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一个用于发现和访问800TB+地理空间数据的MCP服务器，支持AI客户端通过JSON-RPC协议进行交互，提供智能搜索和高效数据访问功能。
---
name: 地理空间数据访问服务
description: 一个用于发现和访问800TB+地理空间数据的MCP服务器，支持AI客户端通过JSON-RPC协议进行交互，提供智能搜索和高效数据访问功能。
version: 1.0.0
---
# 地理空间数据访问服务
一个用于发现和访问800TB+地理空间数据的MCP服务器，支持AI客户端通过JSON-RPC协议进行交互，提供智能搜索和高效数据访问功能。
---
## ⚠️ 强制要求：API 密钥
**此 Skill 必须配置 API 密钥才能使用。**
- 首次使用时，如果 `.env` 中没有 `XBY_APIKEY`，**必须使用 AskUserQuestion 工具向用户询问 API 密钥**
- 拿到用户提供的密钥后，调用 `scripts.config.set_api_key(api_key)` 保存，然后继续处理
- 获取 API 密钥：https://xiaobenyang.com
- **禁止**在缺少 API 密钥时自行搜索或编造数据
---
## 工作流程（必须遵守）
你（大模型）是路由层，负责理解用户意图、选择工具、提取参数。代码只负责调用API。
```
用户输入 → 你选择工具 → 提取该工具需要的参数 → 调用 scripts.tools 中的函数 → 返回结果给用户
```
### 步骤
1. **检查 API 密钥**：如果 `scripts.config.settings.api_key` 为空，使用 AskUserQuestion 询问用户，拿到后调用 `scripts.config.set_api_key(key)` 保存
2. **选择工具**：根据用户意图从下方工具列表中选择对应的工具函数
3. **提取参数**：根据选中的工具，提取该工具需要的参数
4. **调用工具**：使用**关键字参数**调用 `scripts.tools` 中的函数，例如 `scripts.tools.search_schools(score='520', province='北京', category='综合')`
5. **返回结果**：将工具返回的 `raw` 数据整理后展示给用户
---
## 工具选择规则
根据用户意图选择对应的工具函数：
| 用户意图 | 工具函数 |
|---------|---------|
| Discover all organizations/accounts in Source Cooperative.
Returns:
List of account IDs (e.g., ['clarkcga', 'harvard-lil', 'youssef-harby'])
Example:
>>> await list_accounts()
['addresscloud', 'clarkcga', 'harvard-lil', ...] | `scripts.tools.list_accounts` |
| List products (datasets) in Source Cooperative with hybrid S3 + API approach.
DEFAULT: Uses S3 direct scan (fast, includes ALL products with file counts).
Set include_unpublished=False for published-only with rich metadata from API.
Args:
account_id: Filter by specific account. REQUIRED for S3 mode (default).
If None with include_unpublished=False, lists published from all accounts.
featured_only: Only return featured/curated products (API mode only).
include_unpublished: If True (default), scan S3 for ALL products including unpublished.
If False, use API for published products with rich metadata.
include_file_count: Count files in each product (default True, only in S3 mode).
Returns:
S3 mode (default): Basic info (product_id, s3_prefix, file_count) - fast!
API mode: Rich metadata (product_id, title, description, dates) - slower
Performance:
- S3 mode (default): ~240ms, includes unpublished products + file counts
- API mode (include_unpublished=False): ~500ms, rich metadata, published only
Examples:
>>> # ALL products with file counts (DEFAULT - fast!)
>>> await list_products(account_id="youssef-harby")
[
{"product_id": "exiobase-3", "source": "s3", "file_count": 1000, ...},
{"product_id": "egms-copernicus", "source": "s3", "file_count": 53, ...},
...
]
>>> # Published products with rich metadata (API mode)
>>> await list_products(account_id="youssef-harby", include_unpublished=False)
[{"product_id": "egms-copernicus", "title": "...", "description": "...", ...}]
>>> # Fast mode without file counts
>>> await list_products(account_id="youssef-harby", include_file_count=False)
[{"product_id": "exiobase-3", "source": "s3", ...}]
>>> # Featured products only (requires API mode)
>>> await list_products(featured_only=True, include_unpublished=False)
[{"product_id": "gov-data", "featured": 1, ...}] | `scripts.tools.list_products` |
| Get comprehensive metadata for a specific product.
Always includes README content if found in the product root directory.
Args:
account_id: Account ID (e.g., "harvard-lil")
product_id: Product ID (e.g., "gov-data")
Returns:
Full product metadata including account info, storage config, roles, tags
Always includes 'readme' field with content and metadata (if README exists)
Example:
>>> await get_product_details("harvard-lil", "gov-data")
{
"title": "Archive of data.gov",
"description": "...",
"account": {"name": "Harvard Library Innovation Lab", ...},
"readme": {
"found": true,
"content": "# Archive of data.gov...",
"size": 5344,
"path": "harvard-lil/gov-data/README.md"
},
...
} | `scripts.tools.get_product_details` |
| List all files in a product with full S3 paths ready for analysis.
Optionally show a hierarchical tree visualization (optimized for LLM tokens).
Args:
account_id: Account ID
product_id: Product ID
prefix: Optional prefix to filter files (subdirectory path)
max_files: Maximum files to return (default 1000)
show_tree: If True, return tree visualization only (more token-efficient, default True)
Returns:
Dict with either files list OR tree visualization (not both to save tokens)
Example (List mode - detailed metadata):
>>> result = await list_product_files("harvard-lil", "gov-data", "metadata/")
>>> print(result["files"][0])
{
"key": "harvard-lil/gov-data/metadata/metadata.jsonl.zip",
"s3_uri": "s3://us-west-2.opendata.source.coop/harvard-lil/gov-data/metadata/metadata.jsonl.zip",
"http_url": "https://data.source.coop/harvard-lil/gov-data/metadata/metadata.jsonl.zip",
"size": 1012127330,
"last_modified": "2025-02-06T16:20:22+00:00"
}
Example (Tree mode - token optimized):
>>> result = await list_product_files("harvard-lil", "gov-data", show_tree=True)
>>> print(result["tree"])
s3://us-west-2.opendata.source.coop/harvard-lil/gov-data/
├── README.md (5.2 KB) → s3://...README.md
├── metadata/
│ └── metadata.jsonl.zip (965.4 MB) → s3://...metadata.jsonl.zip
└── data/
└── datasets.parquet (128.5 MB) → s3://...datasets.parquet
Example (Partitioned data - smart summarization):
>>> result = await list_product_files("account", "product", show_tree=True)
>>> print(result["tree"])
s3://us-west-2.opendata.source.coop/account/product/
├── year={1995,1996,...,2007 (13 total)}/ [partitioned]
│ └── format={ixi,pxp}/ [partitioned]
│ └── matrix={F_impacts,F_satellite,Y,Z}/ [partitioned]
│ └── data.parquet (5.1 MB)
Note: Shows first,second,...,last (total) for >10 values; lists all for ≤10
Tree mode saves ~70% tokens + smart partition detection saves 96%+ more | `scripts.tools.list_product_files` |
| Get metadata for a specific file without downloading it.
Uses obstore's head operation for efficient metadata retrieval.
Args:
path: S3 URI (s3://...) or relative path (account_id/product_id/file)
Returns:
File metadata: size, content-type, last-modified, etag, URLs
Example:
>>> await get_file_metadata("harvard-lil/gov-data/README.md")
{
"key": "harvard-lil/gov-data/README.md",
"content_type": "binary/octet-stream",
"content_length": 5344,
"last_modified": "2025-02-06T16:29:24+00:00",
...
} | `scripts.tools.get_file_metadata` |
| Search for products across ALL accounts with smart fuzzy matching.
Handles typos, partial matches, and incomplete words using 60% similarity threshold.
**Hybrid Search** - Automatically searches across:
- All 94+ organizations
- ALL products (published + unpublished)
- All fields: title, description, product_id
Published products: Full metadata (title, description, product_id)
Unpublished products: product_id only (no title/description available)
Args:
query: Search keyword (supports typos and partial matches)
Returns:
**Top 5** matching accounts or products (sorted by relevance score)
Performance:
~5-8s (parallel 2-level S3 scan + top 5 API enrichment)
Performance breakdown:
- S3 parallel listing: ~2.4s (94 accounts + 354 products)
- Fuzzy matching: <1s (in-memory processing)
- API enrichment: ~2-5s (only top 5 results)
**11x faster** than sequential approach (was ~27s)
**Uses 2-level delimiter listing** (not full recursive scan)
Examples:
>>> # Exact match
>>> results = await search("climate")
>>> # Fuzzy match (handles typos)
>>> results = await search("climte") # Finds "climate"
>>> results = await search("exiopase") # Finds "exiobase-3" (includes unpublished!)
>>> # Partial match
>>> results = await search("geo") # Finds "geospatial", "geocoding", etc.
>>> # Result formats
>>> print(results[0]) # Account match
{
"type": "account",
"account_id": "harvard-lil",
"match_string": "harvard-lil",
"search_score": 9.5,
"similarity": 0.95,
"matched_fields": ["account_id"]
}
>>> print(results[1]) # Product match
{
"type": "product",
"account_id": "youssef-harby",
"product_id": "exiobase-3",
"match_string": "youssef-harby/exiobase-3",
"title": "", # Empty for unpublished products
"description": "", # Empty for unpublished products
"search_score": 8.2,
"similarity": 0.82,
"matched_fields": ["product_id"]
} | `scripts.tools.search` |
**如果参数不完整，使用 AskUserQuestion 向用户询问缺失的参数。**
---
## 工具函数说明
---
## scripts.tools.list_accounts
工具描述：Discover all organizations/accounts in Source Cooperative.
Returns:
List of account IDs (e.g., ['clarkcga', 'harvard-lil', 'youssef-harby'])
Example:
>>> await list_accounts()
['addresscloud', 'clarkcga', 'harvard-lil', ...]
### 参数定义
|参数名称|参数类型|是否必填|默认值|描述|
|------|-------|------|-----|----|
---
## scripts.tools.list_products
工具描述：List products (datasets) in Source Cooperative with hybrid S3 + API approach.
DEFAULT: Uses S3 direct scan (fast, includes ALL products with file counts).
Set include_unpublished=False for published-only with rich metadata from API.
Args:
account_id: Filter by specific account. REQUIRED for S3 mode (default).
If None with include_unpublished=False, lists published from all accounts.
featured_only: Only return featured/curated products (API mode only).
include_unpublished: If True (default), scan S3 for ALL products including unpublished.
If False, use API for published products with rich metadata.
include_file_count: Count files in each product (default True, only in S3 mode).
Returns:
S3 mode (default): Basic info (product_id, s3_prefix, file_count) - fast!
API mode: Rich metadata (product_id, title, description, dates) - slower
Performance:
- S3 mode (default): ~240ms, includes unpublished products + file counts
- API mode (include_unpublished=False): ~500ms, rich metadata, published only
Examples:
>>> # ALL products with file counts (DEFAULT - fast!)
>>> await list_products(account_id="youssef-harby")
[
{"product_id": "exiobase-3", "source": "s3", "file_count": 1000, ...},
{"product_id": "egms-copernicus", "source": "s3", "file_count": 53, ...},
...
]
>>> # Published products with rich metadata (API mode)
>>> await list_products(account_id="youssef-harby", include_unpublished=False)
[{"product_id": "egms-copernicus", "title": "...", "description": "...", ...}]
>>> # Fast mode without file counts
>>> await list_products(account_id="youssef-harby", include_file_count=False)
[{"product_id": "exiobase-3", "source": "s3", ...}]
>>> # Featured products only (requires API mode)
>>> await list_products(featured_only=True, include_unpublished=False)
[{"product_id": "gov-data", "featured": 1, ...}]
### 参数定义
|参数名称|参数类型|是否必填|默认值|描述|
|------|-------|------|-----|----|
|account_id|null|false| |null|
|featured_only|boolean|false|false|null|
|include_unpublished|boolean|false|true|null|
|include_file_count|boolean|false|true|null|
---
## scripts.tools.get_product_details
工具描述：Get comprehensive metadata for a specific product.
Always includes README content if found in the product root directory.
Args:
account_id: Account ID (e.g., "harvard-lil")
product_id: Product ID (e.g., "gov-data")
Returns:
Full product metadata including account info, storage config, roles, tags
Always includes 'readme' field with content and metadata (if README exists)
Example:
>>> await get_product_details("harvard-lil", "gov-data")
{
"title": "Archive of data.gov",
"description": "...",
"account": {"name": "Harvard Library Innovation Lab", ...},
"readme": {
"found": true,
"content": "# Archive of data.gov...",
"size": 5344,
"path": "harvard-lil/gov-data/README.md"
},
...
}
### 参数定义
|参数名称|参数类型|是否必填|默认值|描述|
|------|-------|------|-----|----|
|account_id|string|true| |null|
|product_id|string|true| |null|
---
## scripts.tools.list_product_files
工具描述：List all files in a product with full S3 paths ready for analysis.
Optionally show a hierarchical tree visualization (optimized for LLM tokens).
Args:
account_id: Account ID
product_id: Product ID
prefix: Optional prefix to filter files (subdirectory path)
max_files: Maximum files to return (default 1000)
show_tree: If True, return tree visualization only (more token-efficient, default True)
Returns:
Dict with either files list OR tree visualization (not both to save tokens)
Example (List mode - detailed metadata):
>>> result = await list_product_files("harvard-lil", "gov-data", "metadata/")
>>> print(result["files"][0])
{
"key": "harvard-lil/gov-data/metadata/metadata.jsonl.zip",
"s3_uri": "s3://us-west-2.opendata.source.coop/harvard-lil/gov-data/metadata/metadata.jsonl.zip",
"http_url": "https://data.source.coop/harvard-lil/gov-data/metadata/metadata.jsonl.zip",
"size": 1012127330,
"last_modified": "2025-02-06T16:20:22+00:00"
}
Example (Tree mode - token optimized):
>>> result = await list_product_files("harvard-lil", "gov-data", show_tree=True)
>>> print(result["tree"])
s3://us-west-2.opendata.source.coop/harvard-lil/gov-data/
├── README.md (5.2 KB) → s3://...README.md
├── metadata/
│ └── metadata.jsonl.zip (965.4 MB) → s3://...metadata.jsonl.zip
└── data/
└── datasets.parquet (128.5 MB) → s3://...datasets.parquet
Example (Partitioned data - smart summarization):
>>> result = await list_product_files("account", "product", show_tree=True)
>>> print(result["tree"])
s3://us-west-2.opendata.source.coop/account/product/
├── year={1995,1996,...,2007 (13 total)}/ [partitioned]
│ └── format={ixi,pxp}/ [partitioned]
│ └── matrix={F_impacts,F_satellite,Y,Z}/ [partitioned]
│ └── data.parquet (5.1 MB)
Note: Shows first,second,...,last (total) for >10 values; lists all for ≤10
Tree mode saves ~70% tokens + smart partition detection saves 96%+ more
### 参数定义
|参数名称|参数类型|是否必填|默认值|描述|
|------|-------|------|-----|----|
|account_id|string|true| |null|
|product_id|string|true| |null|
|prefix|string|false|""|null|
|max_files|integer|false|1000.0|null|
|show_tree|boolean|false|true|null|
---
## scripts.tools.get_file_metadata
工具描述：Get metadata for a specific file without downloading it.
Uses obstore's head operation for efficient metadata retrieval.
Args:
path: S3 URI (s3://...) or relative path (account_id/product_id/file)
Returns:
File metadata: size, content-type, last-modified, etag, URLs
Example:
>>> await get_file_metadata("harvard-lil/gov-data/README.md")
{
"key": "harvard-lil/gov-data/README.md",
"content_type": "binary/octet-stream",
"content_length": 5344,
"last_modified": "2025-02-06T16:29:24+00:00",
...
}
### 参数定义
|参数名称|参数类型|是否必填|默认值|描述|
|------|-------|------|-----|----|
|path|string|true| |null|
---
## scripts.tools.search
工具描述：Search for products across ALL accounts with smart fuzzy matching.
Handles typos, partial matches, and incomplete words using 60% similarity threshold.
**Hybrid Search** - Automatically searches across:
- All 94+ organizations
- ALL products (published + unpublished)
- All fields: title, description, product_id
Published products: Full metadata (title, description, product_id)
Unpublished products: product_id only (no title/description available)
Args:
query: Search keyword (supports typos and partial matches)
Returns:
**Top 5** matching accounts or products (sorted by relevance score)
Performance:
~5-8s (parallel 2-level S3 scan + top 5 API enrichment)
Performance breakdown:
- S3 parallel listing: ~2.4s (94 accounts + 354 products)
- Fuzzy matching: <1s (in-memory processing)
- API enrichment: ~2-5s (only top 5 results)
**11x faster** than sequential approach (was ~27s)
**Uses 2-level delimiter listing** (not full recursive scan)
Examples:
>>> # Exact match
>>> results = await search("climate")
>>> # Fuzzy match (handles typos)
>>> results = await search("climte") # Finds "climate"
>>> results = await search("exiopase") # Finds "exiobase-3" (includes unpublished!)
>>> # Partial match
>>> results = await search("geo") # Finds "geospatial", "geocoding", etc.
>>> # Result formats
>>> print(results[0]) # Account match
{
"type": "account",
"account_id": "harvard-lil",
"match_string": "harvard-lil",
"search_score": 9.5,
"similarity": 0.95,
"matched_fields": ["account_id"]
}
>>> print(results[1]) # Product match
{
"type": "product",
"account_id": "youssef-harby",
"product_id": "exiobase-3",
"match_string": "youssef-harby/exiobase-3",
"title": "", # Empty for unpublished products
"description": "", # Empty for unpublished products
"search_score": 8.2,
"similarity": 0.82,
"matched_fields": ["product_id"]
}
### 参数定义
|参数名称|参数类型|是否必填|默认值|描述|
|------|-------|------|-----|----|
|query|string|true| |null|
---
---
## 返回值处理
工具函数返回 `dict` 对象：
- `result["raw"]` - API 原始返回数据（JSON），**直接将此数据整理后展示给用户**
- `result["success"]` - 是否成功（True/False）
- `result["message"]` - 状态消息
---
## 项目结构
```
xiaobenyang_gaokao_skill/
├── scripts/
│ ├── __init__.py
│ ├── config.py # 配置管理 + set_api_key()
│ ├── call_api.py # API 客户端 + call_api()
│ └── tools.py # 工具函数（直接调用）
├── requirements.txt
└── SKILL.md
```
---
## 注意事项
1. **API 密钥是必需的**，无密钥时必须通过 AskUserQuestion 询问用户
2. **禁止**在缺少 API 密钥时自行搜索或编造数据don't have the plugin yet? install it then click "run inline in claude" again.