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cankilercioglu99-sudo

rekabet-karar-mcp

by cankilercioglu99-sudo
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The search tool and retrieval tool have clearly distinct functions: one queries by keywords and returns metadata, the other takes an opinion_id and returns the full text. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb-noun pattern: 'us_karar_ara' (search) and 'us_karar_getir' (get), sharing the 'us_karar_' prefix. This makes the toolset's structure predictable and easy to learn.

    Tool Count3/5

    With only two tools, the server feels thin but is not inappropriate for its narrow purpose. The calibration suggests 1-2 tools are borderline, so this score reflects the minimalism while acknowledging the clear scope.

    Completeness4/5

    The server covers the essential search-and-retrieve workflow for US antitrust decisions. There are minor gaps such as lack of filtering options, but the core lifecycle (find a case, fetch its text) is complete for the stated purpose.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden. The phrase 'tam metnini' (full text) contradicts the maks_karakter parameter which caps returned length, meaning the text may be truncated. It also fails to mention permissions, error cases, or return format, and the truncation behavior is especially relevant for the verbatim-passage guarantee.

    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 extremely concise: two sentences convey purpose, ID provenance, and a key use case. No redundant words, and it is front-loaded with the core purpose.

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

    Completeness3/5

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

    For a low-complexity tool with no output schema, the description explains the basic flow (search then retrieve) and return content type. However, it does not mention that the length may be capped by maks_karakter or describe the output structure, leaving notable gaps for an agent.

    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% and the schema already describes both parameters. The tool description adds no extra meaning beyond what the schema provides, and the 'full text' wording conflicts with the length-limit parameter. 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 verb ('getirir'), the resource ('bir gorusun tam metnini ve kunyesini'), and specifies that opinion_id comes from the us_karar_ara result. This distinguishes it from the sibling search tool.

    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 tells the agent that the tool should be used after a search (opinion_id is from us_karar_ara) and states a specific use case (footnote verification). However, it does not explicitly say when not to use it or name alternatives beyond the implicit search workflow.

    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?

    With no annotations, the description carries the transparency burden. It explicitly notes 'Kunye uydurulmaz; ham API verisidir' (does not fabricate metadata; raw API data), which is a meaningful behavioral disclosure. It also clarifies it returns specific fields (case name, court, date, citation, opinion_id) without describing every edge case.

    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 three sentences, front-loaded with the core function, and contains no redundant phrases. Every sentence adds value: what it does, query format examples, and data authenticity note.

    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 search tool with no output schema and no annotations, the description covers purpose, query language, return fields, and data trustworthiness. It doesn't explicitly mention optional filters like court/date, but those are fully described in the input schema, so the description is sufficiently complete for agent use.

    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 all six parameters documented. The description adds example queries and output fields but does not elaborate on parameter formats or nuances beyond the schema. Thus it meets the baseline without significantly enhancing parameter understanding.

    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 performs full-text search in US federal case law (CourtListener) specifically for competition/antitrust decisions, using a specific verb and resource. It explicitly distinguishes this as a search tool from the likely retrieval-oriented sibling tool by describing its search scope and output.

    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 provides clear usage context: user should supply English keywords and examples are given ('consumer welfare Sherman Act'). It doesn't explicitly state when not to use this tool or mention the sibling alternative, but the search vs. retrieval distinction is implied strongly enough.

    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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