Skip to main content
Glama
matematicsolutions

mcp-saos

by matematicsolutions
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.1.2

  • Disambiguation5/5

    Each tool targets a distinct operation: retrieving a judgment by ID, general search, and search by case number. There is no overlap in functionality.

    Naming Consistency4/5

    Tool names mostly follow a verb_noun or verb_preposition_noun pattern, but 'search' is just a verb while 'get_judgment' uses get. However, 'search_by_case' is descriptive and consistent with 'search'.

    Tool Count5/5

    Three tools are appropriate for a focused database query server. Each tool serves a clear purpose without redundancy.

    Completeness3/5

    Core operations (search and retrieve) are present, but there are gaps: no pagination for search results, and full judgment text is limited to 2000 characters. Missing features like listing courts or fetching full text could hinder some workflows.

  • Average 4.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 15 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 MIT License.

  • 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.

  • Add related servers to improve discoverability.

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

  • Behavior4/5

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

    Annotations already declare read-only, idempotent, non-destructive. Description adds valuable behavioral details: coverage uneven by court type, dates may be OCR-distorted, possible error types. This goes beyond annotations to set proper expectations.

    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?

    Description is a single paragraph of 5 sentences, front-loaded with purpose, no redundant sentences. Every sentence adds information about coverage, limitations, use cases, or errors.

    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?

    Covers scope, limitations, use cases, and errors. Missing details about result format or pagination behavior (though pageNumber parameter exists). Given no output schema, a brief note on return structure would improve completeness.

    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 parameters are already well-documented. Description adds marginal value by warning about date distortion (relevant to dateFrom/dateTo) and mentioning courtType enumeration pitfalls, but does not elaborate on each parameter beyond schema.

    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 starts with clear verb 'Przeszukuje' (searches) and specific resource 'baze orzeczen sadow polskich w SAOS'. It distinguishes from siblings by implying this tool is for broad search across multiple fields, while siblings like 'search_by_case' are likely more specific.

    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?

    Description provides clear context for when to use (analysis of case law, precedents, searching by content/signature/judge/legal basis) and explicitly states what not to use (administrative courts not indexed). However, it does not directly compare to sibling tools or give explicit when-not-to-use scenarios.

    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?

    Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds details about content truncation (first 2000 characters) and summary availability, which go beyond annotations. No contradictions.

    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 paragraph of three sentences, each serving a purpose: action, return contents, and source/errors. It is front-loaded with the core function and contains no redundant information.

    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 one-parameter retrieval tool without output schema, the description covers purpose, input source, errors, and return structure (metadata, summary, truncated content). It is fully adequate for an agent to understand and use the 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 descriptive parameter name and type. The description adds context about ID source (from search tools) and error handling, but the schema already adequately documents the parameter 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 action (pobiera pelne orzeczenie) and the resource (SAOS by numeric ID). It lists return contents and differentiates from sibling tools (search, search_by_case) which are for finding IDs, not retrieving full records.

    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 specifies when to use: when an ID is available from search or search_by_case. It lists possible errors (missing_arg, not_found, upstream_error) providing guidance on handling. While it doesn't explicitly state when not to use, the context is clear.

    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?

    Annotations already indicate read-only, idempotent, and open-world. The description adds valuable context about potential missing results and reasons, and lists specific errors, enhancing transparency beyond 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 concise paragraph covering purpose, examples, usage equivalence, failure scenarios, and errors, with no superfluous content.

    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 tool with one required parameter and no output schema, the description addresses purpose, parameter, error handling, and limitations, making it sufficiently complete.

    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 schema itself defines the parameter. The tool description provides examples and explains the format, adding modest value but not essential new meaning.

    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 it searches for judgments by case number, provides examples, and distinguishes it from the sibling 'search' tool by noting it is an equivalent shortcut with a specific parameter.

    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 explains that it is equivalent to 'search' with caseNumber, and clarifies scenarios when results may be missing (administrative court or not yet published). It gives error types but lacks explicit when-not-to-use or alternative tool guidance.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

mcp-saos MCP server

Copy to your README.md:

Score Badge

mcp-saos MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/matematicsolutions/mcp-saos'

If you have feedback or need assistance with the MCP directory API, please join our Discord server