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marksverdhei

DHLAB MCP Server

by marksverdhei

lookup_word_forms

Find different grammatical forms of Norwegian words using the National Library of Norway's Digital Humanities Lab resources for linguistic analysis and research.

Instructions

Look up different forms of a Norwegian word.

Args: word: The word to look up

Returns: JSON string containing different word forms

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
wordYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function decorated with @mcp.tool(), implementing the lookup_word_forms tool. It takes a word, uses dhlab.WordForm to retrieve different forms, and returns them as JSON or an error message.
    @mcp.tool()
    def lookup_word_forms(word: str) -> str:
        """Look up different forms of a Norwegian word.
    
        Args:
            word: The word to look up
    
        Returns:
            JSON string containing different word forms
        """
        try:
            word_form = dhlab.WordForm(word)
    
            if hasattr(word_form, 'forms') and word_form.forms is not None:
                return word_form.forms.to_json(orient='records', force_ascii=False)
            return f"No forms found for word: {word}"
        except Exception as e:
            return f"Error looking up word forms: {str(e)}"
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return format ('JSON string containing different word forms'), which adds some context, but lacks details on error handling, rate limits, authentication needs, or what specific forms are included (e.g., inflections, derivations). This is a significant gap for a tool with no annotation coverage.

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 appropriately sized and front-loaded, with the core purpose stated first. The 'Args' and 'Returns' sections are structured but could be more integrated. There's no wasted text, though it could be slightly more polished for readability.

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 low complexity (one parameter) and the presence of an output schema (which handles return values), the description is somewhat complete but has gaps. It covers the basic purpose and return format but lacks behavioral details and usage guidelines, making it minimally viable but not fully helpful for an agent.

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 description adds minimal semantics beyond the input schema. It states 'word: The word to look up,' which clarifies the parameter's purpose but doesn't provide format details (e.g., case sensitivity, language variants) or examples. With 0% schema description coverage and only one parameter, this is adequate but not comprehensive, aligning with the baseline for low coverage.

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's purpose: 'Look up different forms of a Norwegian word.' It specifies the verb ('look up'), resource ('Norwegian word'), and scope ('different forms'). However, it doesn't explicitly differentiate from sibling tools like 'lookup_word_lemma', which might provide related but distinct functionality.

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. It doesn't mention sibling tools (e.g., 'lookup_word_lemma' for lemmas or 'find_collocations' for word combinations) or specify contexts where this tool is preferred. Usage is implied by the purpose but not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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