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Show HN Launches — buy per-query in-session (showhnwatch)

data_session_open

Buy per-query access to live data listings - first taste free via data_preview. Listing: showhnwatch: Show HN Launch Stream (Hacker News) (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. 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?

It discloses the per-query cost and max session queries, which provides some financial transparency. However, it doesn't mention side effects like creating a blockchain transaction, that the operation is non-idempotent, or that the session requires funding before querying—critical details for a tool that is not read-only.

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 brief and front-loaded with the main purpose, and it avoids excessive detail. The phrasing is somewhat choppy and includes a specific listing name and price, which are arguably extraneous, but overall it is concise and scannable.

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?

It provides a high-level workflow (open, fund, query) and pricing, which gives some context. However, it omits how to specify the listing (slug vs ID), the meaning of optional fields like buyer_address or proof_escrow_id, and any expected return value—leaving significant gaps especially without an output schema.

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?

The description mentions no parameter names, roles, or constraints; it relies entirely on the schema's partial descriptions. Since schema coverage is only 50% and the description does not compensate, an agent gets virtually no help in deciding which of the six optional fields to provide or how they relate.

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 states the action is to open a prepaid session for per-query data access, and it distinguishes the free preview via data_preview. However, it doesn't explicitly name the tool as 'data_session_open' and includes unnecessary listing details that could distract from the core purpose.

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?

It explicitly advises using data_preview for a free taste and then funding/querying after opening, which gives a clear workflow context. It doesn't explicitly contrast with alternative session tools like data_session_fund or data_session_query, but the overall sequence is implied well enough.

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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