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Swetha-Josh

FCSC MCP Server

by Swetha-Josh
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JavaScript
Remote

FCSC MCP Server

An MCP server that exposes official UAE statistics from the Federal Competitiveness and Statistics Centre (FCSC) as tools an AI assistant can call.

It acts as the bridge between Claude and the FCSC SDMX API: Claude never calls FCSC directly. It invokes a tool here, this server calls the FCSC endpoint, and the response is returned as readable, labelled data. FCSC credentials stay in this server's environment and are never exposed to the model.

Status

The tool layer is complete and tested against local stubs. It has not yet returned live FCSC data — see Known blocker below.

Related MCP server: OECD MCP Server

Tools

Tool

Purpose

listDatasets

Discover available datasets, filtered by topic or free-text search.

getDatasetData

Fetch observations for one dataset as a labelled table.

getDatasetStructure

List a dataset's dimensions and codelists, for building filtered keys.

Rather than defining 50 near-identical tools, the server is driven by datasets.json — a manifest of 50 FCSC dataflows spanning Economy, Social and Environment topics. Claude discovers what exists with listDatasets, then reads it with getDatasetData.

Setup

Requires Node 20 or newer. The SDK's HTTP transport depends on @hono/node-server, which does not support Node 18.

npm install

Local use — stdio transport

The client launches the server as a subprocess and speaks JSON-RPC over stdin/stdout. Nothing listens on a port.

npm start
{
  "mcpServers": {
    "fcsc-statistics": {
      "command": "node",
      "args": ["/absolute/path/to/my-mcp-server/server.js"]
    }
  }
}

Hosted use — Streamable HTTP transport

One process serving many clients over the network. This is what claude.ai custom connectors and shared team deployments require.

npm run start:http          # listens on :3000, endpoint POST /mcp
curl localhost:3000/health  # {"status":"ok","datasets":50,...}

PORT, HOST and CORS_ORIGIN are configurable via the environment.

Note that nohup npm start & will not work for backgrounding — the stdio server exits immediately when its stdin is closed. Background start:http instead, or run either under a process manager such as systemd or pm2.

To register as a claude.ai custom connector the endpoint must be reachable at a public HTTPS URL — a private EC2 address won't do. Terminate TLS with nginx, Caddy or an ALB in front of the service, and give Claude the https://your-domain/mcp URL.

Environment variables

Variable

Purpose

FCSC_BASE_URL

Override the API base URL. Useful for pointing at a test stub.

FCSC_API_KEY

Sent as Ocp-Apim-Subscription-Key if FCSC issue an API key.

Regenerating the dataset manifest

datasets.json is generated from the FCSC deep-links spreadsheet:

npm run build:manifest -- "/path/to/FCSC- MOBILE APP- Deep links.xlsx"

The generator scans the three URL columns for the first /rest/data/ URL and normalises it to the flat (dimensionAtObservation=AllDimensions) flavour itself, rather than trusting the spreadsheet's "SDMX flavour (Flat)" column — that column has known defects, including one row shifted a column right and 19 rows whose time-series URL duplicates the flat one.

Two rows (Vital Statistics → Birth, Death) carry no URL at all and are reported as skipped.

Known blocker

The FCSC hosts sit behind Cloudflare bot protection, which returns HTTP 403 to every non-browser client — the whole domain, not just the API paths. A browser User-Agent does not help.

The API itself is live and serving: an external request reached DF_LFUNEMP_ED and received HTTP 200 with SDMX-ML. So the barrier is Cloudflare sitting between a program and the API, not the API itself.

Needed from FCSC: a machine-usable path to the API — an API key, a service account, or allowlisting for the server — plus confirmation of which response format the service will serve.

The server negotiates for SDMX-JSON, falls back to SDMX-CSV, and detects SDMX-ML and reports it rather than failing silently. If FCSC only ever serve SDMX-ML, an XML parsing path needs adding.

Layout

server.js                  stdio entry point
http-server.js             Streamable HTTP entry point (hosted / claude.ai)
create-server.js           MCP server factory and tool definitions
fcsc.js                    URL building, HTTP, SDMX parsing, table rendering
datasets.json              Generated manifest of 50 FCSC dataflows
scripts/build-manifest.py  Regenerates datasets.json from the spreadsheet

Available Tools

3 tools
getDatasetDataGet FCSC dataset observationsA

Fetches official observations for one FCSC dataset from the UAE statistics SDMX API (flat flavour) and returns them as a labelled table. Always cite these figures as official FCSC data.

ParametersJSON Schema
NameRequiredDescriptionDefault
keyNoAdvanced: SDMX dimension key overriding the default, dot-separated with empty segments meaning "all", e.g. ".A.............".
limitNoMaximum observations to return. Defaults to 100.
datasetIdYesDataflow id from listDatasets, e.g. "DF_NA_ISIC_CON" or "DF_CPI_ANN".
endPeriodNoLatest period, same format as startPeriod.
startPeriodNoEarliest period, e.g. "2020", "2020-Q1" or "2020-01". Defaults to the dataset default.

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that this is a read-only 'fetch' operation, returns a labelled table, and mentions the 'flat flavour' of the API. However, it does not mention pagination behavior, error handling, rate limits, or other side effects, which would be valuable for an agent.

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 exactly two sentences, front-loaded with the core purpose ('Fetches official observations') and includes a key usage instruction (always cite). It is concise with no redundant or filler content.

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

Completeness3/5

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

The tool has no output schema, so the description must convey what the return looks like. It says 'labelled table' but does not specify columns or structure beyond that. For a data-fetching tool, this is a moderate gap; more detail about the table's content (e.g., periods, values, dimensions) would improve completeness.

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?

The input schema has 100% description coverage for all five parameters, so the schema already documents each parameter's meaning. The description adds no extra parameter-level detail, which is acceptable given the high schema coverage.

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 fetches official observations for one FCSC dataset from the UAE statistics SDMX API (flat flavour) and returns a labelled table. This distinguishes it from sibling tools listDatasets and getDatasetStructure, which list datasets and retrieve structure respectively.

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 provides clear context: use this tool to fetch observations for a specific dataset, and there is an implicit instruction to use listDatasets to find the datasetId. It also includes a citation requirement. However, it does not explicitly name alternatives or exclusion criteria, so it stops short of a 5.

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

getDatasetStructureGet FCSC dataset structureA

Returns the dimensions and codelists for one FCSC dataset. Use this when you need to build a filtered key for getDatasetData, or to explain what breakdowns a dataset offers.

ParametersJSON Schema
NameRequiredDescriptionDefault
datasetIdYesDataflow id from listDatasets, e.g. "DF_CPI_ANN".

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It communicates that the tool 'Returns' data, implying a read-only operation, and mentions practical uses (building keys, explaining breakdowns). However, it does not explicitly state side-effect-free behavior or provide details on error handling or required permissions, leaving some ambiguity.

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, both informative and free of fluff. It front-loads the primary action ('Returns the dimensions and codelists') and then provides usage guidance, achieving maximum efficiency with zero wasteful content.

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 the tool's simplicity (one parameter, no output schema, no annotations), the description adequately conveys its purpose and usage. It explains what the tool returns (dimensions and codelists) and why you would use it. A brief mention of the response structure could enhance completeness, but it's not essential for such a straightforward tool.

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?

The input schema already provides a full description of the single parameter `datasetId`, including an example, achieving 100% schema description coverage. The tool description adds marginal context by linking the parameter to building a `key` for `getDatasetData` and referencing `listDatasets`, but it does not significantly expand on the parameter's meaning 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?

The description clearly states the tool 'Returns the dimensions and codelists for one FCSC dataset', identifying both the action and the resource. It distinguishes itself from siblings by specifying it works on a single dataset's structure rather than listing datasets or retrieving data.

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 gives explicit usage guidance: 'Use this when you need to build a filtered `key` for getDatasetData, or to explain what breakdowns a dataset offers.' It clearly indicates the intended use case, though it does not explicitly state when not to use it or name alternative tools directly.

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

listDatasetsList FCSC datasetsA

Lists the official UAE statistics datasets available from FCSC, optionally filtered by topic or a free-text search over dataset names and KPIs. Use this first to find the datasetId to pass to getDatasetData.

ParametersJSON Schema
NameRequiredDescriptionDefault
topicNoFilter by main or sub topic, e.g. "Economy", "Labor Force", "Tourism".
searchNoFree-text filter over dataset name, KPI and dataflow id, e.g. "unemployment".

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description must convey behavioral context. It states the tool lists datasets and supports filtering, and implies read-only operation. Yet it does not disclose pagination, response format, or other potential quirks. The mention of datasetId provides some return context.

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?

Two sentences, front-loaded with the core purpose, and zero wasted words. The guidance about finding datasetId is included efficiently.

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 simple list tool, the description covers the purpose, filtering capability, and its role in the workflow (finding datasetId). While no output schema exists, the description gives enough context for an agent to use it correctly. It could mention pagination or result limits, but overall it is adequate.

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% with descriptive parameter definitions. The description adds no extra meaning beyond that, so the baseline of 3 is appropriate.

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 official UAE statistics datasets from FCSC with optional filters. It also distinguishes from sibling tools by positioning it as the first step to obtain a datasetId for getDatasetData.

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?

Explicitly instructs to use this tool first to find the datasetId for getDatasetData. However, it does not mention when not to use it or contrast with getDatasetStructure, so it falls short of full 5.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv1.0.0
    • First observedgetDatasetData
    • First observedgetDatasetStructure
    • First observedlistDatasets
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TDQS

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: listing datasets, fetching data, and retrieving structure. No overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with camelCase: listDatasets, getDatasetData, getDatasetStructure. The naming is predictable and coherent.

Tool Count5/5

With 3 tools, the server is well-scoped for its purpose. Each tool earns its place and covers the essential operations without excess.

Completeness5/5

The set covers the full dataset workflow: discover (listDatasets), understand (getDatasetStructure), and retrieve (getDatasetData). No significant gaps for the stated purpose.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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