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HuggingFace New Dataset Release Tracker (hfdatasets)

data_preview

Read-onlyIdempotent

✅ No API key needed — call this now. Listing: HuggingFace New Dataset Release Tracker (hfdatasets). Price 0.01 USDC/query (max 20 queries/session). Sample questions: What are the newest HuggingFace datasets released today?; Show me recent datasets tagged text-classification.. FREE preview — no key, no payment. Try one of the sample questions now.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugNoPublic listing slug. Defaults to the routed session's listing when connected via /mcp/data/{slug}/http.
questionNoOptional free-text question you'd ask this data (echoed back).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.7/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

It adds cost/quota context (0.01 USDC/query, max 20 queries/session) and no-key simplicity beyond the annotations. But it undercuts that disclosure by also claiming 'FREE preview — no key, no payment,' which is directly confusing/contradictory with the stated per-query price and leaves cost behavior ambiguous.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The text is repetitive: 'No API key needed' and 'FREE preview — no key, no payment' say the same thing, and 'Try one of the sample questions now' adds no information. The emojis and CTA phrasing push the description toward marketing rather than a focused tool spec; the useful payload is buried in a few short clauses.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a low-complexity read-only preview tool with optional parameters, the description covers the key decision factors (free/no-key, listing, sample input). It is still incomplete about expected output or return shape, and there is no output schema; a precise one-line statement of what the preview returns (e.g., metadata rows) would make it complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and both parameters are optional, so the schema carries the burden; baseline 3 applies. The sample questions add useful illustration for the 'question' parameter, but the description adds no clarification for 'slug' or the echo-back behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies a concrete resource (HuggingFace New Dataset Release Tracker) and provides sample questions, so it is not a tautology. But it never states the core function with a clear verb (e.g., 'returns a preview of...') and reads like a promotional call to action ('call this now', 'Try one of the sample questions now'). A reader must infer that this tool returns a listing preview from the name and examples.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly indicates there is no API key required and frames the call as a free preview, which gives some contextual basis for when to use it. However, it provides no comparison to sibling tools (like data_session_query or check_earnings) and no exclusions or 'when not to use' guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.1/5.0
Disambiguation2/5

The data access tools overlap heavily: data_session_fund and data_session_funding_package both describe funding but one executes it and the other just returns instructions, while data_preview is easily mistaken for data_session_query. a2awire_guide and get_recommended_action also both serve as navigation/recommendation tools, so agents must read descriptions carefully to pick the right one.

Naming Consistency3/5

Most tools use snake_case verb-first names like check_earnings, discover_agents, and register, and the session tools mostly follow data_session_<action>. However, data_preview is object-verb, data_session_funding_package is a noun phrase, and a2awire_guide is a bare noun, making the overall naming pattern mixed but still readable.

Tool Count2/5

16 tools is borderline on its own, but at least 10 of them are generic A2AWire marketplace tools unrelated to the named HuggingFace dataset tracker. The actual dataset-access surface needs only a handful of tools, so the set feels inflated and mismatched to the server's apparent purpose.

Completeness2/5

The paid query workflow includes preview, open, fund, and query, but there is no session management, refund, quota inspection, or dedicated dataset discovery/metadata tool beyond an opaque natural-language query. The many unrelated marketplace tools don't fill these gaps and instead obscure the promised HuggingFace dataset release tracking domain.

Resources