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Hug0x0

mcp-reunion

reunion_get_ftth_coverage

Retrieve FTTH deployment coverage data for La Réunion, including coverage rates and premises counts, to monitor infrastructure rollout and analyze the digital divide.

Instructions

FTTH (Fiber to the Home) deployment coverage for La Réunion region, from ARCEP regional dashboards. Each row is one observation period. Returns: period, total housing units, total businesses, IPE premises (T3 2022 sum across all OIs), best premise estimate (T2 2022), coverage rate %, deployment notes. Useful for digital-divide analysis, infrastructure rollout monitoring, telecom-investment tracking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for 'reunion_get_ftth_coverage'. It queries the 'arcep_regions' dataset from the OpenDataSoft API filtering for "La Réunion", maps API fields (periode, logements, etablissements, nombre_locaux_ipe_t3_2022_somme_tous_oi, meilleure_estimation_des_locaux_t2_2022, taux_de_couverture, deploiements) to a clean result, and returns JSON.
    server.tool(
      'reunion_get_ftth_coverage',
      'FTTH (Fiber to the Home) deployment coverage for La Réunion region, from ARCEP regional dashboards. Each row is one observation period. Returns: period, total housing units, total businesses, IPE premises (T3 2022 sum across all OIs), best premise estimate (T2 2022), coverage rate %, deployment notes. Useful for digital-divide analysis, infrastructure rollout monitoring, telecom-investment tracking.',
      {},
      async () => {
        try {
          const data = await client.getRecords<RecordObject>(DATASET_FTTH, {
            where: `nom_region = ${quote('La Réunion')}`,
            limit: 20,
          });
          return jsonResult({
            total_rows: data.total_count,
            coverage: data.results.map((row) => ({
              period: pickString(row, ['periode']),
              housing_total: pickNumber(row, ['logements']),
              businesses_total: pickNumber(row, ['etablissements']),
              ipe_premises_t3_2022: pickNumber(row, ['nombre_locaux_ipe_t3_2022_somme_tous_oi']),
              best_estimate_t2_2022: pickNumber(row, ['meilleure_estimation_des_locaux_t2_2022']),
              coverage_rate_pct: pickNumber(row, ['taux_de_couverture']),
              deployments: pickString(row, ['deploiements']),
            })),
          });
        } catch (error) {
          return errorResult(error instanceof Error ? error.message : 'Failed to fetch FttH coverage');
        }
      }
    );
  • Input schema for the tool — an empty object literal {}, meaning the tool takes no parameters.
    {},
  • The registerTelecomTools function that registers telecom tools (including reunion_get_ftth_coverage) with the MCP server via server.tool().
    export function registerTelecomTools(server: McpServer): void {
  • Import of registerTelecomTools from telecom module.
    import { registerTelecomTools } from './telecom.js';
  • Invocation of registerTelecomTools(server) during tool registration.
    registerTelecomTools(server);
Behavior3/5

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

No annotations are provided. The description discloses the return fields and that each row is one observation period, but does not explicitly state it is a read-only operation or mention any behavioral traits like auth requirements or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences with no redundant words. It front-loads the core purpose and data source, lists return columns, and ends with use cases. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description lists all return fields (period, housing units, etc.). It provides data source context and use cases. It is complete for a simple retrieval tool, though could mention if results are limited or aggregated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so the description naturally does not discuss them. Baseline of 4 is appropriate since the tool requires no configuration.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns FTTH deployment coverage for La Réunion region, specifying the data source (ARCEP regional dashboards) and the verb 'get'. It distinguishes from sibling tools by focusing on a specific telecom infrastructure dataset.

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

Usage Guidelines4/5

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

The description explicitly lists use cases: digital-divide analysis, infrastructure rollout monitoring, telecom-investment tracking. It does not provide when-not-to-use or alternatives, but the use cases are clear and appropriate.

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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