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kagi-session2api-mcp

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Python
Hybrid

Kagi Session2API MCP-Server

Ein kostenloser Kagi Search MCP-Server (Model Context Protocol) – greifen Sie über Sitzungs-Tokens auf die Kagi-Suche und den Zusammenfassungsdienst zu, ohne dass ein API-Schlüssel erforderlich ist. Funktioniert mit Claude Desktop, Cursor, Windsurf, Cline, Hermes und jedem MCP-kompatiblen KI-Coding-Assistenten/Agenten.

Englisch | Chinesische Dokumentation

⚠️ Warnung für Endbenutzer: Dieses Projekt verwendet Kagi-Sitzungs-Tokens, um auf Such- und Zusammenfassungsfunktionen ohne die offizielle kostenpflichtige API zuzugreifen. Dies kann gegen die Nutzungsbedingungen von Kagi verstoßen. Mögliche Konsequenzen sind die Sperrung des Kontos oder ein dauerhafter Ausschluss. Die Nutzung erfolgt auf eigenes Risiko. Die Autoren übernehmen keine Haftung für Maßnahmen, die Kagi gegen Konten ergreift.

⚠️ Sicherheitshinweis: Ihr Kagi-Sitzungs-Token bietet vollen Zugriff auf Ihr Konto. Behandeln Sie es wie ein Passwort. Geben Sie es niemals weiter und speichern Sie es nicht in der Versionsverwaltung.

Funktionen

  • Sitzungsbasierter Zugriff: Verwendet Kagi-Sitzungs-Tokens anstelle der offiziellen API ($25/1000 Abfragen) – komplett kostenlos

  • Multi-Token-Pool: Konfigurieren Sie mehrere Tokens für einen höheren Durchsatz mit Round-Robin-Rotation

  • Ratenbegrenzung pro Token: 5 Anfragen/Sekunde pro Token mit Token-Bucket-Algorithmus

  • API-kompatible Ausgabe: Gibt Ergebnisse im gleichen Format wie das offizielle kagimcp zurück

  • Zwei Tools: kagi_search_fetch (Websuche) und kagi_summarizer (URL-Zusammenfassung)

  • Firefox UA-Spoofing: Ahmt einen echten Firefox-Browser für den sitzungsbasierten Zugriff nach

  • Automatische Deaktivierung abgelaufener Tokens: Abgelaufene Tokens werden automatisch erkannt und übersprungen

  • Suchoperatoren: Unterstützt Kagi-Operatoren (site:, lang:, filetype: usw.)

Related MCP server: Kagi MCP Server

Kompatible Plattformen

Funktioniert mit jedem MCP-kompatiblen Client:

Schnellinstallation (für KI-Agenten)

Fügen Sie einfach diesen Prompt in Ihren KI-Agenten ein:

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.

Manuelle Installation

pip install kagi-session2api-mcp

Oder mit uvx:

uvx kagi-session2api-mcp

Konfiguration

Option 1: Umgebungsvariable (Einzelnes Token)

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

Option 2: Umgebungsvariable (Mehrere Tokens)

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

Option 3: Konfigurationsdatei (Empfohlen für mehrere Tokens)

Erstellen Sie ~/.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

Konfigurieren Sie dann:

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

Abrufen Ihres Sitzungs-Tokens

  1. Melden Sie sich bei kagi.com an

  2. Gehen Sie zu Einstellungen → Konto → Sitzungs-Link

  3. Kopieren Sie das Token aus der Sitzungs-URL: https://kagi.com/search?token={DIESER_TEIL}&q=test

  4. Verwenden Sie dieses Token in Ihrer Konfiguration

Verwendung

MCP-Tools

kagi_search_fetch

Durchsuchen Sie das Web mit Kagi:

Search for "Python async tutorial"

Unterstützt Kagi-Suchoperatoren:

  • site:github.com - Auf Domain beschränken

  • -site:reddit.com - Domain ausschließen

  • filetype:pdf - Dateityp-Filter

  • intitle:python - Titel-Filter

  • lang:zh - Sprachfilter

  • before:2024-01-01 / after:2024-01-01 - Datumsfilter

  • "exakte phrase" - Exakte Übereinstimmung

kagi_summarizer

Fassen Sie eine beliebige URL zusammen:

Summarize https://example.com/article

Optionen:

  • summary_type: "summary" (Fließtext) oder "takeaway" (Stichpunkte)

  • engine: "cecil" (Standard), "agnes", "daphne", "muriel"

  • target_language: Sprachcode (z. B. "DE")

⚠️ Der Zusammenfassungsdienst ist experimentell – er verwendet Kagis internen Endpunkt, der sich ändern kann.

Transportmodi

Stdio (Standard, für Claude Desktop):

kagi-session2api-mcp

HTTP (für Fernzugriff):

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

Architektur

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

Verhalten des Token-Pools

Konfiguration

Ratenbegrenzung

Effektive Rate

1 Token

5 Anfragen/s

5 Anfragen/s

2 Tokens

je 5 Anfragen/s

10 Anfragen/s

N Tokens

je 5 Anfragen/s

5×N Anfragen/s

Wenn ein Token abläuft (erkannt durch 401/403 oder Weiterleitung zum Login), wird es automatisch deaktiviert. Verbleibende Tokens bedienen weiterhin Anfragen.

Unterschiede zum offiziellen kagimcp

Aspekt

Offizielles kagimcp

kagi-session2api-mcp

Authentifizierung

API-Schlüssel ($25/1000)

Sitzungs-Token (kostenlos)

Such-Endpunkt

/api/v0/search

/html/search (HTML-Scraping)

Zusammenfassung

/api/v0/summarize

/mother/summary_labs (intern)

Ratenbegrenzung

Serverseitig

Clientseitig (Token-Bucket)

api_balance

Gibt Guthaben zurück

Immer null

Kosten

Kostenpflichtig

Kostenlos (nutzt bestehende Sitzung)

Risiken

  • Kagi könnte die HTML-Struktur ändern, wodurch der Parser beschädigt wird

  • Der sitzungsbasierte Zugriff könnte gegen die Nutzungsbedingungen von Kagi verstoßen

  • Eine Sperrung des Kontos oder ein dauerhafter Ausschluss ist möglich

  • Der Endpunkt für die Zusammenfassung ist intern und kann sich ohne Vorankündigung ändern

  • Die Nutzung erfolgt auf eigenes Risiko. Die Autoren übernehmen keine Haftung für Konsequenzen, einschließlich, aber nicht beschränkt auf Maßnahmen, die Kagi gegen Konten ergreift.

Lizenz

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
NameRequiredDescriptionDefault
queriesYesOne 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
limitNoMaximum number of results per query. Default: all available.

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

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
NameRequiredDescriptionDefault
urlYesA URL to a document to summarize.
summary_typeNoType of summary to produce. Options are 'summary' for paragraph prose and 'takeaway' for a bulleted list of key points.summary
target_languageNoDesired output language using language codes (e.g., 'EN' for English). If not specified, the document's original language influences the output.
engineNoSummarizer 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
NameRequiredDescription
resultYes

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.

  1. 2 tool updatesv0.2.1
    • First observedkagi_search_fetch
    • First observedkagi_summarizer
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.9/5.0

Scored across 2 tools

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
ResponsivenessSlow

Resources

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