Eau et assainissement DOM : crise eau Guadeloupe (60% pertes réseau), SMGEAG, 1,2 Mds€ travaux 2022-2030, appels d'offres en cours, dessalement, chlordécone. Prix : 0.050 USDC.
| Name | Required | Description | Default |
|---|---|---|---|
| payment_tx_hash | No | Hash tx USDC Base (0x+64hex). Absent = instructions paiement. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already signals a safe read operation. The description adds informational context by specifying the geographic focus, key entities (SMGEAG), investment scale, and the price of 0.050 USDC, which is relevant for invocation. However, it does not elaborate on return behavior, pagination, or other side effects beyond what the annotation already covers.
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 a compact single sentence, front-loaded with the topic 'Eau et assainissement DOM,' and packs useful facts (loss rate, budget, timeframe, keywords) without unnecessary fluff. It is somewhat dense but appropriately sized for a data-retrieval tool.
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?
Given the tool has no output schema and only one optional parameter, the description partially compensates by listing likely content areas (crisis, tenders, desalination, chlordécone). However, it does not explicitly state the return format, structure, or how to interpret the output, leaving the agent to infer that it returns data about these topics.
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 already documents the only parameter (payment_tx_hash) with a clear description to 100% coverage. The description adds the price point but does not explain payment mechanics or parameter usage beyond the schema. Thus, the schema carries the load, and the description offers minimal additional semantic value.
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 identifies the tool's domain: water and sanitation in French overseas departments (DOM), specifically focusing on Guadeloupe's water crisis, SMGEAG, infrastructure budget, and related issues. It distinguishes itself from sibling tools by topic (e.g., solar, biodiversity, tourism). However, it lacks an explicit verb like 'retrieve' or 'get,' so it reads more as a content summary than a functional statement.
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 no guidance on when to use this tool versus alternatives such as caribbean_get_marches_publics or caribbean_get_financement_dom. It only implies by topic that it is for water/sanitation data, but there is no explicit context on suitable use cases or exclusions.
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.
Most tools have clearly distinct domains (solar, biodiversity, water, etc.), but the pair caribbean_get_marches_publics and caribbean_get_marches_publics_dom overlap significantly, differing only in geographic scope. The sub-group of 'caye' tools (marche, vanille, vetiver) are also thematically similar, though descriptions clarify their specific commodities. Overall, an agent could generally select correctly, but a few boundary cases exist.
The tools overwhelmingly follow a caribbean_get_<subject> convention, with only two exceptions: caribbean_analyse_solaire (uses 'analyse' instead of 'get') and caribbean_list_marketplace (uses 'list' instead of 'get'). This makes the pattern predictable and readable, with minor deviations that are still intuitive. The subject names are consistently descriptive, though some use abbreviations like 'dom' and 'oa' that might require context.
24 tools is on the heavy side for a single server, particularly given that many tools are single-purpose data lookups (e.g., biodiversity, eDNA, tourism). The count is justifiable for a wide-ranging Caribbean data API, but it risks overwhelming an agent with a large surface. It is not excessive enough to be absurd, but it's in the 'heavy' zone.
The server covers multiple domains comprehensively, such as solar energy (tariffs, irradiation, connection, tax credit, financing) and public contracts (Guadeloupe and all DOM). The marketplace meta-tools (llms_txt, list_marketplace, get_provider) ensure discoverability, but there is no general search or aggregation tool. Some areas like agriculture or real estate beyond what's present could be considered gaps, but the core coverage is solid.