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matematicsolutions

au-eli-mcp

by matematicsolutions
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.3.3

  • Disambiguation5/5

    Each tool has a clearly distinct role: searching by title, fetching full text by FRLI identifier, and reporting coverage/gaps. There is no functional overlap between the tools.

    Naming Consistency4/5

    The au_ prefix is consistent, and two tools follow the verb_noun pattern (search_acts, get_text). However, au_coverage breaks the pattern by being a bare noun rather than an action-oriented name, creating a minor inconsistency.

    Tool Count4/5

    Three tools is on the lean side but appropriate for a narrowly scoped legal research connector: search, retrieve, and coverage awareness. The count feels intentional rather than sparse.

    Completeness4/5

    The core workflow of searching and retrieving Act text is covered, and the explicit coverage tool acknowledges known gaps with fallbacks. Missing features like browsing all acts or version history are not clearly required for the stated purpose.

  • Average 4.1/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 12 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • This repository includes a glama.json configuration file.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations already declare read-only, idempotent, and non-destructive behavior, so description does not need to repeat safety traits. However, description adds no detail about response format, error handling, or what constitutes 'current consolidated text'. Without annotations, this would score lower.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    One sentence, no fluff. All necessary information is front-loaded. Every word serves a purpose.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's simplicity, one parameter, and the presence of an output schema (not shown but indicated), the description is sufficient. It could mention the output type explicitly, but the schema covers that.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 100% description coverage for the single parameter 'frli_id' with an example. The description does not add further semantic context beyond what the schema already provides.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    Clearly states the action ('Fetch'), resource ('current consolidated text of an Act'), and identifier type ('FRLI identifier'). Distinguishes from sibling tool 'au_search_acts' which searches acts rather than retrieves text.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance on when to use this tool versus alternatives. Does not mention prerequisite of having an FRLI identifier, nor refer to the sibling tool for searching. The description implies usage but provides no explicit context for decision-making.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations already provide comprehensive behavioral cues (read-only, idempotent, open-world). The description adds the 'partial' matching nuance but does not go into detail about matching behavior. Given the rich annotations, the description is satisfactory.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single short sentence, front-loaded with the core verb and resource. No unnecessary words.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a simple search tool with one parameter and existing output schema and annotations, the description is mostly complete. It doesn't specify result ordering or pagination, but these may be covered by the output schema.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema covers the 'title' parameter with examples (100% coverage). The description's mention of 'partial' title adds minimal extra meaning. Baseline 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool searches Commonwealth Acts by partial title, using a specific verb and resource. It distinguishes from the sibling tool 'au_get_text', which likely retrieves full text.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage for finding Acts by title, and the sibling tool name suggests a complementary retrieval function. However, it does not explicitly state when to use this tool versus the alternative.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Beyond the readOnly/openWorld/idempotent annotations, the description discloses that gaps are known and carry fallbacks, and it explains the open-world consequence of empty search results. This is valuable behavioral context that the annotations alone would not provide.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is compact and front-loads the core purpose, then adds usage triggers and return details. Every sentence adds information and none repeats schema or annotation data.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a zero-parameter, read-only tool with an output schema, the description covers what it does, when to call it, what it returns, and how to interpret gaps. Nothing needed for correct invocation is missing.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The tool has zero parameters, so parameter-level description is not needed; the rubric baseline of 4 applies. No parameter semantics are missing.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description uses a specific verb ('Declare') and a clear resource ('what this connector covers, how it is sourced, and what it does NOT cover'), and it states the returned type ('Coverage'). This clearly differentiates it from the search and text-retrieval siblings.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    It gives explicit triggers: call before asserting the law 'does not contain' something and whenever a search returns empty, because the absence may be a connector gap. It does not explicitly name alternatives or state when not to use it, so it stops short of a 5.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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