295 个工具、约 107 个提示词和约 141 个资源，涵盖 9 个联邦 API + 9 个省级 API + 2 个市级 API + 1 个本地 SQLite 数据存储——汇率、议会数据、产品召回、药品信息、80K+ 开放数据集、食品营养数据、实时天气、移民统计、安大略省数据、多伦多市数据、约克区 ArcGIS Hub 数据、不列颠哥伦比亚省 CKAN + WFS 地理空间数据、魁北克省 Données Québec CKAN + ArcGIS IQA 数据、阿尔伯塔省开放数据 + AER 能源 + WMBappServices 野火 + AHSGIS 健康 + 511 Alberta 交通、曼尼托巴省地理门户（ArcGIS Hub）+ 511 Manitoba 交通、萨斯喀彻温省地理门户（ArcGIS Hub）+ WSA 水利基础设施 + SPSA 禁火令、新斯科舍省 Socrata SODA 门户（data.novascotia.ca）、新不伦瑞克省联邦 CKAN 发现 + GeoNB 裸 ArcGIS Server 地理空间数据 + gnb.socrata.com Socrata 门户 + 需密钥的 511 NB 交通，以及持久化本地存储。全部支持双语（英语/法语）。
首个 ArcGIS Hub 模块——
shared/arcgis_hub.py中的共享基础设施可复用于未来的加拿大市级模块（BC、卡尔加里、埃德蒙顿以及其他通过 ArcGIS Hub 发布数据的城市）。 首个 OGC WFS 模块——BC 通过shared/ogc.py引入了 WFS 2.0（OGC）支持，使 WFS 成为继 CKAN 和 ArcGIS Hub 之后的第三种门户技术。参见docs://bc/wfs-query-guide了解 CKAN→WFS 两步工作流。
# Auto-configure your platform (interactive)
uvx mcp-canada install
# Or name platforms directly
uvx mcp-canada install claude-desktop cursor vscode支持 14 个平台：Claude Desktop、Claude Code、Cursor、VS Code、Windsurf、Zed、Codex CLI、Gemini CLI、Amazon Q、OpenCode、Cline、Roo Code、Goose CLI、Junie CLI。
添加到 ~/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-canada标志 | 描述 | 示例 |
| 传输协议 |
|
| SSE/HTTP 端口 |
|
| 仅加载指定模块 |
|
| INFO 级别日志 |
|
| DEBUG 级别日志 |
|
环境变量：MCP_CANADA_MODULES=bank_of_canada,recalls
Related MCP server: canlii-mcp
请参阅**文档站点**了解跨 API 智能场景——从追踪草原干旱与加元的关系，到构建药品安全审计，到汇编议员问责简报，再到在单个 SQL 查询中联接多个 API 的数据。每个示例都包含您现在即可运行的精确提示词和工具链。源文件保留在 EXAMPLES.md 中。
面对 250 个工具，全部列出将消耗代理一半的上下文窗口。取而代之的是，BM25 搜索让代理精确找到所需内容：
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, ...}]}代理可以看到5 个始终可见的工具：
工具 | 用途 |
| 跨所有工具的 BM25 自然语言搜索 |
| 按名称执行任何已发现的工具 |
| 列出可用的 API 模块及工具数量 |
| 规划跨加拿大政府数据 API 的多步骤查询 |
| 并行运行多个工具调用，并逐步隔离错误 |
所有工具都接受 lang: "en" | "fr" 以支持双语。响应包含带有来源归属和缓存状态的 _meta 信封。浏览完整、可搜索的**工具参考**以了解当前的工具参数和源 API。
模块 | 级别 | 工具 | 提示词 | 资源 | 描述 |
— | 5 | — | — | 始终可见的编排工具（ | |
联邦 | 8 | 5 | 7 | 汇率、利率、大宗商品价格、通货膨胀——Valet API | |
联邦 | 7 | 5 | 7 | 80,000+ 联邦数据集——open.canada.ca | |
联邦 | 8 | 5 | 7 | 药品、成分、管制目录——Health Canada DPD | |
联邦 | 10 | 5 | 7 | 永久居民、学习/工作许可、快速通道、庇护——IRCC Open Data | |
联邦 | 8 | 5 | 7 | 食品营养数据——Canadian Nutrient File | |
联邦 | 10 | 5 | 7 | 法案、议员、投票、选票、Hansard 辩论——Open Parliament API | |
联邦 | 6 | 4 | 6 | 食品、车辆和健康产品召回——Healthy Canadians | |
联邦 | 15 | 6 | 8 | 时间序列、立方体元数据、SDMX 过滤——StatCan WDS | |
联邦 | 34 | 6 | 8 | 天气状况、气候、空气质量、水文、海洋、雷达——MSC GeoMet | |
省级 | 24 | 6 | 7 | CKAN + AER 能源 + WMBappServices 野火 + AHSGIS 健康 + 511 Alberta——open.alberta.ca | |
省级 | 20 | 6 | 7 | CKAN + WFS 地理空间——BC Data Catalogue | |
省级 | 20 | 6 | 7 | ArcGIS Hub + 511 Manitoba——geoportal.gov.mb.ca | |
省级 | 13 | 6 | 7 | ArcGIS Hub + WSA 水利 + SPSA 禁火令——geohub.saskatchewan.ca | |
New Brunswick ( | 省级 | 22 | 6 | 7 | 联邦 CKAN + GeoNB 裸 ArcGIS Server + gnb.socrata.com Socrata + 需密钥的 511 NB 交通——geonb.snb.ca |
省级 | 16 | 6 | 7 | Socrata SODA 门户（水产养殖、环境、健康）——data.novascotia.ca | |
省级 | 6 | 4 | 6 | 3,000+ 省级数据集——Ontario Open Data | |
省级 | 18 | 6 | 7 | 联邦式 CKAN（139 个组织）——Données Québec | |
市级 | 12 | 6 | 8 | TTC、社区、311、RentSafe——Toronto Open Data | |
市级 | 27 | 5 | 8 | 4 个 ArcGIS Hub 门户（约克区、万锦市、新市、奥罗拉） | |
本地 | 6 | 4 | 6 | 用于跨 API SQL JOIN 的 SQLite 持久化—— | |
总计 | 295 | ~107 | ~141 |
所有工具都返回一致的响应信封：
{
"_meta": {
"source": {"api": "bank-of-canada-valet", "url": "https://..."},
"cached": true,
"lang": "en",
"timestamp": "2026-04-04T12:00:00Z"
},
"data": [ ... ]
}错误返回：
{
"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 days每个模块遵循7 文件模式：
文件 | 用途 |
| 模块名称和描述 |
| 基础 URL、速率限制、缓存 TTL、API 映射 |
| Pydantic v2 响应模型（始终扁平） |
| 带缓存和速率限制的异步 HTTP 函数 |
| 使用 |
|
|
|
|
新模块会自动发现 — 在 modules/ 中放入文件夹，它会通过 FileSystemProvider 注册。
# 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=95每个模块都是独立的。要添加新的 API：
使用 7 文件模式创建 src/mcp_canada/modules/your_api/
添加同目录的 __tests__/ 并包含单元测试
在 tests/integration/test_tool_scenarios.py 中添加集成测试
在 docs/modules/ 中添加模块文档，并更新本 README 中的模块表
有关编码约定，请参阅 CLAUDE.md。
有关逐版本变更，请参阅 CHANGELOG.md，或浏览 GitHub Releases。
发现漏洞？请不要公开提交 issue。请通过电子邮件 contact@reyem.tech 发送详细信息和复现步骤。我们支持 PyPI 上的最新次要版本。
问题与想法： GitHub Discussions
错误与功能请求： GitHub Issues
联系方式： contact@reyem.tech
本库访问以下政府来源的数据，须遵守其各自的许可证：
新斯科舍省开放数据 — 根据 Open Government Licence – Nova Scotia v1.1 许可。包含新斯科舍省提供的公共部门信息。
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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