This server provides tools for conversational model discovery and comparison on the Hugging Face Hub.
Search for models: Find models by keyword and/or task (e.g., text-classification).
Get detailed model facts: Retrieve structured information like license, parameters, download stats, and benchmark scores for a specific model.
Compare models: Side-by-side comparison of 2–6 models on key metrics such as size, license, downloads, and benchmarks.
Discover trending models: List currently trending models on the Hub, optionally filtered by task.
Ask free-text questions about a model: Answer queries about training data, limitations, or biases using RAG over the model card README.
Provides tools to search, compare, and retrieve model cards from the Hugging Face Hub, including benchmark-aware selection and RAG over model card READMEs.
Click on "Install 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., "@Model Card Chat what's a good small model for sentiment analysis?"
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.
An MCP server that turns the Hugging Face Hub into a conversational model picker — with a tool-use eval harness to prove the agent actually uses it correctly.
Ask "what's a good small model for sentiment analysis?" and the agent searches the Hub live, compares candidates on size, license and published benchmark scores, and recommends one — grounded entirely in tool output, never in the model's memory.
Choosing a model on Hugging Face means opening a dozen tabs and manually reconciling parameter counts, licenses and eval numbers. This exposes that workflow as five tools an LLM agent can call, and — the part that makes it engineering rather than a demo — measures whether the agent picks the right tool, passes the right arguments, and stays grounded.
Related MCP server: Hugging Face MCP Server
┌──────────────────────────────────┐
Claude Desktop ──▶│ server.py (MCP / stdio) │
LangGraph agent ─▶│ langchain_tools.py │──┐
LlamaIndex agent ▶│ llamaindex_tools.py │ │
└──────────────────────────────────┘ │
▼
┌──────────────────────────┐
│ core.py — tool registry │
│ one implementation, │
│ three bindings │
└───────────┬──────────────┘
│
┌─────────────────┬───────────────────┼──────────────────┐
▼ ▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ hf_client.py │ │formatting.py │ │benchmarks.py │ │ rag.py │
│ cache, retry │ │ context │ │ model-index │ │ chunk + BM25 │
│ rate limits │ │ engineering │ │ parsing │ │ / dense │
└──────────────┘ └──────────────┘ └──────────────┘ └──────────────┘The tool logic lives once in core.py; MCP, LangChain and LlamaIndex are thin bindings over it. A tool description tuned against the eval suite improves every binding at once.
Tool | Answers |
| "What models exist for X?" |
| Structured facts: license, size, downloads, benchmarks |
| Side-by-side table of 2–6 models |
| "What's popular right now?" |
| RAG over the written card: training data, limitations, intended use |
Tool results are consumed by an LLM, not a browser. formatting.py converts raw API payloads into the minimum text that still supports a correct recommendation, dropping fields no decision depends on (siblings, spaces, base64 verifyToken blobs) and routing-noise tags like region:us.
Measured against live payloads with tiktoken/cl100k_base:
Sample raw pruned formatted vs raw
-------------------------------------------------------------------------------
distilbert-base-uncased-finetuned-sst-2-english 7778 7754 256 96.7%
search(whisper, automatic-speech-recognition) 4095 3849 293 92.8%
search(sentiment, text-classification) 2028 1795 319 84.3%
-------------------------------------------------------------------------------
TOTAL 18016 17167 1805 90.0%
Mean tokens per tool call: 226Reproduce with python scripts/measure_context_savings.py. Note raw is already the expand[]-narrowed response, so this understates the saving versus a naive wrapper.
Most Hub wrappers rank by downloads — a popularity contest. This parses the model-index block for published evaluation results, and distinguishes HF-verified metrics from self-reported ones (marked *), because self-reported numbers aren't comparable across authors:
Benchmarks (published in model card):
- glue (sst2): Accuracy 91.06%, Precision 89.78%, Recall 93.02%, AUC 97.17%Metrics where lower is better (WER, perplexity, MAE) are recognised so comparisons don't rank a high error rate as the winner.
Model card READMEs run 5–40 KB — too large to paste into context to answer one question. Cards are chunked on Markdown structure (so each chunk is topically coherent and carries its heading as a citation), then retrieved.
Two design decisions worth noting:
Headings are indexed with their body. A question about "limitations" often shares no vocabulary with the prose underneath, matching only the heading. Indexing headings separately was a real bug caught by the test suite.
Fenced code blocks are tracked, so # Load the model inside a Python example isn't parsed as a section heading.
BM25 is the default (pure Python, no dependencies, no model download). Install .[rag] to switch to dense embeddings for paraphrase-style questions.
TTL cache — compare_models and follow-up turns re-request the same models constantly; caching keeps the anonymous rate limit (500 req / 5 min) out of reach.
Retry with backoff on 429s and transient network errors.
Tools never raise. A raised exception breaks the agent loop; a returned message lets the model recover and explain.
Correct error semantics — the Hub returns 401, not 404, for missing models (so private repo names can't be probed).
Building a tool server is half the job. The half that matters is whether an LLM uses it correctly. evals/ measures three things across 22 cases in five groups, programmatically — no LLM-as-judge, so runs are deterministic and cheap:
Metric | What it catches |
Tool-selection accuracy | Right tool first; forbidden tools avoided |
Parameter accuracy | Correct Hub task tag (models love inventing |
Groundedness | Every model ID in the answer appeared in a tool result — anything else is a hallucination |
Case groups: routing (the floor), disambiguation (two tools plausibly apply), parameters (arguments are the hard part), grounding (tempting the model to answer from memory), robustness (bad IDs, vague asks).
The harness is provider-agnostic, so the same cases and the same scoring run against any backend — including free ones. Credentials load from a gitignored .env.
python evals/run_evals.py --list-providers
python evals/run_evals.py --provider github --output evals/results-gpt4o.json # free
python evals/run_evals.py --provider anthropic --output evals/results-claude.json22 cases. Raw outputs are committed in evals/ — every number below is reproducible from them.
Metric | Claude Sonnet 5 | GPT-4o-mini |
Tool-selection accuracy | 94.7% | 94.7% |
Parameter accuracy | 100% | 100% |
Groundedness | 100% | 100% |
Cases fully passed | 95.5% | 90.9% |
Tool-selection accuracy by group:
Group | Claude Sonnet 5 | GPT-4o-mini |
routing | 100% | 100% |
disambiguation | 80% | 80% |
parameters | 100% | 100% |
grounding | 100% | 100% |
robustness | 100% | 100% |
The descriptions in core.TOOLS were rewritten against measured failures — each
negative clause ("do not call get_model_card repeatedly", "do not use this for
license questions") traces to a specific case that failed.
GPT-4o-mini improved substantially: tool-selection 78.9% → 94.7%, parameter accuracy 87.5% → 100%, cases fully passed 81.8% → 90.9%.
Claude did not improve — it was already at the ceiling these cases can measure (94.7%), and finished exactly where it started. Reporting that honestly matters more than the headline: prompt tuning has diminishing returns against a model that is already choosing correctly, and the remaining 5.3% is one genuinely ambiguous case rather than a fixable defect.
One intermediate version actively regressed Claude, 94.7% → 89.5%. A clause
added to ask_about_model to fix a GPT-4o failure — "call get_model_card first
if the model may not exist" — caused Claude to run a metadata lookup before
every prose question, breaking two disambiguation cases that previously
passed. Narrowing the condition recovered it and lifted disambiguation 60% →
80%.
The lesson worth taking from this: tool-description changes are not monotonic across models. A clause that repairs a weaker model's behaviour can degrade a stronger one's, and without a per-model eval the regression is invisible.
1. A false-positive groundedness metric. The first run scored Claude at 20%
groundedness, which read as a serious hallucination problem. It wasn't: model
IDs were extracted with a naive word/word regex, so ordinary prose —
positive/negative, size/hardware, POS/NEG — counted as fabricated IDs. The
metric punished whichever model wrote the most explanatory prose. Claude's true
score was 100%. A metric that makes a good model look bad is more dangerous than
no metric, because it reads as a finding.
2. Scoring that punished good behaviour. forbid_tool originally meant
"never call this tool". But calling the right tool first and then fetching
supporting detail is good agent behaviour, and it was being marked wrong. It now
means "must not be called instead of the expected tool" — scored on order, not
presence. This moved Claude 89.5% → 94.7% and left both GPT models unchanged,
confirming it corrected a specific mis-scoring rather than inflating everything.
3. A real crash in the tool layer. gpt-4o-mini sent limit="10" as a
string, and min(limit, MAX) raised TypeError comparing str to int. The
agent saw tool crashes, gave up, and returned an empty answer — surfacing as a
groundedness failure. Tool arguments come from a language model, not a type
checker, so core.py now coerces them; compare_models likewise accepts a
comma-separated string where a list was specified. This bug was reachable from
Claude Desktop and no unit test would have found it.
core.TOOLS is the single source of truth for tool and parameter
descriptions. It was not always: the eval scored one set of strings while the
MCP server advertised its docstrings, so tuning against the evals changed
nothing in production. The MCP server now takes both from the registry (via
Field annotations, since the SDK does not surface docstring Args: blocks in
the schema), the eval derives its schemas from the same place, and
tests/test_core.py asserts they cannot drift apart.
evals/rescore.py re-applies current scoring to past runs by replaying their
recorded tool calls, so fixing a metric costs nothing and old numbers stay
comparable:
python evals/rescore.py evals/*.json --writeCaveat worth stating: n=22, with 3–5 cases per group, so one case moves a group by 20–33 points. Group-level numbers are directional, not statistically solid.
git clone https://github.com/farnoosh-afshinrad/model-card-chat-mcp.git
cd model-card-chat-mcp
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytestAdd to claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):
{
"mcpServers": {
"model-card-chat": {
"command": "/absolute/path/to/model-card-chat-mcp/.venv/bin/python3",
"args": ["-m", "model_card_chat.server"]
}
}
}Restart Claude Desktop; five tools should appear. Claude Desktop has no cwd option, which is why pip install -e . matters — it makes the package importable regardless of the launch directory.
mcp dev model_card_chat/server.py # MCP Inspector UI
python scripts/smoke_test.py # verify tools are advertised
python -m model_card_chat.server \
--transport streamable-http --host 0.0.0.0 --port 7860 # as a servicestatic/ is a zero-dependency page over the same five tools — the shape anyone
can actually try, since an MCP endpoint needs a client rather than a browser.
It calls the Hugging Face API straight from the browser (the Hub allows CORS),
so there is no backend, no API key and no cold start.
python -m http.server 8000 --directory static # http://localhost:8000Deployed at huggingface.co/spaces/Farnooshrad/model-card-chat. Static Spaces are free, whereas Gradio and Docker Spaces now require PRO:
pip install huggingface_hub && hf auth login
python deploy/push_to_space.pydocker build -t model-card-chat .
docker run -p 7860:7860 model-card-chatThe image runs the streamable-HTTP transport bound to 0.0.0.0 — the FastMCP
default of 127.0.0.1 would start healthily and still be unreachable from
outside the container. CI builds the image and asserts it answers an MCP
handshake, so that failure mode can't ship silently.
See deploy/README.md for free hosting (Hugging Face Spaces, Render, Fly.io) and a note on why a public MCP endpoint is a weaker portfolio asset than the demo video.
from langgraph.prebuilt import create_react_agent
from langchain.chat_models import init_chat_model
from model_card_chat.integrations.langchain_tools import get_langchain_tools
agent = create_react_agent(
init_chat_model("claude-sonnet-5", model_provider="anthropic"),
get_langchain_tools(),
)"What's a good small model for sentiment analysis?"
"Compare bert-base-uncased, distilbert-base-uncased and roberta-base."
"What's trending for text-to-image right now?"
"What data was openai/whisper-large-v3 trained on?" (hits the RAG path)
"What are the known limitations of Whisper?" (retrieves the limitations section)
static/ # zero-dependency web UI (Hugging Face Space)
model_card_chat/
core.py # tool implementations + registry (single source of truth)
server.py # MCP server; tool docstrings are the prompt
hf_client.py # HTTP: caching, retries, rate limits
formatting.py # context engineering
benchmarks.py # model-index parsing
rag.py # chunking + BM25 / dense retrieval
integrations/ # LangChain + LlamaIndex bindings
evals/ # tool-use eval harness + cases
tests/ # 58 tests, fully mocked
scripts/ # token measurement, MCP smoke testExtra | Adds |
| Dense embedding retrieval ( |
| Eval harness ( |
| Framework adapters |
| pytest, respx, ruff, tiktoken |
Eval results tracked over time in CI
Re-run the GPT-4o column once the free quota resets
Dataset and Spaces tools
MIT
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?"
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes | ||
| question | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that it returns relevant excerpts with section headings as citations and that answers should be based only on those excerpts. No annotations to contradict.
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?
Concise, well-structured: one-sentence purpose, then usage guidance, then parameter details. No unnecessary 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?
Complete for a QA tool: explains purpose, when to use, what returns (excerpts with citations), and parameter details. Output schema exists but is not needed to understand behavior.
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?
With 0% schema coverage, the description adds full meaning: explains both parameters, provides format and examples (e.g., 'openai/whisper-large-v3', 'what data was it trained on?').
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 verb 'answer' and resource 'model card README', and distinguishes from sibling tools by noting that get_model_card is for structured facts.
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?
Explicitly says when to use (free-text questions about prose documents) and when not to (structured facts, use get_model_card), providing clear alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| model_ids | Yes |
| 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 full burden. It indicates comparison (likely read-only) and specifies the model ID range, but lacks details on error handling, rate limits, or output format. Adequate but not exhaustive.
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 three sentences: purpose, usage guidance, and parameter description. No wasted words, front-loaded with key action.
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 output schema exists, the description need not detail return values. It covers comparison dimensions and model count range. Nearly complete, though missing potential error scenarios.
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, but the description adds meaning by specifying 'List of 2 to 6 Hugging Face model IDs', which compensates for the schema's lack of explanation. High value added.
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 compares 2-6 models side by side on specific attributes (size, license, downloads, benchmark scores), distinguishing it from siblings like get_model_card that handle individual 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?
Explicitly states 'Use this whenever the user is choosing between named alternatives' and mentions it is 'cheaper and easier to read than calling get_model_card repeatedly', providing clear context and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description does not disclose any behavioral traits beyond core function. Could mention it's a read-only remote call, but not critical.
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?
Two short, well-structured paragraphs with no wasted words. Front-loaded with key facts, then usage, then parameter detail.
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?
With output schema present, description covers all needed context: what is returned, when to use, parameter explanation. Complete for this simple lookup 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?
Provides example format for model_id, explains short form resolution. Adds significant value beyond the schema (0% coverage).
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?
Clearly states it retrieves structured facts (task, license, params, downloads, benchmarks) about a specific model. Distinguishes from siblings like ask_about_model 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?
Explicitly tells when to use (user names a specific model, before recommending) and when not to (free-text questions, use ask_about_model).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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).
| Name | Required | Description | Default |
|---|---|---|---|
| task | No | ||
| limit | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description fully bears the transparency burden. It describes the tool as listing trending models by score, which implies a read-only operation. No destructive or rate-limiting info needed given simplicity.
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?
Concise, well-organized: first sentence states purpose, then usage guideline, then parameter details. Every sentence adds value. No fluff.
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 and existence of output schema, the description is largely complete. Covers purpose, usage, and parameters. Could mention that results are live, but not essential.
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 no descriptions (0% coverage), so description compensates fully. Explains task parameter as 'optional exact Hub pipeline tag' with examples like text-to-image, and limit parameter with default (10) and cap (50).
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?
Clearly states it lists models trending on Hugging Face by live trending score. Distinguishes from search_models by specifying it's for 'what's popular' rather than keyword search.
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?
Explicitly provides usage context: 'Use this for "what's popular", "what's new", or "what are people using lately".' Directly names alternative tool search_models for specific keyword or exhaustive search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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).
| Name | Required | Description | Default |
|---|---|---|---|
| task | No | ||
| limit | No | ||
| query | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It clearly describes the tool's purpose and parameters, but does not explicitly state non-destructiveness or other behavioral traits. However, for a search tool, the description is sufficiently transparent.
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 concise and well-structured: a single purpose sentence, usage guidance sentence, then parameter documentation. 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 the tool's complexity (search with multiple filters) and the presence of an output schema, the description covers all necessary input details and usage context. It is complete for an AI agent to select and invoke correctly.
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 description coverage is 0%, but the description thoroughly explains all parameters: task is described as exact Hub tag with examples, query with free-text examples, limit with default and cap. This adds significant meaning beyond the schema.
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 it finds Hugging Face models matching a keyword and/or task, using specific verbs and resources. It also distinguishes itself from sibling tool get_trending_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?
Explicitly provides when to use this tool versus get_trending_models, stating 'For "what is popular/new right now", prefer get_trending_models instead.'
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. Dates show when Glama detected each change.
v0.2.0ask_about_modelcompare_modelsget_model_cardget_trending_modelssearch_modelsEach 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.
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