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telota

bbaw-dse-mcp

by telota

cs_search_for_wikidata_occupation

Find Wikidata IDs for occupations to filter correspondence by profession. Search returns occupation labels, descriptions, and URIs.

Instructions

Search for occupation entities in Wikidata.

PURPOSE: Find Wikidata IDs for occupations to filter correspondence by profession.

WHEN TO USE:

  • When you need a Wikidata ID for correspSearch occupation filtering

  • To find letters by correspondent profession (writers, philosophers, etc.)

  • For professional network analysis

WHEN NOT TO USE:

  • For person names → use search_for_gnd_id()

  • For places → use search_for_geonames_id()

  • For non-occupation entities → use search_wikidata_entity()

Args: occupation_query: Occupation name to search for (e.g., "Dichter", "Philosoph", "Maler") limit: Maximum number of results to return

Returns: List of dicts with occupation entities: - id: Wikidata ID (e.g., "Q36180") - label: Occupation name in German - description: Brief description - uri: Full Wikidata URI

Example: >>> results = await search_for_wikidata_occupation("Schriftsteller") >>> occupation_id = results[0]['id'] # e.g., "Q36180" >>> letters = await search_correspondences(occupation_wikidata=occupation_id)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
occupation_queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the return format (list of dicts with id, label, description, uri) and provides a usage example. It does not explicitly mention side effects or rate limits, but 'search' implies a read-only operation. Minor gap: no explicit statement that it does not modify data or require authentication, but the example and return details give good transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-organized with clear sections (PURPOSE, WHEN TO USE, WHEN NOT TO USE, Args, Returns, Example). It is front-loaded and every section adds value. The example is concise and informative. No redundant or vague wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple (a search with two parameters) and the description covers purpose, usage, parameters, return format, and an example. Although an output schema exists, the description independently documents the return structure, which is valuable given the output schema was not provided in the prompt. It also differentiates from the large sibling tool set.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully explain parameters. It does: occupation_query is described as an occupation name with examples (e.g., 'Dichter', 'Philosoph', 'Maler'), and limit is described as the maximum number of results. This fully compensates for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches for occupation entities in Wikidata, with the specific purpose of finding Wikidata IDs for filtering correspondence by profession. It distinguishes itself from sibling tools by explicitly naming alternatives for person names, places, and non-occupation entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'WHEN TO USE' section lists concrete use cases (e.g., finding occupation IDs for correspSearch filtering, professional network analysis), and the 'WHEN NOT TO USE' section explicitly names sibling tools with clear exclusions. This provides strong guidance on tool selection.

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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