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., "@ca-eli-mcp Get the full text of the Criminal Code (C-46)"
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.
MCP server for Canadian federal legislation via the Justice Laws Website (laws-lois.justice.gc.ca), the Department of Justice Canada's official source for consolidated Acts and regulations. Bilingual (English/French).
No free-text search - the Justice Laws Website has no search API of
its own. This connector is by-code only (the same limitation as
ie-eli-mcp for Ireland): you need to already know the code (e.g.
"C-46" for the Criminal Code) or a short title to look one up.
Federal only - provincial and territorial legislation is out of scope.
No case law - CanLII's terms of service forbid bulk redistribution and its content API requires a per-identity key, which breaks the zero-cloud pattern this connector otherwise follows. Not attempted here.
Related MCP server: canlii-mcp
Tool | Purpose |
| Metadata (title, in-force status, last-consolidated date) for one act or regulation |
| Full consolidated XML text of the same document |
| Declare what this connector covers, when each family was captured, and - explicitly - what it does NOT cover. Every gap carries a fallback. |
Every response carries lex_uri (the XML source), source_url (the public
HTML page), and human_readable_citation (e.g. "Criminal Code (C-46)").
pip install ca-eli-mcpSmart App Control blocks unsigned executables, which covers uvx.exe, pip.exe
and the ca-eli-mcp.exe launcher that pip writes at install time. The python.exe and
py.exe from the python.org installer are signed by the Python Software
Foundation, so running the module through the interpreter works:
python -m pip install ca-eli-mcp
python -m ca_eli_mcppip.exe is blocked for the same reason, so install with python -m pip, not
pip install. If python is not on PATH, use the Windows launcher: py -3 -m ca_eli_mcp.
{ "mcpServers": { "ca-eli-mcp": { "command": "python", "args": ["-m", "ca_eli_mcp"] } } }Do not turn Smart App Control off to work around this - it cannot be re-enabled without reinstalling Windows.
Env var | Default |
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Apache-2.0 (code). Justice Laws Website content is Government of Canada material (see SOURCES.md).
Declare what this connector covers, how it is sourced, and what it does NOT cover.
Call this before telling a user that the law "does not contain" something, and whenever a search comes back empty: the absence may be a gap in this connector rather than in the law. Every gap carries a fallback saying where to look instead.
Returns:
Coverage with families, an as-of note, and a non-empty list of known gaps.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| status | No | |
| families | No | |
| as_of_note | Yes | States what the dates mean, and what they do not promise. |
| known_gaps | No | Never empty. An empty list would mean 'not checked', not 'no gaps'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavior beyond the annotations: absence of results may be a connector gap rather than a gap in the law, and each gap carries a fallback. This complements the openWorldHint and readOnlyHint annotations without contradicting them.
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 well-structured and front-loaded: first what it does, then when to call it, then what it returns. Every sentence earns its place, and there is no redundant text.
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 zero parameters, no required inputs, rich annotations, and an output schema, the description is complete. It also adds the key caveat about empty search results, which is essential for correct tool use.
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 tool has zero parameters, so parameter semantics are not a burden on the description. The baseline for zero-parameter tools is 4, and the description appropriately focuses on return and usage rather than inputs.
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 states a clear purpose: declare what the connector covers, how it is sourced, and what it does not cover. It also distinguishes itself from the sibling retrieval tools by framing the tool as coverage metadata rather than document/text access.
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 gives explicit triggers: call before telling a user the law 'does not contain' something, and whenever a search returns empty. It does not explicitly name sibling alternatives, but the guidance is actionable and context-rich.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch metadata for a Canadian federal act or regulation.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | e.g. ``"C-46"`` (Criminal Code) or ``"SOR-2018-151"`` (a regulation). | |
| lang | No | ``"eng"`` or ``"fra"`` (default ``"eng"``). | eng |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds only the 'metadata' scope and does not disclose any additional behavioral traits such as language-dependent behavior, response variability, or potential errors. It contributes minimal behavioral context beyond the annotations.
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?
A single sentence with a direct verb-object structure and zero wasted words. It is appropriately concise for the amount of information it conveys.
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 simple two-parameter schema, full schema documentation, an output schema, and strong read-only annotations, the description is nearly sufficient. The main gap is the absence of explicit routing guidance relative to the sibling tools, which prevents a perfect score.
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 coverage is 100%, with both parameters documented including examples and a default value. The description adds no independent parameter-level meaning, so the baseline of 3 applies.
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 states a specific verb ('Fetch metadata') and a clear resource ('Canadian federal act or regulation'). It implicitly differentiates from ca_get_text by focusing on metadata rather than text, but it does not explicitly distinguish itself from ca_coverage, so sibling differentiation is incomplete.
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 provides no guidance on when to use this tool versus ca_coverage or ca_get_text. There are no conditions, exclusions, or alternative routing hints, leaving the agent to infer usage from sibling names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch the full consolidated XML text of a Canadian federal act or regulation.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | e.g. ``"C-46"``. | |
| lang | No | ``"eng"`` or ``"fra"`` (default ``"eng"``). | eng |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already communicate that the operation is read-only, idempotent, and non-destructive. The description adds the useful behavioral detail that the result is 'full consolidated XML text', but it does not disclose return-envelope behavior, language handling, or any other operational context beyond the annotations.
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?
A single, front-loaded sentence that wastes no words. It immediately conveys the verb, resource, and output format without filler or 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?
For a simple two-parameter tool with complete schema coverage, rich annotations, and an output schema, the description is largely sufficient. The only meaningful gap is the lack of sibling-relation guidance, but that is secondary for basic invocation.
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 100%, and both 'code' and 'lang' are documented with examples and defaults. The description adds no parameter-level detail, so it does not need to compensate for schema gaps; baseline 3 applies.
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 uses a specific verb (Fetch) and a precise resource (full consolidated XML text of a Canadian federal act or regulation). It clearly distinguishes the tool from the siblings ca_get_document and ca_coverage by emphasizing 'full consolidated XML text'.
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 states what the tool does but gives no explicit guidance on when to choose it over ca_get_document or ca_coverage. It neither names alternatives nor provides exclusion criteria, leaving usage decisions to inference.
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.
v0.3.3ca_coverageca_get_documentca_get_textEach tool has a clearly distinct purpose: ca_get_document returns metadata, ca_get_text returns full XML content, and ca_coverage declares connector limitations. There is no meaningful overlap or ambiguity between them.
ca_get_document and ca_get_text follow a consistent ca_get_noun pattern, but ca_coverage breaks the pattern by omitting the verb. The shared ca_ prefix keeps the set recognizable, and no mixed casing styles are present.
Three tools is well-scoped for a focused read-only legislation retrieval server. Each tool serves a distinct role and there is no redundancy.
The server covers metadata retrieval, full-text retrieval, and coverage declarations, but lacks any search or listing capability for discovering acts and regulations. Users must already know the exact document identifier, which is a notable gap; the ca_coverage tool mitigates but does not eliminate this limitation.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
MCP for CanLII: Canadian case law and legislation metadata (federal, provincial, territorial).
Resolve, search and verify legal citations against the official sources, with provenance.
Search UK Acts, Statutory Instruments, and legislation with full text retrieval
Japan Law MCP — Japanese national laws & ordinances via the e-Gov Law API.
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/matematicsolutions/ca-eli-mcp'
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