summarizer, including support for various document types. It distinguishes from the sibling tool (kagi_search_fetch) by focusing on summarization rather than search, though no explicit comparison is made.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description warns that the tool is experimental and may break due to internal API changes, which provides important context. However, it does not specify when to use this tool over alternatives or when not to use it, leaving the agent to infer from the sibling tool's purpose.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

2 tool updatesv0.2.1May 17, 2026
- First observedkagi_search_fetch
- First observedkagi_summarizer

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.9/5.0

Disambiguation5/5

The two tools have clearly distinct purposes: one fetches search results, the other summarizes content from URLs. There is no overlap or ambiguity in their functionality.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with the 'kagi_' prefix ('kagi_search_fetch', 'kagi_summarizer'), making the naming predictable and understandable.

Tool Count3/5

With only 2 tools, the server feels minimal but still reasonable for its narrow focus on Kagi search and summarizer APIs. The experimental nature of the summarizer tool might warrant additional tools in the future.

Completeness4/5

The tool surface covers the two core operations of the Kagi API: search and summarization. While there are no additional utilities like listing or filtering, the basic workflow is supported without obvious dead ends.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

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Kagi Session2API MCP Server

⭐ Star History

🌟 Features

Compatible Platforms

Quick Install (For AI Agents)

Install Manually

Configuration

Option 1: Environment Variable (Single Token)

Option 2: Environment Variable (Multiple Tokens)

Option 3: Config File (Recommended for Multi-Token)

Getting Your Session Token

Usage

MCP Tools

kagi_search_fetch

kagi_summarizer

Transport Modes

Architecture

Token Pool Behavior

Differences from Official kagimcp

Thank you!

License

Available Tools

Output Schema

TDQS

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

kagi-kan-mcp

Kagi MCP Server

Search MCP Server

kagi-server

Latest Blog Posts

MCP directory API

`kagi_search_fetch`

`kagi_summarizer`

Differences from Official `kagimcp`