Skip to main content
Glama

HuggingFace New Dataset Release Tracker (hfdatasets)

data_session_open

Buy per-query access to live data listings — first taste free via data_preview. Listing: HuggingFace New Dataset Release Tracker (hfdatasets) (0.01 USDC/query (max 20 queries/session)). Open a prepaid session, then fund and query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
listing_idYes
max_queriesNo
open_tx_hashNo
buyer_addressYes
proof_escrow_idNo

Schema Changelog

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

  1. First observed

TDQS

B3.1/5.0
Behavior3/5

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

Annotations provide only generic flags (not read-only, not idempotent, not destructive), so the description must carry the behavioral burden. It discloses that access is prepaid and per-query, includes pricing and session query limits, and indicates that subsequent funding is required. Missing are side effects like requiring/creating an on-chain transaction (suggested by open_tx_hash) or consequences of repeated non-idempotent opens.

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

Conciseness4/5

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

The description is compact and front-loads the primary action and value proposition. The three sentences are efficient and include practical pricing and sequence information. Minor marketing language like 'first taste free' is slightly extraneous but does not undermine clarity.

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

Completeness2/5

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

With no output schema, no parameter descriptions, and minimal annotations, the description alone is insufficient for an agent to correctly call this tool. It explains where this step fits in the workflow but omits parameter meaning, required transaction details, return value, and behavioral caveats for a paid, non-idempotent operation.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not compensate by explaining any of the five parameters. It never mentions listing_id, buyer_address, max_queries, open_tx_hash, or proof_escrow_id. The only parameter-adjacent information is the listing name and per-session query cap, which are not mapped to specific schema properties.

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

Purpose4/5

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

The description clearly identifies the action ('Open a prepaid session') and the resource ('per-query access to live data listings'), and it names the specific listing being purchased. It also distinguishes itself from data_preview by offering a free taste via that sibling, which helps an agent understand this is the paid access path. However, it does not explicitly distinguish itself from other data_session_* siblings like fund or attach_escrow beyond the sequential hint 'then fund and query.'

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

Usage Guidelines4/5

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

The description provides clear ordering context: first try data_preview for free, then open a prepaid session, then fund and query. This implies when to use this tool relative to data_preview, data_session_fund, and data_session_query. It does not state explicit exclusions or alternatives for edge cases, such as when to use data_session_attach_escrow instead, so it falls short of full guidance.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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