이 모듈은 NASA의 지구 데이터 공통 메타데이터 저장소(CMR)를 위한 모델 컨텍스트 프로토콜 (MCP)입니다. 이 MCP 서버의 목표는 Earthaccess를 통해 AI 검색 결과를 NASA 데이터셋 카탈로그와 통합하는 것입니다.
uv - Rust 기반 Python 패키지 관리자, Claude 데스크톱 또는 chatGPT 데스크톱(MCP 사용용)과 같은 LLM 클라이언트
Related MCP server: Google Earth Engine MCP Server
로컬 환경이나 LLM 클라이언트가 실행 중인 환경에 저장소를 복제합니다.
지엑스피1
curl -LsSf https://astral.sh/uv/install.sh | shuv venv
source .venv/bin/activateuv sync다음 구성을 업데이트하려면 which uv (UV_LIB) 및 PWD (CMR_MCP_INSTALL)의 출력을 사용합니다.
이 예에서는 Claude desktop을 사용하겠습니다.
claude_desktop_config.json 파일을 업데이트하세요(이 파일을 생성해야 하는 경우도 있습니다). Mac에서는 보통 ~/Library/Application\ Support/Claude/claude_desktop_config.json 에 있습니다.
다음 구성을 추가하고 UV_LIB 및 CMR_MCP_INSTALL 값을 입력합니다. 여기서는 환경 변수를 사용하지 마세요.
{
"mcpServers": {
"cmr": {
"command": "$UV_LIB$",
"args": [
"--directory",
"$CMR_MCP_INSTALL$",
"run",
"cmr-search.py"
]
}
}
}상담원에게 search cmr for... 하도록 요청하기만 하면 됩니다. 아래는 이 기능을 실제로 사용하는 간단한 예시입니다.
효과가 있는 다른 프롬프트:
2024년부터 2025년까지의 데이터 세트를 CMR에서 검색하세요.
키워드 Climate를 사용하여 2020년부터 2024년까지 PO.DAAC 데이터세트에 대한 CMR 검색
Get a list of datasets form CMR based on keywords.
Args:
startdate: (Optional) Start date of search request (like "2002" or "2022-03-22")
stopdate: (Optional) Stop date of search request (like "2002" or "2022-03-22")
daac: the daac to search, e.g. NSIDC or PODAAC
keywords: A list of keyword arguments to search collections for.
| Name | Required | Description | Default |
|---|---|---|---|
| daac | No | ||
| keyword | No | ||
| startdate | No | ||
| stopdate | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool retrieves data ('Get a list'), implying a read-only operation, but fails to detail critical behaviors like error handling, rate limits, authentication needs, or response format. This leaves significant gaps in understanding how the tool behaves in practice.
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 efficiently structured with a clear purpose statement followed by parameter explanations. Each sentence adds value without redundancy. However, the minor typo ('form' instead of 'from') slightly detracts from polish, and the parameter list could be more integrated into the flow rather than a separate block.
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?
Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is partially complete. It covers the purpose and parameters adequately but lacks details on behavioral traits, usage context, and return values. This makes it functional but insufficient for fully informed use without additional assumptions or trial-and-error.
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 description adds meaningful context for all parameters beyond the schema, which has 0% coverage. It explains that 'startdate' and 'stopdate' are for search requests with format examples, 'daac' specifies the source to search, and 'keywords' are for searching collections. This compensates well for the lack of schema descriptions, though it could clarify parameter interactions or constraints.
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 the tool's purpose: 'Get a list of datasets from CMR based on keywords.' It specifies the verb ('Get'), resource ('datasets'), and source ('CMR'), making it easy to understand what the tool does. However, with no sibling tools mentioned, there's no explicit differentiation from alternatives, preventing a perfect score.
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 provides no guidance on when to use this tool versus alternatives, prerequisites, or context for its application. It only lists parameters without explaining the tool's role in workflows or decision criteria for invocation, leaving users to infer usage from the purpose alone.
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.
v1.0.0get_datasetsScored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool has a clear, singular purpose of retrieving datasets from CMR based on search parameters.
The single tool name 'get_datasets' follows a clear verb_noun pattern, and there are no other tools to create inconsistency. The naming is straightforward and descriptive.
A single tool is too few for a server named 'CMR Model Context Protocol', which suggests a broader scope for interacting with CMR datasets. This minimal toolset feels thin and incomplete for the apparent domain.
The tool surface is severely incomplete for a CMR dataset server. There are obvious gaps: no tools for creating, updating, deleting, or managing datasets, and no support for operations like filtering, sorting, or detailed metadata retrieval beyond basic search.
Discover, verify, and access NASA Earth science datasets via the Common Metadata Repository (CMR)
MCP server for searching Airweave collections with natural language queries.
Cloud-hosted MCP server for secure AI access to enterprise data sources via CData Connect AI.
