Model Card Chat
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_modelsA | Find Hugging Face models matching a keyword and/or task. Use this to discover candidate models. For "what is popular/new right now", prefer get_trending_models instead. Args: query: Free-text keyword, e.g. "sentiment", "whisper", "code". task: Hugging Face pipeline tag. Must be an exact Hub tag such as: text-classification, token-classification, question-answering, summarization, translation, text-generation, fill-mask, sentence-similarity, feature-extraction, automatic-speech-recognition, text-to-speech, audio-classification, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, tabular-classification, tabular-regression, time-series-forecasting, reinforcement-learning. Leave empty to search all tasks. limit: Maximum number of results (default 10, capped at 50). |
| get_model_cardA | Get structured facts about one model: task, license, parameter count, downloads, and any benchmark scores published in its model card. Use this when the user names a specific model, or to check details before recommending one. For free-text questions about training data, limitations, or intended use, use ask_about_model instead. Args: model_id: Hugging Face model ID, e.g. "google-bert/bert-base-uncased". Short forms like "bert-base-uncased" are resolved automatically. |
| compare_modelsA | Compare 2-6 models side by side on size, license, downloads, and benchmark scores. Use this whenever the user is choosing between named alternatives — it is cheaper and easier to read than calling get_model_card repeatedly. Args: model_ids: List of 2 to 6 Hugging Face model IDs. |
| get_trending_modelsA | List models trending on Hugging Face right now, by live trending score. Use this for "what's popular", "what's new", or "what are people using lately". For a specific keyword or an exhaustive search, use search_models. Args: task: Optional exact Hub pipeline tag to filter by, e.g. "text-to-image" or "text-generation". Leave empty for all tasks. limit: Maximum number of results (default 10, capped at 50). |
| ask_about_modelA | Answer a free-text question using the written model card README. Use this for things only prose documents: training data, intended use, limitations, known biases, evaluation setup, or usage instructions. Returns the most relevant excerpts with their section headings as citations — base the answer only on these excerpts. For structured facts (license, size, downloads, benchmark numbers), use get_model_card instead; it is cheaper and more reliable for those. Args: model_id: Hugging Face model ID, e.g. "openai/whisper-large-v3". question: The question to answer, e.g. "what data was it trained on?" |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose with descriptions explicitly differentiating them (e.g., search_models vs. get_trending_models, get_model_card vs. ask_about_model). Overlaps are minimal and well-documented with guidance on when to use which.
All tool names follow a consistent verb_noun pattern in snake_case (search_models, get_model_card, compare_models, get_trending_models, ask_about_model).
The server has 5 tools, a well-scoped number for the domain of Hugging Face model card exploration. Each tool serves a distinct need without being overly broad or too few.
The tool set covers the full lifecycle for model discovery and information retrieval: searching, trending, structured facts, comparison, and free-text querying. No obvious gaps for the stated purpose.