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HuggingFace Trending Models (hftrendingwatch)

data_session_open

Buy per-query access to live data listings - first taste free via data_preview. Listing: HuggingFace Trending Models (hftrendingwatch) (0.01 USDC/query (max 20 queries/session)). Open a prepaid session, then fund and query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
listing_idNoUUID of the listing. Provide exactly one of listing_slug or listing_id.
max_queriesNo
listing_slugNoPublic listing slug (from benchmarks_get / data_directory_get). Provide exactly one of listing_slug or listing_id.
open_tx_hashNo
buyer_addressNoBuyer EVM address. Optional: defaults to your own platform wallet when omitted.
proof_escrow_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior3/5

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

Annotations declare it is a non-read-only, non-destructive, non-idempotent write operation. The description adds some context: it is prepaid, there is a max of 20 queries per session for the shown listing, and a funding step follows. However, it does not disclose what happens on failure, whether sessions expire, or what open_tx_hash/proof_escrow_id are for. With annotations covering safety, a 3 reflects partial but not rich behavioral context.

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 first sentence mixes marketing ('first taste free') with pricing and listing details, and the third sentence is a terse afterthought. Information is present but not well front-loaded or structured; an agent must parse pricing, listing name, and workflow out of a single dense line.

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?

For a 6-parameter, 0-required mutation with no output schema and only 50% schema coverage, the description omits key operational details: what open_tx_hash and proof_escrow_id do, error handling, and the exact funding/query sequence. It gives the buyer's perspective but not enough for an agent to call the tool confidently.

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 description coverage is 50%, and the schema itself documents listing_slug/listing_id and buyer_address defaults. The description adds no parameter-level guidance (e.g., which listing to use, how max_queries interacts with pricing). Baseline 3 is appropriate when the schema carries half the load but the description does not compensate for the rest.

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?

States a specific verb and resource: 'Open a prepaid session' for per-query live data access. It names the listing and pricing, and references the data_preview sibling for the free tier. It's clear what the tool does; the only weakness is that the description is buried under marketing text ('first taste free').

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?

Implies a workflow ('first taste free via data_preview', 'then fund and query') but does not explicitly state when to use this over data_session_attach_escrow or data_session_funding_package. The sequence is implied rather than spelled out as when/when-not 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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