このモジュールは、NASAのearthdata共通メタデータリポジトリ（CMR）用のモデルコンテキストプロトコル（MCP）です。このMCPサーバーの目的は、AIによる検索結果をEarthaccessを介してNASAのデータセットカタログに統合することです。
uv - Rust ベースの Python パッケージ マネージャー、Claude デスクトップや chatGPT デスクトップなどの LLM クライアント (MCP を使用するため)
Related MCP server: Google Earth Engine MCP Server
リポジトリをローカル環境、または LLM クライアントが実行されている場所にクローンします。
git clone https://github.com/podaac/cmr-mcp.git
cd cmr-mcpcurl -LsSf https://astral.sh/uv/install.sh | shuv venv
source .venv/bin/activateuv syncwhich uv (UV_LIB) とPWD (CMR_MCP_INSTALL) の出力を使用して、次の構成を更新します。
この例では、Claude デスクトップを使用します。
claude_desktop_config.jsonファイルを更新します（場合によっては作成する必要があります）。Mac の場合、このファイルは~/Library/Application\ Support/Claude/claude_desktop_config.jsonにあることが多いです。
UV_LIB と CMR_MCP_INSTALL の値を入力して次の構成を追加します。ここでは環境変数を使用しないでください。
{
"mcpServers": {
"cmr": {
"command": "$UV_LIB$",
"args": [
"--directory",
"$CMR_MCP_INSTALL$",
"run",
"cmr-search.py"
]
}
}
}エージェントsearch cmr for...よう指示するだけです。以下は、この動作の簡単な例です。
他にも使えるプロンプト:
2024年から2025年までのデータセットをCMRで検索
2020年から2024年までのPO.DAACデータセットをキーワード「気候」でCMRで検索
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.
