Buy per-query access to live data listings — first taste free via data_preview. Listing: pypiwatch: New PyPI package releases & Python dependency updates (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' and 'Returns fund instructions', which implies a purchase transaction with side effects (charging USDC, creating a funding package). This directly contradicts annotations readOnlyHint=true and idempotentHint=true. The description does not disclose the financial impact, what side effects occur, or what 'fund instructions' entail, and it conflicts with the declared safety hints.
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 concise and front-loaded with the core action and listing details. The two sentences are efficient and include the price and prerequisite. It could be improved by a clearer separation of purpose and lifecycle, but it is not padded.
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?
There is no output schema, so the description must explain what the tool returns; 'fund instructions' is too vague. The description also does not explain the overall purchase/attach/fund flow or how this tool relates to data_session_fund and data_session_attach_escrow, leaving an agent without enough context to correctly sequence the calls.
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 description coverage is 100%, and the schema already explains session_id as 'UUID of a data session you opened (from data_session_open)'. The description adds no new parameter semantics beyond mentioning 'after data_session_open', which is already in the schema field description. Baseline 3 is appropriate since the schema carries the load.
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 states a clear verb ('Buy') and resource ('per-query access to live data listings') and names the specific listing (pypiwatch). It partially distinguishes itself from data_preview by positioning it as a free taste, but it does not clearly differentiate itself from data_session_fund or data_session_attach_escrow, which could overlap.
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 gives a clear prerequisite ('after data_session_open') and suggests data_preview as a free alternative, so the agent knows the basic flow. However, it does not explain when to use this tool versus the closely related siblings data_session_fund or data_session_attach_escrow, which is a meaningful gap.
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.
Multiple tools are near-duplicates in the data-session funding flow (data_session_fund, data_session_funding_package, data_session_attach_escrow) and guidance tools overlap (a2awire_guide vs get_recommended_action). A caller looking for PyPI release information cannot easily distinguish the relevant query tools from the marketplace and onboarding tools.
There is a data_session_* cluster and some get_* names, but the set mixes bare verbs (register), gerund-style names (check_earnings, find_paid_work), compound verbs (hire_and_execute), and prefixed nouns (a2awire_guide, onboard_start). The naming is not chaotic enough for 1, but it lacks a consistent convention.
16 tools is already on the heavy side, and the majority concern agent-marketplace onboarding, escrow, hiring, and earnings rather than PyPI package tracking. The count would be plausible for an A2AWire platform server, but it is far too large and unfocused for the advertised PyPI Release Tracker.
The stated purpose is tracking new PyPI releases and dependency updates, yet there are no dedicated tools for listing packages, fetching release details, or monitoring dependencies. The only data-related surface is a generic data_preview/data_session_query pair, leaving the actual domain essentially uncovered.