Buy per-query access to live data listings — first taste free via data_preview. Listing: fdarecall: FDA recall enforcement database (food, drug, device) (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?
Annotations already declare readOnlyHint and idempotentHint, and the description complements them by clarifying that the tool 'Returns fund instructions' rather than executing a purchase. It also adds the listing name, price, and prerequisite session, giving useful behavioral context beyond the 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 two sentences with no wasted words. The main purpose is front-loaded, followed by concrete details about the listing and price, and the prerequisite is clearly stated.
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 strong annotations, the description is largely complete: it states what it does, the specific listing, the cost, when it returns instructions, and the prerequisite session. It could be slightly more explicit about the relationship to data_session_fund, but that is not essential for correct invocation.
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 input schema has 100% coverage and already explains session_id as a UUID from data_session_open. The description's mention of 'after data_session_open' reinforces this but adds no new parameter-level meaning, so the baseline score applies.
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 the verb ('Buy') and resource ('per-query access to live data listings'), and it identifies the specific listing and price. However, it doesn't explicitly distinguish itself from the sibling data_session_fund, which could cause some ambiguity about which funding tool to invoke.
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 context: use data_preview for a free taste, and this tool returns fund instructions after data_session_open. However, it doesn't explicitly say when not to use this tool or contrast it with data_session_fund or data_session_attach_escrow, leaving the routing partially implied.
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.
The tool set blends FDA recall query tools with generic A2AWire marketplace tools, making purpose boundaries unclear. Multiple tools overlap: data_session_fund, data_session_funding_package, and data_session_open all describe payment/session setup, while a2awire_guide and get_recommended_action both serve as navigational helpers.
Some tools follow a verb_noun pattern (data_session_open, data_session_query, discover_agents), but others are inconsistent or vague (a2awire_guide, check_earnings, register, verify_contract). The mix of domain-specific and platform tool naming with no coherent convention makes the surface feel disjointed.
16 tools is not inherently excessive, but most are unrelated to FDA recall alerts; they cover agent registration, onboarding, escrow, hiring, and earnings. Only a handful actually concern the stated FDA recall data domain, so the count is poorly scoped for the server's apparent purpose.
For an FDA recall alert server, the surface is severely incomplete: there is no direct recall listing, search, filtering, or detail tool, only a generic data_session_query with preview. The remaining tools are platform infrastructure, leaving the actual recall domain shallow and dependent on an opaque paid query flow.