Dieses Projekt ist ein Python-Server, der das Model Context Protocol (MCP) für Zotero implementiert und Ihnen Zugriff auf Ihre Zotero-Bibliothek innerhalb von KI-Assistenten ermöglicht. Es soll eine kleine, aber äußerst nützliche Reihe von Interaktionen mit Zotero für die Verwendung mit MCP-Clients implementieren.
Dieser MCP-Server bietet die folgenden Tools:
zotero_search_items : Suchen Sie mithilfe einer Textabfrage nach Elementen in Ihrer Zotero-Bibliothek
zotero_item_metadata : Erhalten Sie detaillierte Metadateninformationen zu einem bestimmten Zotero-Element
zotero_item_fulltext : Holen Sie sich den vollständigen Text eines bestimmten Zotero-Elements (z. B. PDF-Inhalte).
Diese können über jeden MCP-Client oder über den MCP Inspector entdeckt und aufgerufen werden.
Jedes Tool gibt formatierten Text mit relevanten Informationen aus Ihren Zotero-Elementen zurück, und KI-Assistenten wie Claude können diese nacheinander verwenden, nach Elementen suchen und dann deren Metadaten oder Textinhalte abrufen.
Related MCP server: zotero-assistant-mcp
Dieser Server kann entweder über eine lokale API der Zotero-Desktopanwendung oder über die Zotero-Web-API ausgeführt werden. Die lokale API kann etwas schneller reagieren, erfordert aber, dass die Zotero-App auf demselben Computer läuft und die API aktiviert ist. Um die lokale API zu aktivieren, führen Sie die folgenden Schritte aus:
Öffnen Sie Zotero und öffnen Sie „Zotero-Einstellungen“
Aktivieren Sie auf der Registerkarte „Erweitert“ das Kontrollkästchen „Anderen Anwendungen auf diesem Computer erlauben, mit Zotero zu kommunizieren“.
[!WICHTIG] Für den Zugriff auf den
/fulltext-Endpunkt der lokalen API, der den vollständigen Inhalt Ihrer Bibliothek abruft, benötigen Sie eine Zotero-Betaversion (Stand: 30.03.2025). Nach Veröffentlichung von Version 7.1 ist dies nicht mehr erforderlich. Weitere Informationen finden Sie unter https://github.com/zotero/zotero/pull/5004 . Wenn Sie dies nicht möchten, verwenden Sie stattdessen die Web-API.
Um die Zotero Web API zu verwenden, müssen Sie einen API-Schlüssel erstellen und Ihre Bibliotheks-ID (normalerweise Ihre Benutzer-ID) in den Einstellungen Ihres Zotero-Kontos hier finden: https://www.zotero.org/settings/keys
Dies sind die verfügbaren Konfigurationsoptionen:
ZOTERO_LOCAL=true : Benutze die lokale Zotero-API (Standard: false, siehe Hinweis unten)
ZOTERO_API_KEY : Ihr Zotero-API-Schlüssel (für die lokale API nicht erforderlich)
ZOTERO_LIBRARY_ID : Ihre Zotero-Bibliotheks-ID (Ihre Benutzer-ID für Benutzerbibliotheken, für die lokale API nicht erforderlich)
ZOTERO_LIBRARY_TYPE : Der Typ der Bibliothek (Benutzer oder Gruppe, Standard: Benutzer)
uvx mit lokaler Zotero-APIUm dies mit Claude Desktop und einer direkten Python-Installation mit uvx zu verwenden, fügen Sie der mcpServers -Konfiguration Folgendes hinzu:
{
"mcpServers": {
"zotero": {
"command": "uvx",
"args": ["--upgrade", "zotero-mcp"],
"env": {
"ZOTERO_LOCAL": "true",
"ZOTERO_API_KEY": "",
"ZOTERO_LIBRARY_ID": ""
}
}
}
}Das Flag --upgrade ist optional und lädt die neueste Version herunter, sobald neue verfügbar sind. Wenn Sie uvx nicht installiert haben, können Sie stattdessen pipx run verwenden oder dieses Repository lokal klonen und die Anweisungen unter „Entwicklung“ unten befolgen.
Wenn Sie diesen MCP-Server in einem Docker-Container ausführen möchten, können Sie die folgende Konfiguration verwenden und Ihren API-Schlüssel und Ihre Bibliotheks-ID einfügen:
{
"mcpServers": {
"zotero": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-e", "ZOTERO_API_KEY=PLACEHOLDER",
"-e", "ZOTERO_LIBRARY_ID=PLACEHOLDER",
"ghcr.io/kujenga/zotero-mcp:main"
],
}
}
}Um auf eine neuere Version zu aktualisieren, führen Sie docker pull ghcr.io/kujenga/zotero-mcp:main aus. Sie können auch die Docker-basierte Installation verwenden, um mit der lokalen Zotero-API zu kommunizieren. Dazu müssen Sie jedoch den obigen Befehl anpassen, um sicherzustellen, dass eine Netzwerkverbindung zur lokalen API-Schnittstelle der Zotero-Anwendung besteht.
Informationen zum Vornehmen von Änderungen und zum Mitwirken am Projekt.
Dieses Repository klonen
Installieren Sie Abhängigkeiten mit uv , indem Sie Folgendes ausführen: uv sync
Erstellen Sie im Projektstamm eine .env Datei mit den oben genannten Umgebungsvariablen
Starten Sie den MCP Inspector für die lokale Entwicklung:
npx @modelcontextprotocol/inspector uv run zotero-mcpUm das lokale Repository mit Claude Desktop zu testen, führen Sie echo $PWD/.venv/bin/zotero-mcp in Ihrer Shell in diesem Verzeichnis aus und legen Sie dann Folgendes in Ihrer Claude Desktop-Konfiguration fest
{
"mcpServers": {
"zotero": {
"command": "/path/to/zotero-mcp/.venv/bin/zotero-mcp"
"env": {
// Whatever configuration is desired.
}
}
}
}So führen Sie die Testsuite aus:
uv run pytestErstellen Sie das Container-Image mit diesem Befehl:
docker build . -t zotero-mcp:localUm den Container mit dem MCP-Inspector zu testen, führen Sie den folgenden Befehl aus:
npx @modelcontextprotocol/inspector \
-e ZOTERO_API_KEY=$ZOTERO_API_KEY \
-e ZOTERO_LIBRARY_ID=$ZOTERO_LIBRARY_ID \
docker run --rm -i \
--env ZOTERO_API_KEY \
--env ZOTERO_LIBRARY_ID \
zotero-mcp:localGet the full text content of a Zotero item, given the item key of a parent item or specific attachment.
| Name | Required | Description | Default |
|---|---|---|---|
| item_key | Yes |
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 states the tool 'Get[s] the full text content', implying a read-only operation, but doesn't disclose behavioral traits such as authentication needs, rate limits, error conditions, or what happens if the item key is invalid. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: a single, clear sentence that states the purpose and parameter context without any wasted words. Every part of the sentence earns its place by conveying essential information efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameter semantics but lacks details on behavioral aspects, output format, or error handling. Without annotations or an output schema, the description should do more to be complete, but it meets the bare minimum for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 1 parameter with 0% description coverage, so the description must compensate. It adds meaning by explaining that 'item_key' refers to 'a parent item or specific attachment', which clarifies the parameter's purpose beyond the schema's generic 'Item Key' title. However, it doesn't provide details on format, examples, or constraints, leaving some ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the full text content of a Zotero item' with the specific verb 'Get' and resource 'full text content'. It distinguishes from sibling tools like 'zotero_item_metadata' (which likely returns metadata) and 'zotero_search_items' (which searches for items). However, it doesn't explicitly contrast with siblings, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying 'given the item key of a parent item or specific attachment', which provides some context on when to use it. However, it lacks explicit guidance on when to use this tool versus alternatives like 'zotero_item_metadata' for non-full-text data or 'zotero_search_items' for finding items first. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get metadata information about a specific Zotero item, given the item key.
| Name | Required | Description | Default |
|---|---|---|---|
| item_key | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it's a read operation ('Get'), but doesn't mention whether it requires authentication, rate limits, error conditions (e.g., invalid item keys), or the format of returned metadata. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get metadata information') and includes the key constraint ('given the item key'). There is no wasted text, repetition, or unnecessary elaboration, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no nested objects) but lack of annotations and output schema, the description is minimally complete. It covers the purpose and parameter semantics adequately but misses behavioral details like authentication needs or return format. Without an output schema, the description should ideally hint at what metadata is returned, which it doesn't, leaving room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning by explaining that 'item_key' is used to identify 'a specific Zotero item', which clarifies the parameter's role beyond the schema's generic 'Item Key' title. With 0% schema description coverage and only one parameter, this compensates adequately by providing context, though it doesn't detail the key's format or source. Baseline is high due to low parameter count.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'metadata information about a specific Zotero item', making the purpose immediately understandable. It distinguishes from 'zotero_item_fulltext' (which likely retrieves full text content) and 'zotero_search_items' (which searches multiple items) by focusing on metadata retrieval for a single item. However, it doesn't explicitly mention what metadata fields are included, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying 'given the item key', suggesting this tool is for when you have a specific item identifier. It doesn't provide explicit when-to-use guidance versus alternatives like 'zotero_search_items' (e.g., use this for known items, use search for unknown items) or mention prerequisites like authentication. The context is clear but lacks detailed exclusions or comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for items in your Zotero library, given a query string, query mode (titleCreatorYear or everything), and optional tag search (supports boolean searches). Returned results can be looked up with zotero_item_fulltext or zotero_item_metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| qmode | No | titleCreatorYear | |
| tag | No | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It describes the search functionality and mentions that results can be looked up with other tools, which adds useful context. However, it doesn't disclose important behavioral traits like whether this is a read-only operation, what permissions are needed, pagination behavior beyond the 'limit' parameter, or error conditions. The description adds some value but leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: the first explains the core functionality with key parameters, the second provides important follow-up context about sibling tools. Every word earns its place with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 4 parameters, 0% schema coverage, no annotations, and no output schema, the description does a reasonable job explaining the search purpose and parameters. However, it lacks information about return format, error handling, authentication requirements, and doesn't fully document all parameters (missing 'limit'). Given the complexity and lack of structured documentation, this leaves significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate. It explains the purpose of 'query', 'qmode' (with specific mode examples), and 'tag' (including boolean search support). It doesn't mention the 'limit' parameter, but covers 3 of 4 parameters with meaningful context beyond their names. This significantly improves understanding compared to the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Search for items'), resource ('in your Zotero library'), and scope ('given a query string, query mode... and optional tag search'). It distinguishes from siblings by mentioning that results can be looked up with 'zotero_item_fulltext' or 'zotero_item_metadata', indicating this is a search tool while siblings provide detailed item data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (searching the library with query parameters) and implicitly distinguishes from siblings by noting that results can be looked up with those tools. However, it doesn't explicitly state when NOT to use this tool or provide alternative search methods within the same tool family.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
zotero_item_fulltextzotero_item_metadatazotero_search_itemsScored across 3 tools
Each tool has a clearly distinct purpose: zotero_search_items finds items, zotero_item_metadata retrieves metadata for a specific item, and zotero_item_fulltext gets full text content. There is no overlap or ambiguity between these functions.
All tools follow a consistent 'zotero_item_*' pattern with snake_case, using descriptive suffixes (fulltext, metadata, search_items) that clearly indicate their specific actions. The naming is uniform and predictable.
With only 3 tools, the server feels thin for a Zotero library management domain. While the tools cover basic search and retrieval, typical library operations like creating, updating, or deleting items are missing, suggesting an incomplete surface.
The tool set is severely incomplete for Zotero library management. It only supports search and read operations (search, get metadata, get fulltext), with no ability to create, update, delete, or manage items, collections, or tags, which are core to the domain.
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