search_spacesSearch Hugging Face Spaces (demo apps) with filters for author and SDK (gradio/streamlit/docker/static); returns space id, sdk, runtime status, and likes.
| Name | Required | Description | Default |
|---|---|---|---|
| sdk | No | gradio | streamlit | docker | static | |
| full | No | ||
| sort | No | ||
| limit | No | ||
| author | No | ||
| search | No | ||
| direction | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesPrevious value: -[
- {
- "limit": 20,
- "sdk": "gradio",
- "search": "chatbot"
- },
- {
- "author": "gradio-demos",
- "direction": "-1",
- "limit": 10,
- "sort": "trending_score"
- }
-]New value: +[
+ {
+ "limit": 20,
+ "sdk": "gradio",
+ "search": "chatbot"
+ },
+ {
+ "author": "stabilityai",
+ "direction": "-1",
+ "limit": 10,
+ "sort": "trendingScore"
+ }
+]Input schema / examplesAdded value: +[
+ {
+ "limit": 20,
+ "sdk": "gradio",
+ "search": "chatbot"
+ },
+ {
+ "author": "gradio-demos",
+ "direction": "-1",
+ "limit": 10,
+ "sort": "trending_score"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "List of Spaces matching search criteria",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, open-world, idempotent, and non-destructive. The description adds that it returns specific fields (space id, sdk, runtime status, likes), which is useful but not critical. It does not disclose pagination, rate limits, or other behavioral nuances.
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 a single concise sentence, front-loading the purpose. However, it could benefit from slight restructuring or bullet points for clarity.
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 7 parameters (0 required) and an output schema (unseen), the description provides a basic overview but misses detailed parameter explanations. It is adequate but not thorough for a complex search 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?
Schema coverage is low (14%), with only the 'sdk' parameter having a description. The description mentions filters by 'author' and 'sdk' but does not clarify other parameters like 'full', 'sort', 'direction', or 'limit'. This leaves significant ambiguity.
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 Hugging Face Spaces (demo apps)' with specific filters (author, SDK) and expected return fields (space id, sdk, runtime status, likes). This distinguishes it from sibling tools like search_datasets and search_models.
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 searching spaces with filters but does not explicitly state when to use this tool versus alternatives like get_space or search_datasets. No when-not or alternative guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tool set includes many similarly-named tools, especially the 'ask_pipeworx' variants and the multiple polymarket tools, which could cause confusion. However, each tool has a detailed description specifying its unique purpose, so an agent reading carefully can distinguish them.
Naming is inconsistent across the set: Huggingface tools use 'get_', 'list_', 'search_' prefixes, while Pipeworx tools use varied verbs like 'ask_pipeworx', 'bet_research', 'entity_profile', and others. There is no overall pattern or convention, making it harder to predict tool names.
With 41 tools, the server is heavily loaded. While each tool has a distinct role, the scope combines two large domains (Huggingface and Pipeworx), leading to a tool count well above the typical 3-15 range for a focused server.
The server covers a wide range of functionalities: Huggingface model/dataset queries, Pipeworx data lookups, subscription management, and memory tools. However, it lacks write operations for Huggingface (e.g., uploading models/datasets) and some lifecycle operations, leaving noticeable gaps.