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Hug0x0

mcp-reunion

reunion_list_5g_sites

Retrieve 5G cell sites in La Réunion with operator, frequency bands, commune, and release date. Supports coverage analysis and infrastructure mapping.

Instructions

List mobile 5G cell sites deployed across La Réunion, sourced from ARCEP open data (the French telecoms regulator). Each row is one operator-station combination. Returns operator name, operator site ID, ANFR station ID, active frequency bands (MHz), commercial-release date for 5G service, commune, EPCI. Useful for coverage analysis, infrastructure mapping, operator comparison, real-estate / connectivity studies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
operatorNoOperator name prefix match. Examples: "Orange", "SFR", "Bouygues Telecom", "Free Mobile"
communeNoCommune name prefix match
frequencyNoFrequency band substring match. Examples: "700" (700 MHz), "2100" (2.1 GHz), "3500" (3.5 GHz, the main 5G band)
limitNoMax sites to return (1-500, default 100)

Implementation Reference

  • The tool 'reunion_list_5g_sites' is registered via server.tool() inside registerTelecomTools(). The registration includes the tool name, description, Zod schema for input params (operator, commune, frequency, limit), and the handler function.
    export function registerTelecomTools(server: McpServer): void {
      server.tool(
        'reunion_list_5g_sites',
        'List mobile 5G cell sites deployed across La Réunion, sourced from ARCEP open data (the French telecoms regulator). Each row is one operator-station combination. Returns operator name, operator site ID, ANFR station ID, active frequency bands (MHz), commercial-release date for 5G service, commune, EPCI. Useful for coverage analysis, infrastructure mapping, operator comparison, real-estate / connectivity studies.',
        {
          operator: z.string().optional().describe('Operator name prefix match. Examples: "Orange", "SFR", "Bouygues Telecom", "Free Mobile"'),
          commune: z.string().optional().describe('Commune name prefix match'),
          frequency: z.string().optional().describe('Frequency band substring match. Examples: "700" (700 MHz), "2100" (2.1 GHz), "3500" (3.5 GHz, the main 5G band)'),
          limit: z.number().int().min(1).max(500).default(100).describe('Max sites to return (1-500, default 100)'),
        },
        async ({ operator, commune, frequency, limit }) => {
          try {
            const data = await client.getRecords<RecordObject>(DATASET_5G, {
              where: buildWhere([
                operator ? `op_name LIKE ${quote(`${operator}%`)}` : undefined,
                commune ? `com_name LIKE ${quote(`${commune}%`)}` : undefined,
                frequency ? `frequency LIKE ${quote(`%${frequency}%`)}` : undefined,
              ]),
              limit,
            });
            return jsonResult({
              total_sites: data.total_count,
              sites: data.results.map((row) => ({
                operator: pickString(row, ['op_name']),
                site_id: pickString(row, ['op_site_id']),
                anfr_station_id: pickString(row, ['anfr_station_id']),
                frequency_bands_mhz: pickString(row, ['frequency']),
                commercial_release: pickString(row, ['release_date_5g']),
                commune: pickString(row, ['com_name']),
                epci: pickString(row, ['epci_name']),
              })),
            });
          } catch (error) {
            return errorResult(error instanceof Error ? error.message : 'Failed to fetch 5G sites');
          }
        }
      );
  • The handler function for 'reunion_list_5g_sites'. It builds an ODSQL WHERE clause from optional filters, queries the OpenDataSoft API for dataset 'sites-mobiles-5g-a-la-reunion', maps results to a structured response (operator, site_id, anfr_station_id, frequency_bands_mhz, commercial_release, commune, epci), and returns JSON.
    async ({ operator, commune, frequency, limit }) => {
      try {
        const data = await client.getRecords<RecordObject>(DATASET_5G, {
          where: buildWhere([
            operator ? `op_name LIKE ${quote(`${operator}%`)}` : undefined,
            commune ? `com_name LIKE ${quote(`${commune}%`)}` : undefined,
            frequency ? `frequency LIKE ${quote(`%${frequency}%`)}` : undefined,
          ]),
          limit,
        });
        return jsonResult({
          total_sites: data.total_count,
          sites: data.results.map((row) => ({
            operator: pickString(row, ['op_name']),
            site_id: pickString(row, ['op_site_id']),
            anfr_station_id: pickString(row, ['anfr_station_id']),
            frequency_bands_mhz: pickString(row, ['frequency']),
            commercial_release: pickString(row, ['release_date_5g']),
            commune: pickString(row, ['com_name']),
            epci: pickString(row, ['epci_name']),
          })),
        });
      } catch (error) {
        return errorResult(error instanceof Error ? error.message : 'Failed to fetch 5G sites');
      }
    }
  • Zod schema defining the four input parameters: operator (optional string), commune (optional string), frequency (optional string), and limit (integer 1-500, default 100).
      operator: z.string().optional().describe('Operator name prefix match. Examples: "Orange", "SFR", "Bouygues Telecom", "Free Mobile"'),
      commune: z.string().optional().describe('Commune name prefix match'),
      frequency: z.string().optional().describe('Frequency band substring match. Examples: "700" (700 MHz), "2100" (2.1 GHz), "3500" (3.5 GHz, the main 5G band)'),
      limit: z.number().int().min(1).max(500).default(100).describe('Max sites to return (1-500, default 100)'),
    },
Behavior3/5

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

No annotations are provided, so the description carries full burden. It indicates a read-only list operation and mentions the data source, but lacks details on pagination, rate limits, update frequency, or error handling.

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 two sentences: purpose then output/use cases. No filler, front-loaded with key info. Every sentence earns its place.

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?

For a list tool with 4 parameters and no output schema, the description adequately explains what is returned and the data source. It could mention data freshness or pagination, but it is sufficient for an agent to understand the tool's output.

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

Parameters3/5

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

Schema description coverage is 100%, and the parameter descriptions in the schema are already clear. The tool description does not add additional meaning beyond listing output columns and use cases, so it meets the baseline.

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 lists 5G cell sites in La Réunion from ARCEP data, specifying the resource, location, and source. It distinguishes from all sibling tools, which cover different data domains.

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 lists use cases like coverage analysis and infrastructure mapping, providing clear context for when to use the tool. It doesn't explicitly exclude alternatives or mention when not to use it, but the specificity makes the usage straightforward.

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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