Skip to main content
Glama
kltng

LCSH MCP Server

by kltng

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

The easiest way to install the cataloger mcp server is directly from PyPI:

pip install cataloger-mcp-server

Option 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

  1. Install Claude Desktop if you haven't already from https://claude.ai/desktop

  2. Install the cataloger mcp server using one of the installation methods above

  3. Open Claude Desktop and navigate to Settings:

    • Click on your profile picture in the bottom-left corner

    • Select "Settings" from the menu

  4. Configure the MCP Server:

    • In the Settings panel, click on "MCP Servers"

    • Click "Add Server"

    • Fill in the following details:

      • Name: cataloger mcp search

      • Command: cataloger-mcp-server

    • Click "Save"

  5. 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

  1. Install Cherry Studio following the instructions in the Cherry documentation: https://docs.cherry-ai.com/docs/en-us/advanced-basic/mcp/install

  2. Install the cataloger mcp server using one of the installation methods above.

  3. 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.

  4. 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.

  5. Verify the server:

    • Ensure the new cataloger-mcp-server entry 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), and search_name_authority (for personal names) tools that can be used by AI assistants.

  • Resource Endpoints: Provides resource endpoints at lcsh://search/{query} and lcnaf://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:

  1. Check Server Status: In Claude Desktop, go to Settings > MCP Servers and check if the server is enabled and running

  2. Restart the Server: Toggle the server off and on again

  3. Check Console Output: If running the server manually, check the console output for any error messages

  4. 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 tool
search_lcshC

Search Library of Congress Subject Headings (LCSH) using the public suggest2 API. Returns a dictionary with the top results.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes

TDQS

C2.7/5.0
Behavior2/5

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.

Conciseness4/5

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.

Completeness2/5

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.

Parameters2/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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. 1 tool updatev1.0.0
    • First observedsearch_lcsh

TDQS

C2.9/5.0

Scored across 1 tool

Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness2/5

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

ActivityInactive
ResponsivenessNo issues

Related MCP Connectors

Related MCP Servers

  • A
    license
    A
    quality
    D
    maintenance
    Exposes CDISC standards data including SDTM, ADaM, CDASH, and Controlled Terminology as tools for AI assistants via the CDISC Library API. It enables users to search standards, retrieve domain variables, and access codelist definitions to facilitate clinical research data management.
    12
    MIT
  • A
    license
    A
    quality
    D
    maintenance
    Connects 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.
    4
    1
    GPL 3.0