DHLAB MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_textsA | Search for texts in the National Library's digital collection. Args: query: Search query string limit: Maximum number of results to return (default: 10) from_year: Start year for search period (optional) to_year: End year for search period (optional) media_type: Type of media to search. Options: 'digavis' (newspapers), 'digibok' (books), 'digitidsskrift' (journals). Default: 'digavis' Returns: JSON string containing search results with metadata |
| ngram_frequenciesB | Get word frequency trends over time using NGram analysis. Args: words: List of words to analyze corpus: Corpus type. Options: 'bok' (books), 'avis' (newspapers). Default: 'bok' from_year: Start year (default: 1810) to_year: End year (default: 2020) smooth: Smoothing parameter for the frequency curve (default: 1) Returns: JSON string containing frequency data over time |
| find_concordancesC | Find concordances (contexts) for a word in a specific document. Args: urn: URN identifier for the document word: Word to find concordances for window: Number of words before and after the match (default: 25) limit: Maximum number of concordances to return (default: 100) Returns: JSON string containing concordance results |
| word_concordanceA | Find concordances with structured output (no HTML formatting). Returns clean format with separate before/target/after fields instead of HTML-formatted text. This is useful for programmatic analysis where you need the matched word separated from context. Args: urn: URN identifier for the document word: Word to find concordances for window: Number of words before and after the match (default: 12, max: 24) limit: Maximum number of concordances to return (default: 100) Returns: JSON string containing structured concordance results with fields: - dhlabid: Document identifier - before: Text before the matched word - target: The matched word itself - after: Text after the matched word |
| find_collocationsB | Find collocations (words that appear near the target word) in a document. Args: urn: URN identifier for the document word: Target word to find collocations for window: Size of context window (default: 5) limit: Maximum number of collocations to return (default: 100) Returns: JSON string containing collocation statistics |
| lookup_word_formsC | Look up different forms of a Norwegian word. Args: word: The word to look up Returns: JSON string containing different word forms |
| lookup_word_lemmaC | Look up the lemma (base form) of a Norwegian word. Args: word: The word to look up Returns: JSON string containing lemma information |
| search_imagesB | Search for images in the National Library's digital collection. Args: query: Search query string limit: Maximum number of results (default: 10) from_year: Start year (optional) to_year: End year (optional) Returns: JSON string containing image search results with URLs |
| get_corpus_statisticsC | Get statistical information about a corpus of documents. Args: urns: List of URN identifiers for documents Returns: JSON string containing corpus statistics |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 9 tools
Most tools have distinct purposes, but find_concordances and word_concordance have significant overlap as both find concordances for words in documents, differing mainly in output format. This could cause confusion for an agent deciding which to use. Other tools like find_collocations, ngram_frequencies, and search functions are clearly differentiated.
All tool names follow a consistent snake_case pattern with clear verb_noun structure (e.g., find_collocations, lookup_word_forms, search_images). The naming is predictable and readable throughout the set, with no mixing of conventions or styles.
With 9 tools, this server is well-scoped for digital humanities text analysis. The count is appropriate, covering key operations like searching, concordancing, collocation analysis, and statistical queries without being overwhelming or too sparse for the domain.
The toolset covers core digital humanities workflows including text search, image search, concordancing, collocation analysis, and corpus statistics. Minor gaps exist, such as no direct document retrieval or metadata lookup tools, but agents can likely work around these using the provided search and analysis functions.