The mcp-canada server gives AI agents structured access to Canadian federal, provincial, and municipal government data through 266 tools across 19+ API modules.
Core Orchestration
discover_tools — Natural language (BM25) search to find relevant tools without browsing all 266
call_tool — Execute any tool by name with given arguments
list_modules — Browse all API modules with descriptions and tool counts
plan_query — Generate structured multi-step execution plans for complex natural language questions
execute_batch — Run multiple tool calls in parallel with per-step error isolation
Federal Data
Bank of Canada — Exchange rates, interest rates, commodity prices, inflation
CKAN Open Data — 80,000+ federal datasets
Health Canada Drug Database — Drug products, ingredients, schedules
IRCC Immigration — PR, study/work permits, Express Entry, asylum statistics
Canadian Nutrient File — Food nutrition data
Open Parliament — Bills, MPs, votes, Hansard debates
Recalls & Safety — Food, vehicle, and health product recalls
Statistics Canada — Time series, cube metadata, SDMX filtering
Environment Canada Weather — Real-time conditions, climate, air quality, hydrology, marine, radar
Provincial Data
Alberta (CKAN + AER energy + wildfire + health + transport), British Columbia (CKAN + WFS geospatial), Manitoba (ArcGIS Hub + 511), Saskatchewan (ArcGIS Hub + water + fire bans), Nova Scotia (Socrata SODA), Ontario (3,000+ datasets), Quebec (federated CKAN across 139 organizations)
Municipal Data
Toronto (TTC transit, neighbourhoods, 311, RentSafe) and York Region (4 ArcGIS Hub portals: York Region, Markham, Newmarket, Aurora)
Local Storage
SQLite Datastore — Persist and JOIN data from multiple APIs in a single SQL query for cross-API intelligence
Key Features
Full bilingual support (lang: "en" | "fr") on all tools
Consistent response envelope with source attribution and cache status
Built-in caching and per-source rate limiting
Structured error responses with suggestions
Provides access to ArcGIS Hub portals for municipal data, enabling queries of geospatial datasets from Canadian municipalities like York Region, Markham, Newmarket, and Aurora.
Provides a local SQLite datastore for persistent storage and cross-API SQL JOINs, enabling agents to store and query data across multiple Canadian government APIs.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-canada show me recent product recalls in Canada"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
295 tools, ~107 prompts, and ~141 resources across 9 federal APIs + 9 provincial APIs + 2 municipal APIs + 1 local SQLite datastore — exchange rates, parliamentary data, product recalls, drug information, 80K+ open datasets, food nutrition data, real-time weather, immigration statistics, Ontario provincial data, Toronto municipal data, York Region ArcGIS Hub data, British Columbia CKAN + WFS geospatial data, Quebec Données Québec CKAN + ArcGIS IQA data, Alberta open data + AER energy + WMBappServices wildfire + AHSGIS health + 511 Alberta transport, Manitoba geoportal (ArcGIS Hub) + 511 Manitoba transport, Saskatchewan geoportal (ArcGIS Hub) + WSA water infrastructure + SPSA fire bans, Nova Scotia Socrata SODA portal (data.novascotia.ca), New Brunswick federal-CKAN discovery + GeoNB bare ArcGIS Server geospatial data + gnb.socrata.com Socrata portal + key-gated 511 NB transport, and persistent local storage. All bilingual (English/French).
First ArcGIS Hub module — shared infrastructure in
shared/arcgis_hub.pyis reusable for future Canadian municipal modules (BC, Calgary, Edmonton, and other cities publishing via ArcGIS Hub). First OGC WFS module — BC introduces WFS 2.0 (OGC) support viashared/ogc.py, making WFS the third portal technology alongside CKAN and ArcGIS Hub. Seedocs://bc/wfs-query-guidefor the CKAN→WFS two-step workflow.
# Auto-configure your platform (interactive)
uvx mcp-canada install
# Or name platforms directly
uvx mcp-canada install claude-desktop cursor vscodeSupports 14 platforms: Claude Desktop, Claude Code, Cursor, VS Code, Windsurf, Zed, Codex CLI, Gemini CLI, Amazon Q, OpenCode, Cline, Roo Code, Goose CLI, Junie CLI.
Add to ~/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-canadaFlag | Description | Example |
| Transport protocol |
|
| Port for SSE/HTTP |
|
| Load only specific modules |
|
| INFO-level logging |
|
| DEBUG-level logging |
|
Environment variable: MCP_CANADA_MODULES=bank_of_canada,recalls
Related MCP server: canlii-mcp
See the documentation site for cross-API intelligence scenarios — from tracing prairie drought to the Canadian dollar, to building pharmaceutical safety audits, to assembling MP accountability briefs, to joining data from multiple APIs in a single SQL query. Each example includes the exact prompt and tool chain you can run today. The source remains in EXAMPLES.md.
With 250 tools, listing all of them would consume half an agent's context window. Instead, BM25 search lets agents find exactly what they need:
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, ...}]}Agents see 5 always-visible tools:
Tool | Purpose |
| BM25 natural language search across all tools |
| Execute any discovered tool by name |
| List available API modules with tool counts |
| Plan a multi-step query across Canadian government data APIs |
| Run multiple tool calls in parallel with per-step error isolation |
All tools accept lang: "en" | "fr" for bilingual support. Responses include a _meta envelope with source attribution and cache status. Browse the complete, searchable tool reference for current tool parameters and source APIs.
Module | Level | Tools | Prompts | Resources | Description |
— | 5 | — | — | Always-visible orchestration tools ( | |
Federal | 8 | 5 | 7 | Exchange rates, interest rates, commodity prices, inflation — Valet API | |
Federal | 7 | 5 | 7 | 80,000+ federal datasets — open.canada.ca | |
Federal | 8 | 5 | 7 | Drug products, ingredients, schedules — Health Canada DPD | |
Federal | 10 | 5 | 7 | PR, study/work permits, Express Entry, asylum — IRCC Open Data | |
Federal | 8 | 5 | 7 | Food nutrition data — Canadian Nutrient File | |
Federal | 10 | 5 | 7 | Bills, MPs, votes, ballots, Hansard debates — Open Parliament API | |
Federal | 6 | 4 | 6 | Food, vehicle, and health product recalls — Healthy Canadians | |
Federal | 15 | 6 | 8 | Time series, cube metadata, SDMX filtering — StatCan WDS | |
Federal | 34 | 6 | 8 | Conditions, climate, air quality, hydrology, marine, radar — MSC GeoMet | |
Provincial | 24 | 6 | 7 | CKAN + AER energy + WMBappServices wildfire + AHSGIS health + 511 Alberta — open.alberta.ca | |
Provincial | 20 | 6 | 7 | CKAN + WFS geospatial — BC Data Catalogue | |
Provincial | 20 | 6 | 7 | ArcGIS Hub + 511 Manitoba — geoportal.gov.mb.ca | |
Provincial | 13 | 6 | 7 | ArcGIS Hub + WSA water + SPSA fire bans — geohub.saskatchewan.ca | |
New Brunswick ( | Provincial | 22 | 6 | 7 | Federal CKAN + GeoNB bare ArcGIS Server + gnb.socrata.com Socrata + key-gated 511 NB transport — geonb.snb.ca |
Provincial | 16 | 6 | 7 | Socrata SODA portal (aquaculture, environment, health) — data.novascotia.ca | |
Provincial | 6 | 4 | 6 | 3,000+ provincial datasets — Ontario Open Data | |
Provincial | 18 | 6 | 7 | Federated CKAN (139 orgs) — Données Québec | |
Municipal | 12 | 6 | 8 | TTC, neighbourhoods, 311, RentSafe — Toronto Open Data | |
Municipal | 27 | 5 | 8 | 4 ArcGIS Hub portals (York Region, Markham, Newmarket, Aurora) | |
Local | 6 | 4 | 6 | SQLite persistence for cross-API SQL JOINs — | |
Total | 295 | ~107 | ~141 |
All tools return a consistent envelope:
{
"_meta": {
"source": {"api": "bank-of-canada-valet", "url": "https://..."},
"cached": true,
"lang": "en",
"timestamp": "2026-04-04T12:00:00Z"
},
"data": [ ... ]
}Errors return:
{
"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 daysEach module follows a 7-file pattern:
File | Purpose |
| Module name and description |
| Base URL, rate limits, cache TTLs, API mappings |
| Pydantic v2 response models (always flat) |
| Async HTTP functions with caching and rate limiting |
|
|
|
|
|
|
New modules are auto-discovered — drop a folder in modules/ and it registers via 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=95Each module is self-contained. To add a new API:
Create src/mcp_canada/modules/your_api/ with the 7-file pattern
Add colocated __tests__/ with unit tests
Add integration tests in tests/integration/test_tool_scenarios.py
Add a module doc in docs/modules/ and update the Modules table in this README
See CLAUDE.md for coding conventions.
See CHANGELOG.md for version-by-version changes, or browse GitHub Releases.
Found a vulnerability? Please do not open a public issue. Email contact@reyem.tech with details and reproduction steps. We support the latest minor version on PyPI.
Questions & ideas: GitHub Discussions
Bugs & feature requests: GitHub Issues
Contact: contact@reyem.tech
Data from the following government sources is accessed by this library subject to their respective licences:
Nova Scotia Open Data — Licensed under the Open Government Licence – Nova Scotia v1.1. Contains public sector information provided by the Province of 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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