Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@source-coop-mcp search for climate data"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Discover and access 800TB+ of geospatial data through AI agents.
An MCP (Model Context Protocol) server for Source Cooperative - a collaborative repository with datasets from Maxar, Harvard, ESA, USGS, and 90+ organizations.
graph TB
subgraph "AI Clients"
A1[Claude Desktop]
A2[Claude Code]
A3[Cursor]
A4[Cline]
A5[Zed]
A6[Continue.dev]
end
subgraph "MCP Server"
MCP[Source Cooperative MCP<br/>FastMCP + obstore]
end
subgraph "6 Available Tools"
T1[list_accounts<br/>94+ orgs]
T2[list_products<br/>hybrid S3+API]
T3[get_product_details<br/>+ README]
T4[list_product_files<br/>tree mode]
T5[get_file_metadata<br/>no download]
T6[search<br/>hybrid fuzzy]
end
subgraph "Data Sources"
S1[HTTP API<br/>source.coop/api]
S2[S3 Direct<br/>opendata.source.coop]
end
A1 -->|JSON-RPC| MCP
A2 -->|JSON-RPC| MCP
A3 -->|JSON-RPC| MCP
A4 -->|JSON-RPC| MCP
A5 -->|JSON-RPC| MCP
A6 -->|JSON-RPC| MCP
MCP --> T1
MCP --> T2
MCP --> T3
MCP --> T4
MCP --> T5
MCP --> T6
T1 --> S2
T2 --> S1
T2 --> S2
T3 --> S1
T3 --> S2
T4 --> S2
T5 --> S2
T6 --> S1
style MCP fill:#4CAF50,stroke:#2E7D32,stroke-width:3px,color:#fff
style S1 fill:#2196F3,stroke:#1976D2,stroke-width:2px,color:#fff
style S2 fill:#2196F3,stroke:#1976D2,stroke-width:2px,color:#fffKey Features:
ā Token Optimized - 72% reduction for large datasets
ā Smart Partitions - Auto-detects Hive-style patterns
ā Fuzzy Search - Handles typos and partial matches
ā No Auth - All 800TB+ is public
Related MCP server: SkyFi MCP Server
uvx source-coop-mcpAdd to config file:
Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
Claude Code: VS Code settings.json
Cursor: Cursor settings
Cline: Cline MCP settings
{
"mcpServers": {
"source-coop": {
"command": "uvx",
"args": ["source-coop-mcp"]
}
}
}Add to Zed settings:
{
"context_servers": {
"source-coop": {
"command": "uvx",
"args": ["source-coop-mcp"]
}
}
}Add to Continue config (~/.continue/config.json):
{
"experimental": {
"modelContextProtocolServers": [
{
"transport": {
"type": "stdio",
"command": "uvx",
"args": ["source-coop-mcp"]
}
}
]
}
}Restart your AI client and start exploring!
Tool | Purpose | Performance |
| Find all 94+ organizations | ~850ms |
| Hybrid: S3 mode (default) for ALL datasets + file counts | ~240ms |
| API mode for published datasets with rich metadata | ~500ms |
| Get metadata + README automatically | ~650ms |
| List files with S3/HTTP paths | ~240ms |
| Tree view (72% token savings) | ~980ms |
| Get file info without downloading | ~230ms |
| Hybrid: Search accounts + products (published + unpublished), top 5 results | ~5-10s |
"List all organizations in Source Cooperative"
ā Returns 94+ organizations: maxar, planet, harvard, etc.
"Find all datasets for harvard-lil"
ā Discovers published + unpublished products
"Search for climate datasets"
ā Smart fuzzy search handles typos and partial matches"List files in harvard-lil/gov-data"
ā Returns S3 paths and HTTP URLs ready for analysis
"Show me the file tree with partition detection"
ā Smart visualization: year={2020,2021,...+5 more}/ [partitioned]
"Get file metadata without downloading"
ā Size, last modified, ETag"Search for climte" (typo)
ā Finds "climate" datasets (fuzzy matching)
"Search for geo" (partial)
ā Finds "geospatial", "geocoding", etc.Feature | Description |
Complete Discovery | Finds unpublished products the official API doesn't show |
No Authentication | All 800TB+ data is public |
Fast Performance | Rust-backed S3 client (9x faster than boto3) |
Token Optimized | Tree mode: 72% token reduction for large datasets |
Smart Partitions | Auto-detects patterns: |
Fuzzy Search | Handles typos and partial matches |
README Integration | Documentation automatically included |
800TB+ Data | 94+ organizations, geospatial datasets |
1. "List all organizations"
ā Get 94+ account names
2. "Show me all datasets from maxar"
ā Discover published + unpublished products
3. "Search for climate data"
ā Smart fuzzy search finds relevant datasets
4. "Get details for harvard-lil/gov-data"
ā Full metadata + README content
5. "List files in this dataset with tree view"
ā Token-optimized tree with partition detectionSource Cooperative has 800TB+ of valuable data, but:
Official API only shows published products
No auto-discovery of organizations
Requires knowing what you're looking for
This MCP server provides:
ā Complete auto-discovery (published + unpublished)
ā Smart search with fuzzy matching
ā Direct S3 access for all files
ā Token-optimized outputs (72% reduction)
ā Smart partition detection (10-88% additional savings)
ā README documentation included automatically
ā No authentication required
All operations complete in under 1 second:
list_accounts(): ~850ms (94+ organizations)
list_products(): ~240ms (S3 mode - ALL datasets + file counts)
list_products(include_unpublished=False): ~500ms (API mode - published with metadata)
list_product_files(): ~240ms (simple list)
list_product_files(tree=True): ~980ms (72% token savings)
get_file_metadata(): ~230ms (HEAD only)
search(query): ~5-10s (hybrid search - 1 recursive S3 scan, top 5 enriched)Dataset Size | Without Tree | With Tree | Saved |
10 files | 1,500 tokens | 415 tokens | 72.3% |
100 files | 15,000 tokens | 4,150 tokens | 72.3% |
1,000 files | 150,000 tokens | 41,500 tokens | 72.3% |
With partition detection (1,000 partitions): 88% total savings!
Python: 3.11 or higher
Package Manager: uv (installed automatically by uvx)
Operating Systems: macOS, Linux, Windows
See DEVELOPMENT.md for:
Architecture details
Testing instructions
Contributing guidelines
Performance benchmarks
Token optimization details
Issues: GitHub Issues
MIT License - see LICENSE for details.
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", ... }
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions using 'obstore's head operation for efficient metadata retrieval' and 'without downloading,' implying read-only behavior. However, it does not disclose authentication needs, rate limits, or potential errors. This is moderately transparent but lacks full disclosure.
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 concise with a clear bullet list for args, returns, and an example. No redundant information, every sentence adds value.
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 metadata retrieval tool with one parameter and an output schema, the description covers the purpose, parameter format, and return fields through the example. Missing edge cases like error handling or permissions, but overall complete for the task.
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 has 0% description coverage, so the description compensates well: it explains the path parameter accepts S3 URIs or relative paths (account_id/product_id/file) and provides an example. This adds meaningful context beyond the schema's simple 'string' type.
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 'Get metadata for a specific file without downloading it.' It uses a specific verb and resource, and distinguishes from sibling tools (list_product_files, search, etc.) which focus on listings or different entities.
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 usage for efficient metadata retrieval without downloading, but does not explicitly state when not to use or compare with alternative tools like get_product_details. Given the tool's specificity, this is adequate but could be improved.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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" }, ... }
| Name | Required | Description | Default |
|---|---|---|---|
| account_id | Yes | ||
| product_id | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses that the tool always includes README content if found, and summarizes return fields (account info, storage config, roles, tags). However, it does not mention side effects (likely none, read-only), error behavior for missing products, or permission needs. The description adds moderate behavioral context.
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 concise and well-structured: a single-line summary, a key note about README, then Args, Returns, and Example sections. Every sentence adds value without redundancy. It is appropriately sized for the tool's simplicity.
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 simplicity (2 string params, no enums, output schema exists), the description covers the main functionality, return structure summary, and includes an example. It lacks error handling details or explicit read-only indication, but for a straightforward retrieval tool it is largely complete.
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?
Input schema has 0% description coverage, so the description must compensate. It provides examples for account_id ('harvard-lil') and product_id ('gov-data') in the Args section and a usage example. However, it does not explain the nature of these IDs (e.g., account name vs. identifier) or valid formats. The examples add partial semantics but not full detail.
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 'Get comprehensive metadata for a specific product' with a strong verb and resource. It distinguishes from sibling tools like get_file_metadata (file-level) and list_products (list of products) by focusing on a single product's full details, including README.
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?
No guidance is provided on when to use this tool versus alternatives. For example, it does not explain that this tool is for detailed retrieval of a known product versus using list_products for summaries or get_file_metadata for file-level info. The description lacks explicit 'when to use' or 'when not to use' context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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', ...]
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose whether the operation is read-only, requires authentication, or has any side effects. The return format is given but not the behavior.
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 extremely concise: one sentence plus a returns line and example. The purpose is front-loaded, and every element is necessary.
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 parameterless list tool, the description is almost complete. It provides the return format and an example. However, it does not mention edge cases like empty lists or pagination, which are minor gaps.
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?
There are no parameters, so the schema coverage is 100% by default. The description adds no parameter documentation but does not need to. Baseline score of 4 is appropriate.
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 discovers all organizations/accounts, with a concrete return format and example. It is easily distinguishable from sibling tools that deal with files and products.
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?
No guidance is provided on when to or when not to use this tool. There is no mention of alternatives or prerequisites, leaving the agent without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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/ āāā README.md (5.2 KB) ā s3://...README.md āāā metadata/ ā āāā metadata.jsonl.zip (965.4 MB) ā s3://...metadata.jsonl.zip āāā data/ āāā datasets.parquet (128.5 MB) ā 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/ āāā year={1995,1996,...,2007 (13 total)}/ [partitioned] ā āāā format={ixi,pxp}/ [partitioned] ā āāā matrix={F_impacts,F_satellite,Y,Z}/ [partitioned] ā āāā data.parquet (5.1 MB)
Note: Shows first,second,...,last (total) for >10 values; lists all for ā¤10
Tree mode saves ~70% tokens + smart partition detection saves 96%+ more| Name | Required | Description | Default |
|---|---|---|---|
| account_id | Yes | ||
| product_id | Yes | ||
| prefix | No | ||
| max_files | No | ||
| show_tree | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that the tool returns either files list or tree visualization (not both) to save tokens, and explains token optimization and partition detection. However, it does not mention rate limits, authentication, or side effects.
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?
Description is well-structured with Args, Returns, and multiple examples. While lengthy, each part adds value. A slightly more concise introduction could improve, but overall it is front-loaded and informative.
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 complexity (optional modes, token optimization, partition detection) and that output schema exists, the description is very complete. It covers all parameters, behavior, and edge cases like partitioned data summarization. No gaps identified.
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?
Schema coverage is 0% (no parameter descriptions in schema). The description adds comprehensive explanations for each parameter: account_id, product_id, prefix, max_files, show_tree, including defaults and purpose. Examples further clarify usage.
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 'List all files in a product with full S3 paths ready for analysis', specifying the verb 'list' and resource 'product files'. It differentiates from siblings like 'get_file_metadata' (single file) and 'search' (search across products).
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?
Examples show when to use tree mode vs list mode (token efficiency), and mention that results are either files or tree. However, it does not explicitly state when not to use this tool or compare to alternatives like 'search'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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, ...}]| Name | Required | Description | Default |
|---|---|---|---|
| account_id | No | ||
| featured_only | No | ||
| include_unpublished | No | ||
| include_file_count | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description fully carries behavioral disclosure burden. It details two operation modes, performance (~240ms vs ~500ms), parameter effects (include_unpublished, featured_only, include_file_count), and return formats. No contradictions.
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?
Description is lengthy but well-structured with sections (DEFAULT, Args, Returns, Performance, Examples). Information is front-loaded. Could be slightly more concise, but organization aids readability.
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 complexity (4 parameters, two modes, rich behavior) and absence of annotations, description is thorough. It covers parameter semantics, return formats, performance, and provides multiple examples. Output schema exists but description adds value beyond it.
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?
Schema has 0% description coverage, so description must compensate. It explains each parameter's purpose, defaults, and interactions (e.g., 'account_id required for S3 mode', 'include_unpublished switches modes'). Fully compensates for missing schema descriptions.
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?
Description clearly states it lists products (datasets) in Source Cooperative, distinguishing between S3 direct scan and API modes. The verb 'list' and resource 'products' are specific, and the description differentiates from sibling tools like get_product_details and list_accounts.
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?
Provides explicit guidance on when to use each mode (S3 mode default, fast, includes all products; API mode for published-only with rich metadata). Examples and parameter explanations help. Lacks explicit mention of when not to use this tool vs alternatives, but overall clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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"]
}| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavioral traits: fuzzy matching with 60% similarity threshold, hybrid search across 94+ organizations and all products, metadata differences for published vs unpublished, performance details (5-8s, 11x faster), and result structure with examples.
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 well-structured with sections (details, args, returns, performance, examples) and front-loads the core purpose. While somewhat lengthy, every part adds value (especially the examples). Could be slightly more concise but remains effective.
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 complexity (fuzzy search across accounts/products, handling unpublished items, performance characteristics), the description covers all aspects thoroughly. It includes output schema details in examples, addresses edge cases (unpublished products), and provides usage guidance, making it complete for effective selection and invocation.
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 has 1 parameter with 0% description coverage, but the description explains 'query' as 'Search keyword (supports typos and partial matches)' and provides multiple examples of its usage. This adds significant meaning beyond the schema definition.
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 'Search for products across ALL accounts with smart fuzzy matching', specifying the verb, resource, and key differentiating features like fuzzy matching and cross-account search. It distinguishes from sibling tools that likely do exact listing or detail retrieval.
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 extensive guidelines on when to use this tool: for fuzzy searching, handling typos, and partial matches across all accounts and products (published and unpublished). It explains what is searched and the return format but does not explicitly state when not to use it or provide direct comparisons to sibling tools.
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.
v0.2.6get_file_metadataget_product_detailslist_accountslist_product_fileslist_productssearchScored across 6 tools
Each tool has a clear, distinct purpose: metadata retrieval for files vs. products, listing accounts, products, or files, and a separate search function. No overlap in functionality.
All tool names follow a consistent verb_noun pattern (get_file_metadata, list_accounts, search, etc.) using lowercase with underscores. Highly predictable and uniform.
Six tools is ideal for a data catalog server, covering discovery, browsing, and search without being overwhelming or too sparse.
The tool surface covers all essential operations for exploring a data cooperative: listing accounts, products, files, retrieving detailed metadata, and cross-account search. No obvious gaps.
AI Agent Source Registry. 288K+ curated sources for agentic search and discovery.
GIS tools for AI agents: 65 free tools + 8 paid (hazard/site-scouting/GeoJSON export)
Gateway between LLM agents and world data through eight tools and a bundled endpoint catalog.
Discover, inspect and run 63,000+ agent tools from one balance. Pay per call, no subscriptions.
