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Server Quality Checklist

75%
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  • Latest release: v0.2.1

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: searching by title, fetching metadata by TAR code, and fetching full text by TAR code. The descriptions make the boundaries unambiguous.

    Naming Consistency5/5

    All tool names follow the consistent pattern 'lt_verb_noun' (lt_search, lt_get_act, lt_get_text). The prefix and verb style are uniform.

    Tool Count5/5

    Three tools is well-scoped for a focused legislation access server. Each tool fulfills a distinct and necessary role in the search-and-retrieve workflow.

    Completeness5/5

    The tools cover the essential read-only workflow: discover an act via search, retrieve metadata, and retrieve full text. There are no obvious dead ends or missing operations for this domain.

  • Average 3.9/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 11 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 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 readOnly, openWorld, idempotent, and non-destructive. The description adds the boundary that it fetches 'metadata' (not full text), which is useful, but does not disclose any additional behavioral traits like output format, rate limits, or error conditions. Consistent with annotations, no contradiction.

    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?

    A single, front-loaded sentence conveys the core purpose without filler. Every word contributes, making it highly concise and well-structured.

    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?

    Given the simple one-parameter schema, strong annotations, and presence of an output schema, the description is adequate. It communicates the essential scope (metadata by TAR code) and the structured fields cover the rest, forming a complete picture for this 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 coverage is 100% with a well-described parameter (tar_kodas) including an example and origin from lt_search. The description mentions the TAR code but adds no semantic detail beyond the schema, so 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 uses a specific verb ('Fetch') and resource ('Lithuanian act metadata') and highlights the key ('TAR code'). It clearly distinguishes from siblings like lt_search and lt_get_text by focusing on metadata retrieval by identifier.

    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 is provided on when to use this tool versus alternatives. The description does not mention prerequisites, exclusions, or relationship to sibling tools. The schema's parameter description hints at lt_search but the main description lacks explicit usage direction.

    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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering safety. The description adds a useful behavioral detail: the search is restricted to the act title (not full text). It does not add context like pagination or case sensitivity, but annotations lower the bar.

    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 with no filler. It front-loads the action and resource, and 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?

    For a simple search tool with full schema coverage and annotations, the description is adequate. It explains what the tool does, and the schema covers parameters. It lacks explicit guidance on when to use it versus siblings, but this does not undermine overall completeness given the low complexity.

    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%, with detailed descriptions for limit, contains, and doc_type. The description does not add parameter info beyond what the schema already provides, so a baseline score 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?

    The description uses a specific verb ('Search') and resource ('Lithuanian acts') with a clear parameter ('whose title contains a substring'). It clearly distinguishes from siblings lt_get_act and lt_get_text, which presumably retrieve specific acts or texts.

    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?

    The description implies usage: use this tool when you need to find acts by title substring. It does not explicitly state when not to use it or name alternatives, but the context is clear given the sibling names. No exclusions or alternative scenarios are provided.

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

  • Behavior4/5

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

    The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds useful behavioral context by specifying that it fetches the 'full Lithuanian text' (not a summary), which helps set expectations. It doesn't contradict annotations.

    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, complete sentence that is front-loaded with the action ('Fetch') and resource. No filler or redundant words. It is concise and well-structured.

    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 simple, single-parameter tool with comprehensive annotations and an output schema, the description provides sufficient context. It clearly states what the tool does and what input is needed. The output schema handles return-value details, so no further explanation is required.

    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%, and the parameter 'tar_kodas' is documented with an example. The description mentions 'by its TAR code', which aligns with the schema but does not add extra semantic meaning beyond what the schema already provides. 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 uses the specific verb 'Fetch' with a clear resource ('full Lithuanian text of an act') and the key identifier ('by its TAR code'). This clearly distinguishes it from siblings like lt_search (searching) and lt_get_act (retrieving an act, likely metadata).

    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?

    The description implies the use case: when you need the full text of an act given a TAR code. However, it does not explicitly state when to prefer this over lt_get_act or lt_search, nor does it mention any exclusions or alternatives. The guidance is implied rather than explicit.

    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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