DHIS2 보건 데이터를 위한 읽기 전용 MCP 서버입니다. stdio에서 실행됩니다.
이 서버는 작은 로컬 모델을 위해 설계되었습니다. 각 도구는 원시 DHIS2 응답이 아닌 간결한 JSON을 반환합니다. 작은 컨텍스트 창은 곧 예산과 같으며, 도구들은 이 예산을 최대한 조금만 사용합니다.
도구 | 설명 |
| DHIS2 지표를 이름으로 검색합니다. id, name, description을 반환합니다. |
| 하나의 조직 단위에 대해, 한 지표의 기간별 집계 값을 가져옵니다. |
먼저 search_indicators를 실행하여 지표 id를 얻으세요. 그런 다음 그 id를 query_analytics에 전달하세요.
Related MCP server: WHO MCP Server
Node.js 20 이상.
DHIS2 인스턴스. 기본값은 공개 play 데모이므로 별도의 설정 없이 시작할 수 있습니다.
git clone https://github.com/brianmituka/dhis2-mcp.git
cd dhis2-mcp
npm install
npm run build다음 환경 변수를 설정하세요. 세 변수 모두 선택 사항입니다.
변수 | 기본값 | 의미 |
|
| DHIS2 인스턴스의 기본 URL |
|
| Basic 인증 사용자 이름 |
|
| Basic 인증 비밀번호 |
play 데모는 매일 밤 초기화됩니다. 호스트는 play.dhis2.org가 아닌 play.im.dhis2.org를 사용하세요. 이전 호스트는 HTML 페이지로 리디렉트됩니다.
LM Studio 0.3.17 이상은 MCP를 지원합니다. Program 탭을 열고 mcp.json을 편집하세요. 다음 항목을 추가하세요:
{
"mcpServers": {
"dhis2": {
"command": "node",
"args": ["/absolute/path/to/dhis2-mcp/dist/index.js"],
"env": {
"DHIS2_BASE_URL": "https://play.im.dhis2.org/stable-2-43-1",
"DHIS2_USERNAME": "admin",
"DHIS2_PASSWORD": "district"
}
}
}
}경로를 클론한 위치로 변경하세요. 파일을 편집한 후 모델 대화를 다시 시작하세요.
모든 stdio MCP 클라이언트에서 작동합니다. Claude Code의 경우:
claude mcp add dhis2 -- node /absolute/path/to/dhis2-mcp/dist/index.jsnpx @modelcontextprotocol/inspector node dist/index.jsInspector는 리빌드 후 서버를 다시 로드하지 않습니다. 따라서 npm run build를 실행할 때마다 Inspector에서 Restart를 클릭하세요.
MIT 라이선스입니다. 자세한 내용은 LICENSE를 참조하세요.
Get aggregated values for one indicator, split by period, for one organisation unit. Run search_indicators first to get the indicator id.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | DHIS2 period: 'LAST_12_MONTHS', 'THIS_YEAR', '2025', or '202501' | LAST_12_MONTHS |
| org_unit | No | Organisation unit id. 'USER_ORGUNIT' means the account's root unit | USER_ORGUNIT |
| indicator_id | Yes | Indicator id, from search_indicators |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the behavioral burden. It makes clear this is a read-oriented query returning aggregated values by period/org unit, but it does not disclose authentication needs, output shape, or any limitations beyond the single-indicator constraint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise: two sentences, no filler. The main behavior is front-loaded, and the required workflow hint is placed second, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the necessary prerequisite workflow and the parameters are fully documented in the schema. However, there is no output schema and no discussion of response structure, pagination, result size, or error behavior, so some operational details remain unclear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes all parameters with defaults and examples, so schema coverage is high. The description adds the prerequisite relationship to search_indicators, but otherwise does not meaningfully expand on what the schema already communicates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool returns aggregated values for one indicator, split by period, for one organisation unit. It distinguishes itself from the sibling tool by positioning search_indicators as a prerequisite, making the purpose and scope easy to identify.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent to run search_indicators first to get the indicator id, which is practical workflow guidance. It does not explicitly state when not to use the tool, such as when multiple indicators or multiple organisation units are needed, but the scope is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search DHIS2 indicators by name. Returns id, name, and description for each match.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum results to return | |
| query | Yes | Search text, for example 'ANC' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description correctly signals that this is a read/search operation and transparently states what will be returned for each match. It does not mention edge cases like empty results, case sensitivity, or search matching behavior, but the core behavioral expectations are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally efficient: two short sentences with no fluff. The main verb, resource, search target, and expected output shape are all front-loaded. Every word earns its place, supporting quick agent comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter search tool with no output schema, the description supplies the key return details (id, name, description) and makes the domain clear. Minor missing elements, such as the fact that the limit default is 10 (already in schema) or behavior when no matches exist, are not critical given the simplicity of the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents both parameters with full coverage, but the description adds meaning by clarifying that the query parameter searches against the indicator name specifically. This helps the agent understand that the if the query should be treated as a name filter rather than a keyword search across all fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Search'), a specific resource ('DHIS2 indicators'), and a scoping criterion ('by name'), and includes the exact return fields ('id, name, and description'). This clearly distinguishes it from the sibling tool query_analytics, which very likely focuses on analytical data rather than indicator metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used when an agent needs to find indicators by their name, but it does not explicitly discuss when another tool should be preferred or contrast itself with query_analytics. There is no exclusionary guidance, leaving usage somewhat to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.2.0query_analyticssearch_indicatorsScored across 2 tools
The two tools are clearly distinct: search_indicators handles metadata discovery while query_analytics retrieves aggregated data. The explicit dependency between them removes any ambiguity.
Both tools follow the same verb_noun pattern with snake_case naming (search_indicators, query_analytics), making the naming perfectly consistent and predictable.
Two tools feels very thin for a DHIS2 server, which typically covers a broad health information system domain. The server only exposes a small analytics niche, so the count does not match the implied scope.
The core workflow of searching for indicators then querying analytics is present, but there is no way to discover organization units, periods, or other metadata within the server. This forces agents to obtain those identifiers from outside the tool surface.
Read-only Google Search Console and Google Analytics for AI agents.
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