295 herramientas, ~107 indicaciones y ~141 recursos en 9 API federales + 9 API provinciales + 2 API municipales + 1 almacén de datos SQLite local — tipos de cambio, datos parlamentarios, retiros de productos, información de medicamentos, más de 80 000 conjuntos de datos abiertos, datos de nutrición de alimentos, clima en tiempo real, estadísticas de inmigración, datos provinciales de Ontario, datos municipales de Toronto, datos de ArcGIS Hub de York Region, datos geoespaciales CKAN + WFS de Columbia Británica, datos CKAN + ArcGIS IQA de Quebec Données Québec, datos abiertos de Alberta + energía AER + incendios forestales WMBappServices + salud AHSGIS + transporte 511 Alberta, geoportal de Manitoba (ArcGIS Hub) + transporte 511 Manitoba, geoportal de Saskatchewan (ArcGIS Hub) + infraestructura hídrica WSA + prohibiciones de fuego SPSA, portal Socrata SODA de Nueva Escocia (data.novascotia.ca), descubrimiento federal-CKAN de Nuevo Brunswick + datos geoespaciales GeoNB de ArcGIS Server puro + portal Socrata gnb.socrata.com + transporte 511 NB con clave, y almacenamiento local persistente. Todo bilingüe (inglés/francés).
Primer módulo ArcGIS Hub — la infraestructura compartida en
shared/arcgis_hub.pyes reutilizable para futuros módulos municipales canadienses (BC, Calgary, Edmonton y otras ciudades que publican mediante ArcGIS Hub). Primer módulo OGC WFS — BC introduce soporte WFS 2.0 (OGC) medianteshared/ogc.py, lo que convierte a WFS en la tercera tecnología de portal junto con CKAN y ArcGIS Hub. Consultadocs://bc/wfs-query-guidepara el flujo de trabajo de dos pasos CKAN→WFS.
# Auto-configure your platform (interactive)
uvx mcp-canada install
# Or name platforms directly
uvx mcp-canada install claude-desktop cursor vscodeCompatible con 14 plataformas: Claude Desktop, Claude Code, Cursor, VS Code, Windsurf, Zed, Codex CLI, Gemini CLI, Amazon Q, OpenCode, Cline, Roo Code, Goose CLI, Junie CLI.
Añade a ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"mcp-canada": {
"command": "uvx",
"args": ["mcp-canada"]
}
}
}claude mcp add mcp-canada -- uvx mcp-canadagit clone https://github.com/reyemtech/mcp-canada.git
cd mcp-canada
uv run mcp-canadaIndicador | Descripción | Ejemplo |
| Protocolo de transporte |
|
| Puerto para SSE/HTTP |
|
| Cargar solo módulos específicos |
|
| Registro de nivel INFO |
|
| Registro de nivel DEBUG |
|
Variable de entorno: MCP_CANADA_MODULES=bank_of_canada,recalls
Related MCP server: canlii-mcp
Consulta el sitio de documentación para ver escenarios de inteligencia entre API — desde rastrear la sequía de las praderas hasta el dólar canadiense, hasta crear auditorías de seguridad farmacéutica, armar informes de rendición de cuentas de diputados, o unir datos de múltiples API en una sola consulta SQL. Cada ejemplo incluye la indicación exacta y la cadena de herramientas que puedes ejecutar hoy. El código fuente permanece en EXAMPLES.md.
Con 250 herramientas, enumerarlas todas consumiría la mitad de la ventana de contexto de un agente. En su lugar, la búsqueda BM25 permite a los agentes encontrar exactamente lo que necesitan:
Agent: "What tools do you have for exchange rates?"
→ discover_tools("exchange rate CAD")
→ Returns: boc_get_exchange_rates, boc_get_observations
→ call_tool("boc_get_exchange_rates", {"currency": "USD", "recent": 3})
→ Returns: {"_meta": {...}, "data": [{"date": "2026-04-02", "value": 1.3918, ...}]}Los agentes ven 5 herramientas siempre visibles:
Herramienta | Propósito |
| Búsqueda en lenguaje natural BM25 en todas las herramientas |
| Ejecutar cualquier herramienta descubierta por nombre |
| Listar los módulos de API disponibles con recuentos de herramientas |
| Planificar una consulta de varios pasos en las API de datos gubernamentales canadienses |
| Ejecutar múltiples llamadas a herramientas en paralelo con aislamiento de errores por paso |
Todas las herramientas aceptan lang: "en" | "fr" para soporte bilingüe. Las respuestas incluyen un sobre _meta con atribución de fuente y estado de caché. Consulta la referencia de herramientas completa y buscable para ver los parámetros actuales de las herramientas y las API de origen.
Módulo | Nivel | Herramientas | Indicaciones | Recursos | Descripción |
— | 5 | — | — | Herramientas de orquestación siempre visibles ( | |
Federal | 8 | 5 | 7 | Tipos de cambio, tasas de interés, precios de materias primas, inflación — API Valet | |
Federal | 7 | 5 | 7 | Más de 80 000 conjuntos de datos federales — open.canada.ca | |
Federal | 8 | 5 | 7 | Productos farmacéuticos, ingredientes, listas — DPD de Health Canada | |
Federal | 10 | 5 | 7 | RP, permisos de estudio/trabajo, Entrada Exprés, asilo — Datos abiertos de IRCC | |
Federal | 8 | 5 | 7 | Datos de nutrición de alimentos — Archivo canadiense de nutrientes | |
Federal | 10 | 5 | 7 | Proyectos de ley, diputados, votaciones, papeletas, debates de Hansard — API de Parlamento abierto | |
Federal | 6 | 4 | 6 | Retiros de alimentos, vehículos y productos de salud — Canadienses saludables | |
Federal | 15 | 6 | 8 | Series temporales, metadatos de cubos, filtrado SDMX — WDS de StatCan | |
Federal | 34 | 6 | 8 | Condiciones, clima, calidad del aire, hidrología, marina, radar — MSC GeoMet | |
Provincial | 24 | 6 | 7 | CKAN + energía AER + incendios forestales WMBappServices + salud AHSGIS + 511 Alberta — open.alberta.ca | |
Provincial | 20 | 6 | 7 | CKAN + geoespacial WFS — Catálogo de datos de BC | |
Provincial | 20 | 6 | 7 | ArcGIS Hub + 511 Manitoba — geoportal.gov.mb.ca | |
Provincial | 13 | 6 | 7 | ArcGIS Hub + agua WSA + prohibiciones de fuego SPSA — geohub.saskatchewan.ca | |
Nuevo Brunswick ( | Provincial | 22 | 6 | 7 | CKAN federal + GeoNB ArcGIS Server puro + Socrata gnb.socrata.com + transporte 511 NB con clave — geonb.snb.ca |
Provincial | 16 | 6 | 7 | Portal Socrata SODA (acuicultura, medio ambiente, salud) — data.novascotia.ca | |
Provincial | 6 | 4 | 6 | Más de 3000 conjuntos de datos provinciales — Datos abiertos de Ontario | |
Provincial | 18 | 6 | 7 | CKAN federado (139 organizaciones) — Données Québec | |
Municipal | 12 | 6 | 8 | TTC, barrios, 311, RentSafe — Datos abiertos de Toronto | |
Municipal | 27 | 5 | 8 | 4 portales ArcGIS Hub (York Region, Markham, Newmarket, Aurora) | |
Local | 6 | 4 | 6 | Persistencia SQLite para JOINs SQL entre API — | |
Total | 295 | ~107 | ~141 |
Todas las herramientas devuelven un sobre coherente:
{
"_meta": {
"source": {"api": "bank-of-canada-valet", "url": "https://..."},
"cached": true,
"lang": "en",
"timestamp": "2026-04-04T12:00:00Z"
},
"data": [ ... ]
}Los errores devuelven:
{
"error": {
"code": "INVALID_SERIES",
"message": "Series 'FXXYZCAD' not found.",
"suggestions": ["FXUSDCAD", "FXEURCAD"]
}
}src/mcp_canada/
├── server.py # FastMCP entry point, transport, module loading
├── shared/ # Cross-module utilities
│ ├── cache.py # TTL-based in-memory cache (aiocache)
│ ├── envelope.py # Response/error envelope (make_response/make_error)
│ ├── http.py # Shared HTTP client with retry (tenacity)
│ ├── rate_limiter.py # Per-source token bucket
│ └── i18n.py # Bilingual error messages
├── meta/
│ └── list_modules.py # list_modules meta-tool
└── modules/
├── bank_of_canada/ # 8 tools — Valet API
├── open_parliament/ # 10 tools — Parliament API
├── recalls/ # 6 tools — Healthy Canadians API
├── drug_database/ # 8 tools — Health Canada DPD
├── ckan/ # 7 tools — Open Data Portal
├── nutrient_file/ # 8 tools — Canadian Nutrient File
├── datastore/ # 6 tools — local SQLite persistence
├── ircc/ # 10 tools — IRCC Immigration Open Data
├── ontario/ # 6 tools — Ontario Open Data Catalogue
├── toronto/ # 12 tools — City of Toronto Open Data Portal
├── york_region/ # 27 tools — York Region ArcGIS Hub (4 portals)
├── british_columbia/ # 20 tools — BC Data Catalogue + WFS
├── manitoba/ # 20 tools — geoportal.gov.mb.ca ArcGIS Hub + 511 Manitoba
├── saskatchewan/ # 13 tools — geohub.saskatchewan.ca ArcGIS Hub + WSA water + SPSA fire bans
├── quebec/ # 18 tools — Données Québec CKAN
├── alberta/ # 24 tools — open.alberta.ca CKAN + AER + WMB + AHSGIS + 511
├── nova_scotia/ # 16 tools — data.novascotia.ca Socrata SODA
├── statcan/ # 15 tools — Statistics Canada WDS + SDMX
└── weather/ # 34 tools — MSC GeoMet OGC API
├── current/ # 5 tools — realtime conditions, forecast, alerts
├── climate/ # 7 tools — daily/monthly/normals/trends
├── aqhi/ # 3 tools — air quality health index
├── hydro/ # 5 tools — water levels, flow, flood risk
├── marine/ # 3 tools — marine forecasts, hurricane tracks
├── severe/ # 3 tools — radar, lightning, UV index
├── snow/ # 2 tools — snow depth, snow water equivalent
├── collections/ # 2 tools — collection browser and direct query
└── summary/ # 4 tools — composite summary, extremes, growing season, degree daysCada módulo sigue un patrón de 7 archivos:
Archivo | Propósito |
| Nombre y descripción del módulo |
| URL base, límites de tasa, TTL de caché, mapeos de API |
| Modelos de respuesta Pydantic v2 (siempre planos) |
| Funciones HTTP asíncronas con caché y limitación de tasa |
| Funciones de herramienta MCP decoradas con |
| Funciones |
| Funciones |
Los nuevos módulos se detectan automáticamente: coloca una carpeta en modules/ y se registra mediante FileSystemProvider.
# Install dependencies
uv sync
# Run tests (~2000 unit tests, ~15s)
uv run pytest
# Run integration tests against live APIs (~2min)
uv run pytest tests/integration/ -v -m integration --timeout=120
# Type check and lint
uv run pyright
uv run ruff check src/ tests/
# Coverage (must be ≥95%)
uv run pytest --cov=src/mcp_canada --cov-fail-under=95Cada módulo es autónomo. Para añadir una nueva API:
Crea src/mcp_canada/modules/your_api/ con el patrón de 7 archivos
Añade __tests__/ en la misma carpeta con pruebas unitarias
Añade pruebas de integración en tests/integration/test_tool_scenarios.py
Añade un documento de módulo en docs/modules/ y actualiza la tabla de Módulos en este README
Consulta CLAUDE.md para las convenciones de codificación.
Consulta CHANGELOG.md para los cambios versión por versión, o navega por GitHub Releases.
¿Has encontrado una vulnerabilidad? Por favor, no abras una incidencia pública. Envía un correo a contact@reyem.tech con los detalles y los pasos para reproducirla. Damos soporte a la última versión menor en PyPI.
Preguntas e ideas: GitHub Discussions
Errores y solicitudes de funciones: GitHub Issues
Contacto: contact@reyem.tech
Esta biblioteca accede a datos de las siguientes fuentes gubernamentales sujetos a sus respectivas licencias:
Nova Scotia Open Data — Licenciado bajo la Open Government Licence – Nova Scotia v1.1. Contiene información del sector público proporcionada por la Provincia de Nova Scotia.
Call a tool by name with the given arguments.
Use this to execute tools discovered via search_tools.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | The name of the tool to call | |
| arguments | No | Arguments to pass to the tool |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits such as return value, side effects, rate limits, or error handling. For a tool that invokes other tools, this lack of transparency is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two sentences with no redundant or irrelevant information. It is tightly written and front-loads the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the simple schema, the tool is a meta-tool that executes others. The description fails to explain the return value (the called tool's output) or address error conditions, prerequisites, or synchronization behavior. This leaves the agent without crucial context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage on both parameters ('name' and 'arguments'), so the schema already defines their purpose. The description adds no extra meaning beyond 'with the given arguments,' resulting in a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('call') and the resource ('tool'), and distinguishes from sibling tools like discover_tools and execute_batch by specifying it executes tools discovered via search_tools. The purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises to use this tool after discovering tools via search_tools, providing some context. However, it does not explicitly state when not to use it (e.g., for batch operations) or mention alternative tools like execute_batch. The guidance is minimal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for tools using natural language.
Returns matching tool definitions ranked by relevance, in the same format as list_tools.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language query to search for tools |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behaviors. It states the tool is a read-only search returning ranked definitions in a specific format, which is adequate. However, it omits any mention of side effects, rate limits, or scope (e.g., whether it searches across all modules). The behavior is minimally described but not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no extra words. It front-loads the action and efficiently communicates purpose and return format. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 param, no annotations, output schema exists), the description covers the core purpose and output. It could mention that results are from all available tools or that it is a read operation, but it is largely complete for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters (only 'query' with a description). The description rephrases the schema ('Natural language query') without adding new meaning, such as query format, length limits, or examples. Baseline score of 3 is appropriate since schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches for tools using natural language, with a specific verb ('Search') and resource ('tools'). It explains the return format (matching definitions ranked by relevance, like list_tools), which differentiates it from siblings like list_modules and plan_query.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for finding tools by description, but provides no explicit guidance on when to use it versus alternatives like list_tools or call_tool. There are no 'when not to use' or exclusion criteria, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute multiple tool calls in parallel and return aggregated results.
Accepts either a plan_query output (dict with 'steps' key) or a raw list of tool call objects. Runs all valid steps in parallel using asyncio.gather with per-step error isolation — one failed step does not cancel others.
Use for: running multiple tool calls at once, executing a plan from plan_query, batch queries across multiple APIs, parallel data fetching, multi-source aggregation.
Keywords: batch, execute, parallel, multiple tools, run plan, aggregate, multi-step, concurrent, simultaneous, gather, dispatch, bulk, workflow
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | en | |
| calls | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key behaviors: uses asyncio.gather for parallel execution, per-step error isolation (one failure doesn't cancel others), and accepts specific input formats. With no annotations, this adequately reveals the execution model.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Efficiently structured: purpose first, then behavior, followed by use cases and keywords. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers input format, execution model, error isolation, and use cases. With an output schema present, the return values are implicitly documented. Could add timeout details but overall comprehensive for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema coverage, description adds meaning by explaining the `calls` parameter accepts either a plan_query output or raw list of tool call objects. The `lang` parameter is an enum with default, and its description is not needed beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Execute multiple tool calls in parallel and return aggregated results,' effectively distinguishing it from siblings like call_tool (single call) and plan_query (generates plans without execution).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit use cases like 'running multiple tool calls at once, executing a plan from plan_query,' offering clear guidance on when to use. Could improve by mentioning when not to use, but positive guidance is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all registered API modules with tool counts and descriptions.
Use this to understand what data sources are available before calling discover_tools for specific queries. Keywords: modules, APIs, data sources, available tools, capabilities.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description discloses the read-only nature implicitly. It does not mention auth requirements, rate limits, or return format, though the tool is simple and likely safe.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, no fluff. The purpose is front-loaded, and keywords at the end aid searchability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description need not detail return values. It provides enough context to understand the tool's role, though it could mention the structure of the module list.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema coverage is 100% trivially. The description adds value by stating what the output contains (modules with tool counts and descriptions), which goes beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List all registered API modules with tool counts and descriptions' and positions it as a precursor to discover_tools, distinguishing its purpose from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly guides the agent to 'Use this to understand what data sources are available before calling discover_tools for specific queries', providing clear context but no when-not or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Plan a multi-step query across Canadian government data APIs.
Returns a structured execution plan with the most relevant tool names for the given natural language question. Use execute_batch to run the plan.
Use for: orchestrating queries that span multiple data sources, finding which tools to use for a complex question, multi-API planning, cross-module queries, batch query preparation.
Keywords: plan, query, multi-step, orchestrate, batch, cross-module, execution plan, tool selection, NL query, natural language, discover, which tools, what tools, how to query, planning, workflow
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | en | |
| query | Yes | ||
| top_k | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It transparently states the tool is for planning only and directs to 'execute_batch' for execution. It doesn't cover limitations or error behavior, but the planning nature is well communicated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with core purpose but includes a lengthy keyword list that adds redundancy. It is mostly concise but could be tightened for efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's role and relationship to 'execute_batch', but lacks examples, parameter guidance, and constraints. With an output schema present, some gaps are acceptable, but parameter semantics are missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description adds no meaning for parameters 'query', 'top_k', or 'lang'. It fails to describe input semantics beyond schema defaults and enums, requiring the agent to infer.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool plans multi-step queries across Canadian government data APIs and returns a structured execution plan. It uses specific verbs like 'plan' and 'orchestrate', and is easily distinguishable from siblings like 'call_tool' and 'execute_batch'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists use cases (e.g., multi-API planning, cross-module queries) and advises using 'execute_batch' for execution. This provides clear when-to-use guidance and references an alternative sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.7.0call_tooldiscover_toolsexecute_batchlist_modulesplan_queryTools have distinct purposes: call_tool vs execute_batch differ in batching; discover_tools vs plan_query both find tools but plan_query adds orchestration. Minor overlap but descriptions clarify.
All tool names follow a consistent verb_noun snake_case pattern (call_tool, discover_tools, execute_batch, list_modules, plan_query). No deviations.
5 tools is appropriate for a meta-server that provides discovery and execution. Not too few or too many for the gateway purpose, but could include a direct browse tool.
Covers the discovery-to-execution pipeline well: list modules, discover tools, plan queries, execute. Missing a tool for inspecting tool details directly, but discover_tools suffices.
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Hosted MCP server for live public-data APIs and Skills for AI agents.
Agent-native MCP server over 49M+ US public and government records, privacy-first, always current.
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
MCP server giving Claude AI access to 22+ NYC public-record databases for real estate due diligence
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