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CERT-FR Security Advisories — buy per-query in-session (certfrwatch)

data_session_query

Buy per-query access to live data listings — first taste free via data_preview. Listing: certfrwatch: CERT-FR (ANSSI) National Security Advisories 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
Behavior3/5

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

Annotations are minimal (readOnlyHint=false, idempotentHint=false, destructiveHint=false), so the description carries the burden of disclosing effects. It adds that the operation costs 0.01 USDC per query and is capped at 20 queries/session, which is useful behavioral context. Still, it does not describe what happens with insufficient funds, whether a query consumes session balance, or what side effects occur beyond purchase.

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 compact at two sentences, with the core action and ordering front-loaded. The specific listing and pricing add decision-useful context, though phrasing like 'first taste free' is promotional and slightly nonessential.

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 sequence and pricing make the basic call path clear, and the schema covers session_id and sandbox_receipt. However, there is no output schema, so the return shape is undocumented, and optional parameters like k and delivery_receipt remain undefined. This is a minimum-viable description with clear gaps for a 5-parameter paid operation.

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?

Schema description coverage is only 40%, with session_id and sandbox_receipt described in the schema but query, k, and delivery_receipt left unexplained. The tool description does not compensate: it mentions 'per-query' but adds no meaning for k, delivery_receipt, or query syntax. Since coverage is below 50%, the description needed to clarify these parameters and does not.

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 states a specific action, 'Buy per-query access to live data listings,' and names a concrete listing (CERT-FR advisories). It distinguishes itself from data_preview by contrasting 'first taste free' with paid per-query access. However, it never directly says 'run a query against a funded session,' instead relying on the sequence and title to imply the function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly names data_preview as the free alternative and gives the ordered sequence 'data_session_open → data_session_fund → data_session_query.' This clearly tells an agent when to call this tool and what must happen first, leaving little to inference.

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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