Skip to main content
Glama
Hug0x0

mcp-reunion

reunion_get_housing_overview

Retrieve a comprehensive housing and demographic snapshot of La Réunion, including population, density, age structure, unemployment, poverty, housing units, social housing indicators, and vacancy rates, sorted by publication year.

Instructions

Comprehensive housing and demographic snapshot of La Réunion department from the Banque des Territoires atlas (1 row per publication year). Returns: population, density per km², 10-year population change, age structure (% <20 and ≥60), unemployment rate (T4), poverty rate, total housing units, principal residences, social-housing rate, vacancy rate, individual-housing rate, and social-park-specific indicators (count, average rent EUR/m²/month, average age, energy-poor rate for E/F/G DPE labels). Sorted by publication year descending.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoPublication year filter, 4 digits (e.g. "2024")
limitNoMax yearly snapshots to return (1-50, default 20)

Implementation Reference

  • Handler function for 'reunion_get_housing_overview' tool. Fetches comprehensive housing/demographic data from the 'logements-et-logements-sociaux-dans-les-departements-a-la-reunion' dataset on data.regionreunion.com, returning population, density, age structure, unemployment, poverty, housing units, social housing stats, etc. sorted by publication year descending.
    async ({ year, limit }) => {
      try {
        const data = await client.getRecords<RecordObject>(DATASET_HOUSING, {
          where: buildWhere([year ? `annee_publication = date${quote(`${year}-01-01`)}` : undefined]),
          order_by: 'annee_publication DESC',
          limit,
        });
        return jsonResult({
          total_snapshots: data.total_count,
          snapshots: data.results.map((row) => ({
            publication_year: pickString(row, ['annee_publication']),
            department: pickString(row, ['nom_departement']),
            population: pickNumber(row, ['nombre_d_habitants']),
            density_per_km2: pickNumber(row, ['densite_de_population_au_km2']),
            population_change_10y_pct: pickNumber(row, ['variation_de_la_population_sur_10_ans_en']),
            pct_under_20: pickNumber(row, ['population_de_moins_de_20_ans']),
            pct_60_plus: pickNumber(row, ['population_de_60_ans_et_plus']),
            unemployment_rate_pct: pickNumber(row, ['taux_de_chomage_au_t4_en']),
            poverty_rate_pct: pickNumber(row, ['taux_de_pauvrete_en']),
            total_housing: pickNumber(row, ['nombre_de_logements']),
            principal_residences: pickNumber(row, ['nombre_de_residences_principales']),
            social_housing_rate_pct: pickNumber(row, ['taux_de_logements_sociaux_en']),
            vacancy_rate_pct: pickNumber(row, ['taux_de_logements_vacants_en']),
            individual_housing_rate_pct: pickNumber(row, ['taux_de_logements_individuels_en']),
            social_stock_count: pickNumber(row, ['parc_social_nombre_de_logements']),
            social_avg_rent_eur_m2: pickNumber(row, ['parc_social_loyer_moyen_en_eur_m2_mois']),
            social_avg_age_years: pickNumber(row, ['parc_social_age_moyen_du_parc_en_annees']),
            social_energy_poor_rate_pct: pickNumber(row, ['parc_social_taux_de_logements_energivores_e_f_g_en']),
          })),
        });
      } catch (error) {
        return errorResult(error instanceof Error ? error.message : 'Failed to fetch housing overview');
      }
    }
  • Zod schema defining tool inputs: optional year string filter and optional limit (1-50, default 20).
      year: z.string().optional().describe('Publication year filter, 4 digits (e.g. "2024")'),
      limit: z.number().int().min(1).max(50).default(20).describe('Max yearly snapshots to return (1-50, default 20)'),
    },
  • Registration of the 'reunion_get_housing_overview' tool on the MCP server via the McpServer.tool() call, with name, description, input schema, and handler.
    server.tool(
      'reunion_get_housing_overview',
      'Comprehensive housing and demographic snapshot of La Réunion department from the Banque des Territoires atlas (1 row per publication year). Returns: population, density per km², 10-year population change, age structure (% <20 and ≥60), unemployment rate (T4), poverty rate, total housing units, principal residences, social-housing rate, vacancy rate, individual-housing rate, and social-park-specific indicators (count, average rent EUR/m²/month, average age, energy-poor rate for E/F/G DPE labels). Sorted by publication year descending.',
      {
        year: z.string().optional().describe('Publication year filter, 4 digits (e.g. "2024")'),
        limit: z.number().int().min(1).max(50).default(20).describe('Max yearly snapshots to return (1-50, default 20)'),
      },
      async ({ year, limit }) => {
        try {
          const data = await client.getRecords<RecordObject>(DATASET_HOUSING, {
            where: buildWhere([year ? `annee_publication = date${quote(`${year}-01-01`)}` : undefined]),
            order_by: 'annee_publication DESC',
            limit,
          });
          return jsonResult({
            total_snapshots: data.total_count,
            snapshots: data.results.map((row) => ({
              publication_year: pickString(row, ['annee_publication']),
              department: pickString(row, ['nom_departement']),
              population: pickNumber(row, ['nombre_d_habitants']),
              density_per_km2: pickNumber(row, ['densite_de_population_au_km2']),
              population_change_10y_pct: pickNumber(row, ['variation_de_la_population_sur_10_ans_en']),
              pct_under_20: pickNumber(row, ['population_de_moins_de_20_ans']),
              pct_60_plus: pickNumber(row, ['population_de_60_ans_et_plus']),
              unemployment_rate_pct: pickNumber(row, ['taux_de_chomage_au_t4_en']),
              poverty_rate_pct: pickNumber(row, ['taux_de_pauvrete_en']),
              total_housing: pickNumber(row, ['nombre_de_logements']),
              principal_residences: pickNumber(row, ['nombre_de_residences_principales']),
              social_housing_rate_pct: pickNumber(row, ['taux_de_logements_sociaux_en']),
              vacancy_rate_pct: pickNumber(row, ['taux_de_logements_vacants_en']),
              individual_housing_rate_pct: pickNumber(row, ['taux_de_logements_individuels_en']),
              social_stock_count: pickNumber(row, ['parc_social_nombre_de_logements']),
              social_avg_rent_eur_m2: pickNumber(row, ['parc_social_loyer_moyen_en_eur_m2_mois']),
              social_avg_age_years: pickNumber(row, ['parc_social_age_moyen_du_parc_en_annees']),
              social_energy_poor_rate_pct: pickNumber(row, ['parc_social_taux_de_logements_energivores_e_f_g_en']),
            })),
          });
        } catch (error) {
          return errorResult(error instanceof Error ? error.message : 'Failed to fetch housing overview');
        }
      }
    );
  • Call to registerHousingTools from index.ts, which is invoked by registerAllTools during server startup.
      registerHousingTools(server);
      registerNationalElectionsTools(server);
      registerPossessionTools(server);
      registerSocialTools(server);
      registerTelecomTools(server);
      registerTerritoryTools(server);
      registerTourismTools(server);
      registerTransportTools(server);
      registerUrbanismTools(server);
      registerWeatherTools(server);
    }
  • ReunionClient class (singleton instance 'client') used by the handler to call the OpenDataSoft API's getRecords method with the housing dataset ID.
    export class ReunionClient {
      private readonly baseUrl = 'https://data.regionreunion.com/api/explore/v2.1/';
      private readonly timeout = 30000;
      private readonly maxRetries = 2;
      private readonly metadataCache = new Map<string, Promise<DatasetMetadata | undefined>>();
      private readonly recordsCache = new Map<string, { value: unknown; expiresAt: number }>();
    
      /**
       * Fetch records from a dataset
       */
      async getRecords<T extends RecordObject = RecordObject>(
        datasetId: string,
        params: ODSQueryParams = {}
      ): Promise<ODSResponse<T>> {
        const url = this.buildUrl(`/catalog/datasets/${datasetId}/records`, params);
    
        if (REFERENTIAL_DATASETS.has(datasetId)) {
          const now = Date.now();
          const cached = this.recordsCache.get(url);
          if (cached && cached.expiresAt > now) {
            return cached.value as ODSResponse<T>;
          }
          const value = await this.fetchJson<ODSResponse<T>>(url);
          this.recordsCache.set(url, { value, expiresAt: now + REFERENTIAL_TTL_MS });
          return value;
        }
    
        return this.fetchJson<ODSResponse<T>>(url);
      }
    
      /**
       * Clear the in-memory caches. Intended for tests.
       */
      clearCaches(): void {
        this.metadataCache.clear();
        this.recordsCache.clear();
      }
    
      /**
       * Fetch aggregated data from a dataset
       */
      async getAggregates<T extends RecordObject = RecordObject>(
        datasetId: string,
        select: string,
        options: {
          where?: string;
          groupBy?: string;
          orderBy?: string;
          limit?: number;
        } = {}
      ): Promise<ODSResponse<T>> {
        const params: Record<string, string | number | undefined> = { select };
        if (options.where) params.where = options.where;
        if (options.groupBy) params.group_by = options.groupBy;
        if (options.orderBy) params.order_by = options.orderBy;
        if (options.limit !== undefined) params.limit = options.limit;
    
        const url = this.buildUrl(`/catalog/datasets/${datasetId}/aggregates`, params);
        return this.fetchJson<ODSResponse<T>>(url);
      }
    
      /**
       * Search across all datasets
       */
      async searchDatasets(query: string): Promise<CatalogResponse> {
        const url = this.buildUrl('/catalog/datasets', {
          where: `search(${quote(query)})`,
          limit: 20,
        });
        return this.fetchJson<CatalogResponse>(url);
      }
    
      /**
       * List datasets with an optional raw ODSQL where clause.
       */
      async listDatasets(
        options: { where?: string; limit?: number; offset?: number } = {}
      ): Promise<CatalogResponse> {
        const url = this.buildUrl('/catalog/datasets', {
          where: options.where,
          limit: options.limit ?? 20,
          offset: options.offset,
        });
        return this.fetchJson<CatalogResponse>(url);
      }
    
      /**
       * Fetch dataset metadata from the catalog
       */
      async getDatasetMetadata(datasetId: string): Promise<DatasetMetadata | undefined> {
        if (!this.metadataCache.has(datasetId)) {
          const promise = this.fetchJson<CatalogResponse>(
            this.buildUrl('/catalog/datasets', {
              where: `dataset_id = ${quote(datasetId)}`,
              limit: 1,
            })
          ).then((data) => data.results[0]);
    
          this.metadataCache.set(datasetId, promise);
        }
    
        return this.metadataCache.get(datasetId);
      }
    
      /**
       * Check whether a dataset currently exists in the public catalog
       */
      async datasetExists(datasetId: string): Promise<boolean> {
        return Boolean(await this.getDatasetMetadata(datasetId));
      }
    
      /**
       * Resolve the first matching field name for a dataset
       */
      async resolveField(
        datasetId: string,
        candidates: string[]
      ): Promise<string | undefined> {
        const metadata = await this.getDatasetMetadata(datasetId);
        const fields = metadata?.fields ?? [];
    
        if (fields.length === 0) {
          return candidates[0];
        }
    
        const byNormalizedName = new Map(
          fields.map((field) => [normalizeText(field.name), field.name] as const)
        );
    
        for (const candidate of candidates) {
          const direct = byNormalizedName.get(normalizeText(candidate));
          if (direct) {
            return direct;
          }
        }
    
        const fieldNames = fields.map((field) => field.name);
        for (const candidate of candidates) {
          const normalizedCandidate = normalizeText(candidate);
          const partial = fieldNames.find((fieldName) =>
            normalizeText(fieldName).includes(normalizedCandidate)
          );
          if (partial) {
            return partial;
          }
        }
    
        return candidates[0];
      }
    
      /**
       * Build URL with query parameters
       */
      private buildUrl(
        path: string,
        params: Record<string, string | number | undefined>
      ): string {
        const normalizedPath = path.startsWith('/') ? path.slice(1) : path;
        const url = new URL(normalizedPath, this.baseUrl);
    
        for (const [key, value] of Object.entries(params)) {
          if (value !== undefined && value !== null && value !== '') {
            url.searchParams.set(key, String(value));
          }
        }
    
        return url.toString();
      }
    
      /**
       * Execute HTTP request with retries and timeout handling
       */
      private async fetchJson<T>(url: string, remainingRetries = this.maxRetries): Promise<T> {
        const controller = new AbortController();
        const timeoutId = setTimeout(() => controller.abort(), this.timeout);
    
        try {
          const response = await fetch(url, {
            method: 'GET',
            headers: {
              Accept: 'application/json',
              'User-Agent': 'mcp-reunion/1.0',
            },
            signal: controller.signal,
          });
    
          if (!response.ok) {
            const errorText = await response.text();
            if (response.status >= 500 && remainingRetries > 0) {
              await this.delay(250);
              return this.fetchJson<T>(url, remainingRetries - 1);
            }
            throw new Error(
              `API error ${response.status}: ${response.statusText}. ${errorText}`
            );
          }
    
          return (await response.json()) as T;
        } catch (error) {
          if (error instanceof Error) {
            if (error.name === 'AbortError') {
              throw new Error(`Request timeout after ${this.timeout}ms`);
            }
    
            if (remainingRetries > 0 && this.isRetryableError(error)) {
              await this.delay(250);
              return this.fetchJson<T>(url, remainingRetries - 1);
            }
    
            throw error;
          }
          throw new Error('Unknown error occurred');
        } finally {
          clearTimeout(timeoutId);
        }
      }
    
      private isRetryableError(error: Error): boolean {
        return /fetch failed|ECONNRESET|ETIMEDOUT|ENOTFOUND|EAI_AGAIN/i.test(error.message);
      }
    
      private async delay(ms: number): Promise<void> {
        await new Promise((resolve) => setTimeout(resolve, ms));
      }
    }
    
    // Singleton instance
    export const client = new ReunionClient();
Behavior4/5

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

With no annotations, the description carries the full burden. It details the output structure (1 row per year, sorted descending) and lists all returned indicators, providing good behavioral context for a read-only snapshot.

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 dense with relevant information, front-loaded with purpose, and avoids unnecessary words. Could be slightly more structured but is efficient.

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 and no annotations, the description thoroughly explains the return fields and behavior. Missing only minor details like pagination or empty-result handling, but sufficient for most use cases.

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 coverage is 100% and descriptions already define 'year' and 'limit' well. The overall description adds context about output rows but does not significantly enhance parameter understanding beyond the schema.

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?

Clearly states it provides a comprehensive housing/demographic snapshot of La Réunion department at the department level, distinguishing it from other reunion tools that target communes, iris, or specific datasets.

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

Usage Guidelines3/5

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

Implies usage for retrieving department-level housing and demographic data, but no explicit guidance on when to prefer this tool over siblings or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Hug0x0/mcp-reunion'

If you have feedback or need assistance with the MCP directory API, please join our Discord server