295のツール、約107のプロンプト、約141のリソースを、9つの連邦API + 9つの州API + 2つの市町村API + 1つのローカルSQLiteデータストアにわたって提供 — 為替レート、議会データ、製品リコール、医薬品情報、80,000以上のオープンデータセット、食品栄養データ、リアルタイム気象、移民統計、オンタリオ州データ、トロント市データ、ヨーク地域ArcGIS Hubデータ、ブリティッシュコロンビア州CKAN + WFS地理空間データ、ケベック州Données Québec CKAN + ArcGIS IQAデータ、アルバータ州オープンデータ + AERエネルギー + WMBappServices山火事 + AHSGIS健康 + 511 Alberta交通、マニトバ州ジオポータル（ArcGIS Hub）+ 511 Manitoba交通、サスカチュワン州ジオポータル（ArcGIS Hub）+ WSA水インフラ + SPSA火災禁止令、ノバスコシア州Socrata SODAポータル（data.novascotia.ca）、ニューブランズウィック州連邦CKANディスカバリ + GeoNBベアArcGIS Server地理空間データ + gnb.socrata.com Socrataポータル + キー制限付き511 NB交通、そして永続的なローカルストレージ。すべてバイリンガル（英語/フランス語）。
初のArcGIS Hubモジュール —
shared/arcgis_hub.pyの共有インフラは、将来のカナダの市町村モジュール（BC、カルガリー、エドモントン、その他ArcGIS Hubで公開している都市）に再利用可能です。 初のOGC WFSモジュール — BCはshared/ogc.pyによるWFS 2.0（OGC）サポートを導入し、WFSはCKAN、ArcGIS Hubに次ぐ3番目のポータル技術となります。CKAN→WFSの2段階ワークフローについてはdocs://bc/wfs-query-guideを参照してください。
# Auto-configure your platform (interactive)
uvx mcp-canada install
# Or name platforms directly
uvx mcp-canada install claude-desktop cursor vscode14のプラットフォームに対応: Claude Desktop、Claude Code、Cursor、VS Code、Windsurf、Zed、Codex CLI、Gemini CLI、Amazon Q、OpenCode、Cline、Roo Code、Goose CLI、Junie CLI。
~/Library/Application Support/Claude/claude_desktop_config.jsonに追加:
{
"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フラグ | 説明 | 例 |
| トランスポートプロトコル |
|
| SSE/HTTP用ポート |
|
| 特定のモジュールのみ読み込む |
|
| INFOレベルのログ |
|
| DEBUGレベルのログ |
|
環境変数: MCP_CANADA_MODULES=bank_of_canada,recalls
Related MCP server: canlii-mcp
クロスAPIインテリジェンスシナリオについては、ドキュメントサイト を参照してください — プレーリーの干ばつからカナダドルまでの追跡、医薬品安全性監査の構築、国会議員の説明責任ブリーフの作成、複数のAPIのデータを単一のSQLクエリで結合する方法まで。各例には、今日すぐ実行できる正確なプロンプトとツールチェーンが含まれています。ソースはEXAMPLES.mdに残っています。
250のツールがあるため、すべてを一覧表示するとエージェントのコンテキストウィンドウの半分を消費してしまいます。代わりに、BM25検索によりエージェントは必要なものを正確に見つけられます:
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, ...}]}エージェントには常時表示される5つのツールが表示されます:
ツール | 目的 |
| 全ツールにわたるBM25自然言語検索 |
| 発見したツールを名前で実行 |
| ツール数付きで利用可能なAPIモジュールを一覧表示 |
| カナダ政府データAPIにわたる多段階クエリを計画 |
| ステップごとのエラー分離付きで複数のツール呼び出しを並列実行 |
すべてのツールはバイリンガル対応のためlang: "en" | "fr"を受け付けます。レスポンスにはソース帰属とキャッシュステータスを含む_metaエンベロープが含まれます。現在のツールパラメータとソースAPIについては、完全で検索可能な**ツールリファレンス** を参照してください。
モジュール | レベル | ツール | プロンプト | リソース | 説明 |
— | 5 | — | — | 常時表示のオーケストレーションツール（ | |
連邦 | 8 | 5 | 7 | 為替レート、金利、商品価格、インフレ — Valet API | |
連邦 | 7 | 5 | 7 | 80,000以上の連邦データセット — open.canada.ca | |
連邦 | 8 | 5 | 7 | 医薬品、成分、スケジュール — Health Canada DPD | |
連邦 | 10 | 5 | 7 | 永住権、学業/就労許可、Express Entry、亡命 — IRCCオープンデータ | |
連邦 | 8 | 5 | 7 | 食品栄養データ — カナダ栄養ファイル | |
連邦 | 10 | 5 | 7 | 法案、国会議員、投票、採決、ハンサード議事録 — Open Parliament API | |
連邦 | 6 | 4 | 6 | 食品、車両、健康製品のリコール — Healthy Canadians | |
連邦 | 15 | 6 | 8 | 時系列、キューブメタデータ、SDMXフィルタリング — StatCan WDS | |
連邦 | 34 | 6 | 8 | 気象状況、気候、大気質、水文学、海洋、レーダー — MSC GeoMet | |
州 | 24 | 6 | 7 | CKAN + AERエネルギー + WMBappServices山火事 + AHSGIS健康 + 511 Alberta — open.alberta.ca | |
州 | 20 | 6 | 7 | CKAN + WFS地理空間 — BCデータカタログ | |
州 | 20 | 6 | 7 | ArcGIS Hub + 511 Manitoba — geoportal.gov.mb.ca | |
州 | 13 | 6 | 7 | ArcGIS Hub + WSA水 + SPSA火災禁止令 — geohub.saskatchewan.ca | |
ニューブランズウィック州（ | 州 | 22 | 6 | 7 | 連邦CKAN + GeoNBベアArcGIS Server + gnb.socrata.com Socrata + キー制限付き511 NB交通 — geonb.snb.ca |
州 | 16 | 6 | 7 | Socrata SODAポータル（水産養殖、環境、健康）— data.novascotia.ca | |
州 | 6 | 4 | 6 | 3,000以上の州データセット — オンタリオオープンデータ | |
州 | 18 | 6 | 7 | 連合CKAN（139組織）— Données Québec | |
市町村 | 12 | 6 | 8 | TTC、近隣地域、311、RentSafe — トロントオープンデータ | |
市町村 | 27 | 5 | 8 | 4つのArcGIS Hubポータル（ヨーク地域、マーカム、ニューマーケット、オーロラ） | |
ローカル | 6 | 4 | 6 | クロスAPI SQL JOIN用のSQLite永続化 — | |
合計 | 295 | 約107 | 約141 |
すべてのツールは一貫したエンベロープを返します:
{
"_meta": {
"source": {"api": "bank-of-canada-valet", "url": "https://..."},
"cached": true,
"lang": "en",
"timestamp": "2026-04-04T12:00:00Z"
},
"data": [ ... ]
}エラー時は以下を返します:
{
"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各モジュールは7ファイルパターンに従います:
File | Purpose |
| モジュール名と説明 |
| ベースURL、レート制限、キャッシュTTL、APIマッピング |
| Pydantic v2 レスポンスモデル（常にフラット） |
| キャッシュとレート制限を備えた非同期HTTP関数 |
|
|
|
|
|
|
新しいモジュールは自動検出されます — modules/ にフォルダを置くと、FileSystemProvider 経由で登録されます。
# 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各モジュールは自己完結型です。新しいAPIを追加するには：
src/mcp_canada/modules/your_api/ を7ファイル構成で作成します
ユニットテストを含む __tests__/ を同じ場所に追加します
tests/integration/test_tool_scenarios.py に統合テストを追加します
docs/modules/ にモジュールドキュメントを追加し、このREADMEのモジュール表を更新します
コーディング規約については CLAUDE.md を参照してください。
バージョンごとの変更は CHANGELOG.md を参照するか、GitHub Releases を閲覧してください。
脆弱性を発見しましたか？ 公開issueを開かないでください。詳細と再現手順を contact@reyem.tech にメールしてください。PyPIで最新のマイナーバージョンをサポートしています。
質問とアイデア: GitHub Discussions
バグと機能リクエスト: GitHub Issues
連絡先: contact@reyem.tech
このライブラリは、以下の政府ソースからのデータを、それぞれのライセンスに基づいてアクセスします：
Nova Scotia Open Data — Open Government Licence – Nova Scotia v1.1 の下でライセンスされています。ノバスコシア州が提供する公共部門情報を含みます。
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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/ReyemTech/mcp-canada'
If you have feedback or need assistance with the MCP directory API, please join our Discord server