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Metis · DHIS2 Expert — DHIS2 Query

dhis2_query

Make authenticated API calls to a DHIS2 instance for live metadata validation, data element lookup, indicator queries, and data quality checks.

Instructions

Make an authenticated API call to the configured DHIS2 instance.

Returns the JSON response as formatted text. Use for live metadata
validation, data element lookup, indicator queries, and data quality checks.

Args:
    endpoint: API path relative to /api/, e.g. "dataElements" or "organisationUnits.json".
              If it does not start with "/api/", that prefix is added automatically.
    params:   Query parameters as a dict, e.g. {"fields": "id,name", "paging": "false"}.
    method:   HTTP method — "GET" (default), "POST", or "PUT".
    body:     Request body for POST/PUT (serialised to JSON).

Examples:
    dhis2_query("dataElements", {"fields": "id,name,valueType", "paging": "false"})
    dhis2_query("system/info")
    dhis2_query("organisationUnits", {"filter": "level:eq:2", "fields": "id,name,level"})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNo
methodNoGET
paramsNo
endpointYes

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 full burden. It discloses that the call is authenticated, automatically prefixes '/api/', and allows GET/POST/PUT with a body. This clarifies the tool's behavior beyond a simple query, including mutation potential. However, it does not mention rate limits or error handling, which would be beneficial.

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 well-structured with clear sections (Args, Examples) and a concise first sentence that captures the core purpose. While slightly longer than minimal, every sentence adds value. No redundant content.

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?

Given the presence of an output schema (implied), the description need not detail return values. It covers parameter details, usage context, and provides examples, making it self-contained for an agent to understand and invoke the tool correctly.

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%, but the description compensates fully with detailed parameter explanations and examples. It explains 'endpoint' format, 'params' as a dict with examples, 'method' default and options, and 'body' usage. The examples further clarify common usage patterns, making parameter semantics very clear.

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 makes an authenticated API call to a DHIS2 instance and returns JSON. It lists specific use cases (metadata validation, data element lookup, etc.), which distinguishes it from generic API tools. The verb 'Make' and resource 'API call to the configured DHIS2 instance' make the purpose unambiguous.

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

Usage Guidelines3/5

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

The description suggests appropriate use cases ('Use for live metadata validation...') but does not explicitly state when to avoid this tool or compare it to alternatives like the sibling tool 'dhis2_metadata'. This lack of exclusion criteria or alternative guidance limits its usefulness for 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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