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matematicsolutions

cl-eli-mcp

by matematicsolutions
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.2.2

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one fetches a norm by URI, the other searches by title. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent cl_<verb>_<noun> pattern (cl_get_norm, cl_search_norms), with snake_case throughout.

    Tool Count4/5

    Two tools is minimal but appropriate for the focused domain of Chilean legislation norms, covering search and retrieval. It's slightly thin but not inadequate.

    Completeness3/5

    The server covers basic search and retrieval of norms by URI and title search, but lacks operations like listing all norms or searching by other fields, leaving some gaps.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 8 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.

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

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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 readOnlyHint=true, destructiveHint=false, etc. The description adds 'Fetch full detail' which is consistent but does not provide additional behavioral information such as error handling, rate limits, or authentication needs. With annotations covering the safety profile, the description adds minimal extra transparency.

    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, concise sentence that immediately conveys the tool's purpose. It is front-loaded with the key action and resource, with no wasted 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?

    With one parameter, clear annotations, and an output schema (as indicated by context), the description is adequate. It could be enhanced by noting the output schema covers the full detail, but it is not necessary. The description is complete for this simple tool.

    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?

    Schema description coverage is 100%, so the schema already documents the parameter. The tool description mentions 'by its BCN resource URI' which mirrors the schema's description. No new semantic information is added beyond what the schema provides. Baseline of 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?

    Description clearly states it fetches full detail of one Chilean norm by its BCN resource URI. The verb is specific, and the distinction from sibling tool 'cl_search_norms' is implied by focusing on a single norm by URI rather than searching.

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

    Usage Guidelines3/5

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

    Usage is implied: use this when you have a specific URI. However, no explicit guidance is given about when not to use it or when to prefer the sibling tool 'cl_search_norms'. The description lacks explicit when/when-not alternatives.

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

  • Behavior2/5

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

    Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which convey safety and idempotency. However, the description does not add any behavioral details beyond 'full-text search', such as pagination, rate limits, or search scope. Minimal added value.

    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 sentence of five words, with zero waste. It is front-loaded with the key verb 'search', making the purpose immediately clear. Every word earns its place.

    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, good annotations, and presence of an output schema, the description is largely complete. It clearly specifies the resource (Chilean legislation titles) and operation (full-text search). Minor missing details like search behavior for special characters or case sensitivity are not critical for a basic search tool.

    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?

    Schema description coverage is 100%, so the input schema already documents both parameters (query and limit) with descriptions. The tool description does not provide any additional semantic information beyond what is in the schema, meeting the baseline.

    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 verb 'search' and the resource 'Chilean legislation titles', making the tool's purpose immediately obvious. It distinguishes itself from the sibling tool cl_get_norm, which presumably retrieves a specific norm.

    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 implicitly guides usage: use this tool for full-text search over titles, not for retrieving specific norms. The sibling tool cl_get_norm provides an alternative, and the context signals give enough information to infer when to use each.

    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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Glama performs regular codebase and documentation scans to:

  • 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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