LCSH MCP Server
The LCSH MCP Server allows you to search the Library of Congress Subject Headings (LCSH) through a simple API interface. With this server, you can:
Search LCSH using the public suggest2 API, returning top results based on your query
Integrate with AI assistants like Claude via the
search_lcshtoolAccess the resource endpoint at
lcsh://search/{query}for easy integrationBenefit from built-in error handling for API issues and unexpected response formats
Support multiple response formats (dictionary and list formats)
Easily set up and enable the server in Claude Desktop for seamless LCSH searches
Provides access to the source code repository for installation from source and development purposes.
Supports installation of the LCSH MCP server package directly from PyPI to simplify deployment.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@LCSH MCP Serversearch for 'artificial intelligence' in LCSH"
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.
cataloger mcp server
A Model Context Protocol (MCP) server that provides access to the Library of Congress Subject Headings (LCSH) through a simple API interface.
Overview
This MCP server allows AI assistants like Claude to search the Library of Congress Subject Headings (LCSH) using the public suggest2 API. It provides a clean interface for querying LCSH data and handling the various response formats from the API.
Related MCP server: MeSH MCP
Installation
Option 1: Install from PyPI (Recommended)
The easiest way to install the cataloger mcp server is directly from PyPI:
pip install cataloger-mcp-serverOption 2: Install from Source
If you prefer to install from source:
git clone https://github.com/kltng/cataloger-mcp-server.git
cd cataloger-mcp-server
pip install -e .Setting up with Claude Desktop
Install Claude Desktop if you haven't already from https://claude.ai/desktop
Install the cataloger mcp server using one of the installation methods above
Open Claude Desktop and navigate to Settings:
Click on your profile picture in the bottom-left corner
Select "Settings" from the menu
Configure the MCP Server:
In the Settings panel, click on "MCP Servers"
Click "Add Server"
Fill in the following details:
Name:
cataloger mcp searchCommand:
cataloger-mcp-server
Click "Save"
Enable the Server:
Toggle the switch next to the cataloger mcp search server to enable it
Claude will now have access to the LCSH search capabilities
Setting up with Cherry Studio
Install Cherry Studio following the instructions in the Cherry documentation: https://docs.cherry-ai.com/docs/en-us/advanced-basic/mcp/install
Install the cataloger mcp server using one of the installation methods above.
Prepare the MCP JSON config for Cherry Studio. Use the "Import JSON" method with a snippet like:
{ "mcpServers": { "cataloger-mcp": { "command": "uvx", "args": ["cataloger-mcp-server"] } } }You can change the key
"cataloger-mcp"to whatever label you prefer in Cherry Studio.
Import into Cherry Studio:
Open Cherry Studio and go to Settings → MCP (or MCP Servers).
Click Add / Import from JSON (wording may vary slightly by version).
Paste the JSON snippet above and confirm to add the server.
Verify the server:
Ensure the new
cataloger-mcp-serverentry appears in the MCP list and is enabled.Start a new chat and confirm that Cherry Studio can call the cataloger MCP tools.
Using the cataloger mcp server with Claude
Once the server is set up and enabled in Claude Desktop, you can ask Claude to search for Library of Congress Subject Headings. Here are some example prompts:
"Can you search the Library of Congress Subject Headings for 'artificial intelligence'?"
"Look up 'climate change' in LCSH and tell me the official subject headings."
"What are the LCSH terms related to 'quantum computing'?"
Claude will use the MCP server to query the LCSH and LCNAF databases and return the results.
"Find the name authority record for 'Smith, John Adam'."
"Perform a keyword search in LCSH for 'environmental policy'.
Features
MCP Tool Integration: Exposes
search_lcsh(for subject headings, default left-anchored search),search_lcsh_keyword(for subject headings, keyword search), andsearch_name_authority(for personal names) tools that can be used by AI assistants.Resource Endpoints: Provides resource endpoints at
lcsh://search/{query}andlcnaf://search/{query}.Robust Error Handling: Gracefully handles API errors, connection issues, and unexpected response formats
Multiple Response Formats: Supports both dictionary (hits) and list response formats from the LCSH API
Troubleshooting
If you encounter issues with the MCP server:
Check Server Status: In Claude Desktop, go to Settings > MCP Servers and check if the server is enabled and running
Restart the Server: Toggle the server off and on again
Check Console Output: If running the server manually, check the console output for any error messages
Verify Network Connection: Ensure your computer has an active internet connection to access the LCSH API
License
This project is licensed under the MIT License - see the LICENSE file for details.
For Developers
For more detailed documentation about the server implementation, API references, and testing information, please refer to the references.md file.
Available Tools
1 toolsearch_lcshC
Search Library of Congress Subject Headings (LCSH) using the public suggest2 API. Returns a dictionary with the top results.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the API source ('public suggest2 API') and return type ('dictionary with the top results'), but lacks details on error handling, rate limits, authentication needs, or what 'top results' entails (e.g., ranking criteria, number of results). This leaves behavioral gaps for a search tool.
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 concise with two sentences, front-loading the main action and resource. It avoids unnecessary words, though it could be slightly more structured (e.g., separating API details from return values). Every sentence contributes meaning, making it efficient.
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 complexity (a search operation with no annotations, 0% schema coverage, and no output schema), the description is incomplete. It omits parameter details, behavioral traits like error handling, and specifics on the return value (e.g., dictionary structure). This is inadequate for effective tool use.
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?
Schema description coverage is 0%, so the description must compensate. It does not mention the 'query' parameter at all, failing to explain its purpose, format, or constraints. The description adds no semantic value beyond what the bare schema provides, leaving the parameter undocumented.
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 action ('Search Library of Congress Subject Headings') and the resource (LCSH), with the specific verb 'search' and target 'LCSH'. It distinguishes itself by mentioning the 'public suggest2 API', though there are no sibling tools for comparison. The purpose is specific and unambiguous.
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 no guidance on when to use this tool versus alternatives, prerequisites, or exclusions. It mentions the 'public suggest2 API', but does not explain its context or limitations. With no sibling tools, this is less critical, but still lacks usage context.
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 tool update
v1.0.0- First observed
search_lcsh
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'search_lcsh' has a clearly defined and distinct purpose.
The naming follows a consistent verb_noun pattern with 'search_lcsh'. With only one tool, there is no inconsistency to evaluate, and the pattern is clear and appropriate.
A single tool is too few for a server focused on LCSH, as it lacks basic operations like browsing, filtering, or retrieving detailed subject information. This minimal set limits functionality and agent workflows.
The server is severely incomplete for LCSH operations; it only offers search without supporting actions like get_subject_details, list_subjects_by_category, or related term lookups. This creates significant gaps for agent tasks.
Maintenance
Related MCP Connectors
Curated knowledge API for AI agents - skill packs, semantic search, validated patterns.
Provides AI assistants with access to Seltz's powerful Web Search capabilities.
Search US grants + federal contracts (Grants.gov + SAM.gov) from any LLM.
Academic literature search, retrieval, and private library management on top of OpenAlex.
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- AlicenseAqualityDmaintenanceConnects Claude to the U.S. National Library of Medicine MeSH APIs to search and retrieve medical authority data, descriptors, and qualifiers. It enables library and metadata staff to perform subject analysis and confirm terminology within an AI-assisted cataloging workflow.41GPL 3.0
- FlicenseAqualityDmaintenanceConnects AI assistants to the Open Library API for searching books and authors, retrieving metadata, and comparing works.12-
- AlicenseNot gradedqualityCmaintenanceProvides access to the Library of Congress (loc.gov) data, enabling AI agents to search and retrieve information from the world's largest library.3 npmMIT