Buy per-query access to live data listings — first taste free via data_preview. Listing: ghtrend: fast-growing new GitHub repositories (0.01 USDC/query). Platform-executes funding so you can data_session_query.
| 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?
Annotations mark this as a write operation with idempotentHint=true; the description adds meaningful context by disclosing the cost (0.01 USDC/query) and stating that 'platform-executes funding' actually occurs. This alerts the agent that the tool triggers a real funding action. No contradiction with annotations.
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 compact and front-loaded with the core action, followed by the free preview hint and a specific listing/price. It avoids unnecessary filler, though the phrase 'Platform-executes funding so you can data_session_query' is slightly awkward. Overall, every sentence earns its place.
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?
For a single-parameter tool with full schema coverage, the description covers the purpose, cost, listing, and the downstream query step. It doesn't describe the return value or confirmation format, and there is no output schema, but the invocation path is straightforward enough that this is a minor gap.
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?
The single parameter session_id is fully covered by the schema with a clear description: 'UUID of a data session you opened (from data_session_open).' The tool description adds no parameter-level details beyond this, so the schema does the heavy lifting. Baseline 3 is appropriate for full schema coverage.
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 action: buy per-query access to live data listings and execute funding for a data session. It names the concrete listing (ghtrend) and price, and references data_preview and data_session_query to distinguish the tool from related siblings. This gives an agent a clear picture of what the tool does and where it fits.
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?
The description provides useful sequencing: try data_preview first for free, then fund, then use data_session_query. It makes the intended context evident without much inference. It doesn't explicitly discuss alternatives like data_session_funding_package, but the workflow guidance is clear enough for safe selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools cluster around the same actions: data_session_fund, data_session_funding_package, and data_session_attach_escrow all describe paying for session access; a2awire_guide and get_recommended_action both tell the agent what to do next; discover_agents and hire_and_execute both search by capability. Only a few tools like data_preview, check_earnings, and verify_contract are cleanly distinct.
All names use snake_case, but there is no consistent verb_noun or action pattern: data_preview and a2awire_guide are nouns, data_session_open is object+verb, data_session_funding_package is object+gerund+noun, and hire_and_execute is verb+verb. The fund/funding_package pair is especially confusing.
16 tools is heavy for a server whose stated purpose is a single repo-trend data listing, and most tools are unrelated A2AWire marketplace, onboarding, and escrow operations. The data-access path could be served by 4-5 tools, so the extra redundant payment and meta-navigation tools make the set over-sized.
The GitHub data query path (preview -> open -> fund -> query) is mostly covered, but the broader exposed surface has a clear dead end: find_paid_work instructs callers to call start_job, yet no start_job tool exists. There is also no way to manage sessions or agents beyond basic onboarding, so agents following the provided recommendations can fail.