list_approved_platformsThe official list of Plateformes Agréées (PA, formerly PDP) that DGFiP has approved to transmit invoices under the French e-invoicing mandate, from the published open dataset. Optionally filtered by name or SIREN. FREE and needs no API key.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional filter — platform name or SIREN. Omit for the full list. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds value beyond annotations by stating the data comes from a 'published open dataset' and that it is 'FREE and needs no API key', which are behavioral/access traits not covered by annotations. No contradictions.
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 concise sentences that are front-loaded with the core purpose. Every phrase adds value: official list, data source, optional filter, and access requirements. No redundant or filler text.
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 simple read-only list tool with one optional filter, the description fully covers usage, data source, and access constraints. There is no output schema, but the tool's return value is a list of platforms, which is evident. The sibling tools are clearly distinct, so no additional context is needed.
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 only parameter ('query') is fully described in the schema as an optional filter by platform name or SIREN, which the description echoes. With 100% schema coverage, the description does not need to add much; the baseline 3 is appropriate. No additional semantic nuance is provided beyond what the schema already contains.
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 uses a specific verb ('list') and clearly identifies the resource ('DGFiP-approved French e-invoicing platforms'). It also specifies the data source ('published open dataset') and distinguishes itself from sibling tools by focusing on listing approved platforms rather than checking readiness or validating invoices.
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 explains when to use the tool (to get the official list of approved platforms) and how to optionally filter by name or SIREN. It also notes that it is FREE and requires no API key, which helps in choosing it over paid/key-requiring tools. However, it does not explicitly mention when to use an alternative tool, though the sibling names make that implicit.
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.
Each tool targets a distinct resource and action: validation (two input formats), generation, rule explanation, company readiness, VIES status, platform listing, key issuance, and upgrade link. Even the two validation tools are clearly split by input type (JSON vs XML), and the descriptions explicitly prevent confusion.
All tool names follow a consistent verb_noun snake_case pattern (check_, explain_, generate_, get_, issue_, list_, validate_). The verbs are precise and the nouns clearly indicate the target, with no mixing of conventions or vague terms.
Nine tools is well within the ideal scope for a specialized e-invoicing compliance server. Each tool serves a distinct purpose in the workflow—validate, generate, explain, check company/VIES/platform status, and manage API keys—without redundancy or bloat.
The tool surface covers the full lifecycle of e-invoicing compliance: validation (JSON and XML), generation, rule explanation, and external status checks (company, VIES, approved platforms). API key management and upgrade paths are also included. No obvious missing operations for the stated purpose.