get_spaceDetailed Hugging Face Space metadata by repo_id (e.g. "stabilityai/stable-diffusion") and optional revision; returns sdk, runtime status, likes, and linked models.
| Name | Required | Description | Default |
|---|---|---|---|
| repo_id | Yes | ||
| revision | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "repo_id": "gradio-demos/chatbot"
+ },
+ {
+ "repo_id": "stabilityai/stable-diffusion-xl"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Detailed Space repository information",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint; description adds value by naming specific return fields, but no additional behavioral caveats (e.g., rate limits, auth requirements).
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?
Single sentence with efficient, front-loaded structure: purpose first, then optional parameter, then return fields. No wasted words.
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 simple schema (2 params, no nested objects) and presence of output schema, description covers key aspects. Lacks error handling or validation details, but adequate for basic usage.
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; description only gives an example for repo_id and notes optional revision, adding minimal meaning beyond field names. Does not compensate 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 explicitly states it retrieves 'Detailed Hugging Face Space metadata' by repo_id, lists return fields (sdk, runtime status, likes, linked models), and distinguishes from sibling tools like get_dataset or search_spaces.
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?
Clearly specifies inputs (repo_id, optional revision) and purpose, making usage context obvious. However, no explicit 'when not to use' or alternatives, though sibling names (get_model, search_spaces) imply distinct roles.
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.