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GitHub Platform Changelog — buy per-query in-session (githubchangelog)

data_session_query

Buy per-query access to live data listings — first taste free via data_preview. Listing: githubchangelog: GitHub Platform Changelog — Copilot, Actions & Security Changes at 0.01 USDC per query (max 20 queries/session). Sequence: data_session_open → data_session_fund → data_session_query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNo
queryYes
session_idYesUUID of a data session you opened (from data_session_open).
sandbox_receiptNoLet the platform sign the DeliveryReceipt with your provisioned sandbox wallet — testnet sandbox wallets only.
delivery_receiptNo

Schema Changelog

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

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already mark the tool as non-read-only, non-idempotent, and non-destructive, so the description does not need to repeat that. It adds useful behavioral context: per-query billing at 0.01 USDC, a maximum of 20 queries per session, and a required preceding funding step. It does not disclose failure modes or what happens on insufficient funds, but the added billing and session constraints are valuable.

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 short, dense, and without filler. It front-loads the core purpose, then gives listing specifics and the session sequence in compact form. The listing-specific detail is arguably a minor distraction, but overall the structure is efficient and scannable.

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?

The tool has five parameters, no output schema, and thin annotations, so the description must provide enough context for correct invocation. It supplies the required session sequence and billing limits, but omits parameter semantics and any description of the query response or result shape. This leaves meaningful gaps for an agent deciding how to form a valid query.

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

Parameters2/5

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

With only 40% schema description coverage, the description carries a responsibility to explain unclear parameters. It does not mention k, sandbox_receipt, or delivery_receipt, and only vaguely references session flow. The schema does describe session_id, sandbox_receipt, and delivery_receipt, but key parameters like query and k remain under-explained across both sources.

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 names a clear action ('Buy per-query access') and ties it to a specific listing and session-based sequence. It is distinguishable from sibling tools like data_session_open and data_session_fund by being positioned as the final query step. The slight ambiguity is that 'buy per-query access' could sound like a purchase/funding action rather than a query execution.

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 explicitly gives the sequence 'data_session_open → data_session_fund → data_session_query' and mentions data_preview as the free first taste. This provides enough contextual guidance on when this tool is appropriate relative to its siblings, though it does not state explicit exclusions or when not to use it.

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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