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uruguay-mcp

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Python
Remote

🇺🇾 uruguay-mcp

Structured AI-agent access to Uruguay's open government data Acceso estructurado de agentes de IA a los datos abiertos del Estado uruguayo

PyPI Python MCP CI Tests Coverage License

🌎 Español · English

An MCP server that gives AI agents structured access to Uruguay's open government data — the national data catalog, the Central Bank, the statistics institute, Montevideo's city data & realtime transport, spatial data (IDE), education, health, social programs, government social-security statistics (BPS), tax reference values (DGI), news, and the gub.uy service catalog — behind a single meta-discovery layer.

✨ Why a meta-discovery layer?

Instead of flooding the model with hundreds of tool definitions, the server exposes five meta-tools. The model searches for what it needs, then invokes the matching data tool by name. The prompt-visible surface stays constant no matter how many data sources are added.

Meta-tool

Purpose

discover_tools(query, module?, limit?)

Rank data tools relevant to a natural-language need (returns their argument schemas)

call_tool(name, arguments)

Invoke a data tool by name (validates arguments)

list_modules()

List data-source modules and their tool counts

plan_query(goal)

Surface candidate tools for a multi-step goal

execute_batch(calls)

Run several calls concurrently with per-call error isolation

Every tool returns a unified envelope: { "_meta": { source, cached, lang, timestamp }, "data": ... }.

At a glance: 5 meta-tools + 84 data tools across 17 modules, plus 54 prompts and 36 resources.

Related MCP server: CKAN MCP Server

📚 Data sources (modules)

Module

Source

Protocol

Tools

🏛️

catalogodatos

catalogodatos.gub.uy — national CKAN catalog (~2680 datasets, 72 orgs) + DataStore SQL

CKAN REST

9

💵

bcu

Banco Central del Uruguay — exchange rates

SOAP (zeep)

4

📊

ine

Instituto Nacional de Estadística — ANDA studies + national CKAN DataStore queries

REST

7

🌐

gubuy

gub.uy public API / service catalog

CKAN REST

4

🚌

montevideo

Intendencia de Montevideo — city CKAN + realtime transport

CKAN + REST

11

🗄️

datastore

Cross-source SQLite workspace — load CSV/CKAN data, run read-only SQL JOINs

local SQLite

4

🛒

acce

Agencia de Compras y Contrataciones del Estado — public procurement (OCDS)

OCDS REST/RSS + CKAN

4

⚖️

impo

IMPO — legislation, normativa & Diario Oficial

REST (JSON)

6

🧾

dgi

DGI (tax authority) — reference values (UI, IPC, ITP & late-payment rates) as .ods + statistical bulletins

scrape + ODS/PDF

4

🌦️

inumet

Instituto Uruguayo de Meteorología — stations, forecast & alerts

REST + HTML

3

🏛️

parlamento

Parlamento del Uruguay — datasets, attendance & activity (CKAN-backed)

CKAN REST

4

🗺️

ide

IDE Uruguay (AGESIC) — spatial data: WFS layers, cadastral parcels & geocoding

WFS 2.0 + REST

5

🎓

educacion

ANEP / education — datasets & school directories (national CKAN, org=anep)

CKAN REST

3

🏥

salud

Salud (MSP / FNR) — health datasets, clinics & medication spending

CKAN REST

5

🤝

mides

MIDES — social programs & the Guía de Recursos service directory

CKAN + HTML

4

🧓

bps

Banco de Previsión Social — "BPS en Cifras" observatory: pensions, benefits & contributors (live indicators)

REST (JSON)

5

📰

noticias

gub.uy government news — latest releases & full-text search

HTML scrape

2

The transport surface of montevideo needs OAuth2 credentials (URUGUAY_MCP_MVD_CLIENT_ID / URUGUAY_MCP_MVD_CLIENT_SECRET); without them the transport tools return a typed validation_error while the CKAN tools work unauthenticated.

🧩 Prompts & Resources

Each module also registers reusable prompts (parameterized Spanish instruction templates) and resources (static reference docs under the uru://<module>/<path> URI scheme), exposed natively through FastMCP.

  • 54 prompts — e.g. bcu_cotizacion_dolar_hoy, catalogo_buscar_por_tema, bps_pasividades_actuales, dgi_valor_referencia, ine_buscar_estudios, montevideo_proximo_bus, datastore_unir_dos_fuentes, acce_analizar_compra, impo_consultar_norma, inumet_clima_actual, ide_consultar_catastro, salud_consultar_medicamentos, noticias_ultimas.

  • 36 resources — e.g. uru://bcu/codigos-moneda, uru://bps/catalogo-indicadores, uru://dgi/catalogo-valores, uru://catalogodatos/guia-de-uso, uru://montevideo/credenciales-transporte, uru://acce/glosario-ocds, uru://impo/esquema, uru://inumet/variables, uru://ide/capas-destacadas, uru://salud/fuentes, uru://mides/guia-recursos.

See EXAMPLES.md for end-to-end usage scenarios, including cross-source ones via plan_query / execute_batch and SQL JOINs through the datastore module.

🚀 Quick start

# Run directly from PyPI (once published)
uvx uruguay-mcp

# …or install it
pip install uruguay-mcp        # or: uv pip install uruguay-mcp
uruguay-mcp

One-command install into Claude

uruguay-mcp install

Merges the server into Claude Desktop's config (preserving existing mcpServers and unrelated keys) and prints a ready-to-paste snippet for Claude Code / Cursor. Restart the client afterwards.

Claude Desktop config (manual)

{
  "mcpServers": {
    "uruguay-mcp": { "command": "uruguay-mcp" }
  }
}

Run options

uruguay-mcp                          # stdio (default)
uruguay-mcp --transport sse --port 8000
uruguay-mcp --modules catalogodatos,bcu   # load only some modules
uruguay-mcp --verbose                # INFO logs   (--debug for DEBUG)

⚙️ Configuration

All via URUGUAY_MCP_* environment variables:

Variable

Default

Meaning

URUGUAY_MCP_LANG

es

Language for human-facing strings (es/en)

URUGUAY_MCP_HTTP_TIMEOUT

30

HTTP timeout (seconds)

URUGUAY_MCP_CACHE_TTL

900

Response cache TTL (seconds)

URUGUAY_MCP_RATE_LIMIT_RPS

5

Max requests/sec per host

URUGUAY_MCP_MODULES

(all)

Comma-separated module allowlist

URUGUAY_MCP_MVD_CLIENT_ID

(unset)

OAuth2 client id for the Montevideo transport API

URUGUAY_MCP_MVD_CLIENT_SECRET

(unset)

OAuth2 client secret for the Montevideo transport API

🏗️ Architecture

src/uruguay_mcp/
├── server.py            # FastMCP wiring; meta-tools + registered prompts + resources
├── cli.py               # `uruguay-mcp` / `uruguay-mcp install`; -v/--debug logging
├── meta/                # discovery layer
│   ├── tools.py         # the 5 meta-tools
│   └── search.py        # BM25-lite ranking over the registry
├── shared/              # reused by every module
│   ├── config.py        # env-driven settings (URUGUAY_MCP_*)
│   ├── http.py          # async client: retries (tenacity) + per-host rate limit
│   ├── cache.py         # async TTL cache
│   ├── envelope.py      # unified {_meta, data} response (+ UTC timestamp)
│   ├── i18n.py          # es/en messages
│   ├── errors.py        # typed, localized errors
│   └── registry.py      # tool/prompt/resource registry; @tool/@prompt/@resource
└── modules/             # one self-contained package per data source
    ├── catalogodatos/   ├── bcu/          ├── ine/
    ├── gubuy/           ├── montevideo/   ├── datastore/
    ├── acce/            ├── impo/         ├── inumet/
    ├── parlamento/      ├── ide/          ├── educacion/
    ├── salud/           ├── mides/        ├── noticias/
    ├── bps/             └── dgi/

Each module package is independent (constants · schemas · client · tools · optional prompts/resources). Importing the package self-registers everything it offers.

🛠️ Development

uv venv && uv pip install -e ".[dev]"

uv run pytest                  # 252 unit tests (HTTP mocked, offline) · 86% coverage
uv run pytest -m integration   # hits live government APIs
uv run ruff check src tests
uv run pyright

🙌 Acknowledgements

Built on data published by AGESIC, BCU, INE and the Intendencia de Montevideo under Uruguay's open-data law (Nº 18.381). This project is an independent client and is not affiliated with those institutions.

📄 License

MIT

Available Tools

5 tools
call_toolC

Invoke a data tool by name with the given arguments.

ParametersJSON Schema
NameRequiredDescriptionDefault
nameYes
argumentsNo

TDQS

C2.1/5.0
Behavior1/5

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

No annotations are provided, and the description fails to disclose any behavioral traits such as side effects, error handling, or idempotency. The description says nothing beyond the basic action, leaving the agent with no insight into behavior.

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

Conciseness2/5

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

The description is extremely short (one sentence), but this is due to under-specification rather than conciseness. Essential details are missing, making it insufficient for effective tool selection and use.

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

Completeness1/5

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

With no output schema, no annotations, and a generic purpose, the description is completely inadequate. It does not explain what returns, error behavior, or the concept of 'data tool'. The complexity of the tool (which may call other tools) demands more context.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds no meaning beyond the parameter names. It does not explain what 'arguments' should contain, what types are expected, or any constraints. The description provides no added value for parameter understanding.

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

Purpose4/5

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

The description uses the verb 'invoke' and specifies it operates on a 'data tool' by name with arguments. This is clear in stating the action and resource, but lacks differentiation from sibling tools like execute_batch which might also invoke tools. Without context, the distinction is unclear.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like execute_batch or discover_tools. There is no mention of prerequisites, limitations, or typical use cases.

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

discover_toolsB

Find data tools relevant to a natural-language need.

Returns ranked tools with their argument schemas. Use this first, then invoke the chosen tool via call_tool.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes
moduleNo
limitNo

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states that the tool returns ranked tools with their schemas, which is helpful. However, it does not disclose any other behavioral traits, such as whether the operation is read-only (likely discoverable), has side effects, or requires specific permissions. For a discovery tool, these details are important for an agent to safely invoke it.

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 concise: two sentences that convey the core purpose and a usage hint. It is front-loaded with the primary action ('Find data tools...') and then adds supporting detail. There is no unnecessary text. However, it could be more structured (e.g., bullet points for key aspects), but given its brevity, it is effective.

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

Completeness2/5

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

Given the tool has three parameters (with no schema descriptions), no annotations, and an output schema that likely explains the return format, the description should cover parameter semantics to be complete. It does not. The intended use as a discovery tool is stated, but agents need to understand the 'module' and 'limit' parameters to use it correctly. Thus, the description is incomplete for its complexity.

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

Parameters1/5

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

The input schema has three parameters ('query', 'module', 'limit') with no descriptions (schema coverage 0%), and the description does not explain any of them. The description only says 'Find data tools relevant to a natural-language need,' which vaguely relates to 'query' but does not clarify 'module' or 'limit.' Without parameter explanations, the description adds no semantic value beyond what the schema provides.

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's purpose: 'Find data tools relevant to a natural-language need.' It also specifies the output: 'Returns ranked tools with their argument schemas.' Additionally, it distinguishes itself from the sibling tool 'call_tool' by suggesting a usage order: 'Use this first, then invoke the chosen tool via call_tool.' This provides strong differentiation from its primary sibling.

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 a clear usage guideline: 'Use this first, then invoke the chosen tool via call_tool.' This indicates a typical workflow. However, it does not explicitly mention when not to use this tool or compare it to other siblings like 'list_modules' or 'plan_query,' which could help agents decide between them. The guidance is good but not exhaustive.

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

execute_batchA

Run several tool calls concurrently with per-call error isolation.

calls is a list of {"name": ..., "arguments": {...}}. A failure in one call does not abort the others.

ParametersJSON Schema
NameRequiredDescriptionDefault
callsYes

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.2/5.0
Behavior3/5

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

No annotations provided; the description covers concurrency and error isolation but lacks details on ordering, batch size limits, or argument validation.

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 concise (two sentences plus a code snippet), front-loaded with the main action, and each sentence serves a purpose.

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 presence of an output schema and only one parameter, the description is fairly complete, though it could mention batch size limits or execution order.

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?

With 0% schema description coverage, the description adds meaning by specifying the structure of the 'calls' parameter as a list of objects with 'name' and 'arguments', beyond the schema's generic object type.

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 'Run several tool calls concurrently', which specifies the verb 'run' and resource 'tool calls', distinguishing it from siblings like call_tool (single call) and discover_tools.

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 implies when to use it (batch multiple calls with error isolation) but does not explicitly mention when not to use or alternatives like call_tool for single calls.

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

list_modulesA

List the available data-source modules and how many tools each offers.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.3/5.0
Behavior4/5

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

The behavior is straightforward: list modules with counts. No annotations are provided, but the description fully covers what the tool does without hidden side effects or mutations.

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 a single sentence with the verb front-loaded, no redundant information, and perfectly sized for the tool's simplicity.

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

Completeness5/5

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

Given zero parameters and an output schema that likely details the module list, the description sufficiently explains what the tool returns. No additional context is needed.

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?

There are no parameters, and the schema covers 100% of input specification. The description adds no parameter information, which is acceptable as there are none to document.

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 data-source modules and their tool counts, using a specific verb+resource. It distinguishes from siblings which perform different actions like calling tools or planning queries.

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?

The description implies usage for getting an overview of available modules, but provides no explicit guidance on when to use this tool versus alternatives. No exclusion criteria or usage context is given.

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

plan_queryA

Sketch a plan: surface candidate tools across modules for a broad goal.

For multi-step needs, this returns the most relevant tools so the model can chain them (e.g. search a dataset, then query its datastore).

ParametersJSON Schema
NameRequiredDescriptionDefault
goalYes

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description must bear full responsibility for behavioral disclosure. It only states it 'surface[s] candidate tools' but omits details on side effects, auth requirements, or output format (though output schema may exist).

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, immediate verb-first statement, and no filler. Every sentence adds context.

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?

Given the tool has one parameter and an output schema, the description provides purpose and usage context but lacks detail on the plan structure and potential constraints. It is adequate but not thorough.

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

Parameters2/5

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

With 0% schema description coverage, the description adds little value beyond the parameter name 'goal'. It merely says 'for a broad goal', which is a restatement.

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 'surface[s] candidate tools across modules for a broad goal', using a specific verb and resource, and distinguishes from siblings by focusing on multi-step planning.

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 targets 'multi-step needs' and provides an example (search dataset then query datastore), but does not mention when not to use it or list direct alternatives.

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. 5 tool updatesv0.1.1
    • First observedcall_tool
    • First observeddiscover_tools
    • First observedexecute_batch
    • First observedlist_modules
    • First observedplan_query
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TDQS

A3.5/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: call_tool for invocation, discover_tools for natural language search, execute_batch for concurrent calls, list_modules for module overview, and plan_query for multi-step planning. No overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (call_tool, discover_tools, execute_batch, list_modules, plan_query). The naming is predictable and uniform.

Tool Count5/5

With 5 tools, the server is well-scoped for a meta-tool that discovers and invokes other data tools. The number feels complete without being excessive.

Completeness5/5

The tool surface covers the full workflow: discovering tools (discover_tools), planning multi-step queries (plan_query), invoking individual tools (call_tool), batch execution (execute_batch), and listing available modules (list_modules). No obvious gaps for its stated purpose.

Maintenance

ActivityInactive
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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