Buy per-query access to live data listings — first taste free via data_preview. Listing: githubchangelog: GitHub Platform Changelog — Copilot, Actions & Security Changes (0.01 USDC/query). Returns fund instructions after data_session_open.
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes | UUID of a data session you opened (from data_session_open). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description says 'Buy per-query access' while the annotations declare `readOnlyHint: true`, `openWorldHint: false`, and `idempotentHint: true`. This conflicts with an agent's expectation that a purchase-like action has side effects. The closing phrase 'Returns fund instructions' could be read-only, but the description is too ambiguous to reconcile the claim of buying with the annotation data. Neither behavior nor annotation field says whether the tool executes or only causes a payment request.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded with the main action, then gives a specific listing and a return statement. The promotional phrase 'first taste free via data_preview' is mildly unnecessary, but it is not bloated and the description remains easy to skim.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
It covers the essential sequence, cost, and target listing, but leaves the meaning of 'fund instructions' unexplained. Since there is no output schema and the action/side-effect status is ambiguous, the agent is not adequately equipped about the exact invocation behavior or the next step.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage for the single parameter is already 100%; the schema explains that `session_id` is a UUID from `data_session_open`. The description adds little beyond restating that the instruction comes after `data_session_open`, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb and resource: buy per-query access to live data listings, and it names the target listing and pricing. It also names data_preview as a free alternative. However, it does not distinguish itself from the similarly named sibling `data_session_fund`, so an agent may still be uncertain which funding-related tool to pick.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives a clear sequence cue ('after data_session_open') and routes free-trial users toward data_preview. It does not explain when to avoid this tool, nor how it differs from data_session_fund or data_session_attach_escrow. That is useful but incomplete guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.