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sudhish

Indian Movies MCP Agent

by sudhish
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation4/5

    The tools are mostly distinct with clear purposes: get_movie_recommendations for filtered recommendations, get_random_movie for random selection, and search_movie for specific title lookup. However, get_movie_recommendations and get_random_movie could be slightly confused as both provide recommendations, though one is filtered and the other random.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case (get_movie_recommendations, get_random_movie, search_movie). The naming is predictable and readable throughout the set.

    Tool Count3/5

    With only 3 tools, the set feels thin for a movie recommendation domain. While it covers basic recommendation and search functions, it lacks operations for browsing genres, languages, or detailed movie information, which might limit agent effectiveness.

    Completeness2/5

    The tool surface is significantly incomplete for an Indian movies domain. There are no tools for getting movie details (e.g., plot, cast, ratings), filtering by criteria beyond basic preferences, or managing user interactions (e.g., saving favorites). This will likely cause agent failures in comprehensive movie-related tasks.

  • Average 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
    • 0 commits 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the search action but doesn't describe traits like whether it's read-only, if it requires authentication, rate limits, or what the output format might be. This leaves significant gaps for a tool with no 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, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to understand quickly.

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

    Completeness2/5

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

    Given no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and usage context, which are essential for a search tool. The high schema coverage doesn't compensate for these missing elements.

    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 schema description coverage is 100%, so the input schema already documents the 'title' parameter adequately. The description adds no additional meaning beyond what the schema provides, such as search behavior or result details, meeting the baseline for high schema coverage.

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

    Purpose4/5

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

    The description clearly states the action ('Search for') and resource ('a specific Indian movie by title'), making the purpose unambiguous. It doesn't explicitly differentiate from sibling tools like 'get_movie_recommendations' or 'get_random_movie', which would require a 5.

    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 like 'get_movie_recommendations' or 'get_random_movie'. The description implies usage for searching by title but lacks explicit context or exclusions.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool's purpose but doesn't describe how it works—whether it returns a fixed number of results, uses collaborative filtering, requires authentication, has rate limits, or what the output format looks like. The description is functional but lacks operational details needed for an agent to understand the tool's behavior beyond basic input parameters.

    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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every part of the sentence contributes to understanding the tool's function.

    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 tool with 4 parameters, 100% schema coverage, and no output schema, the description is adequate but incomplete. It covers the basic purpose and filtering scope but lacks details on output (e.g., what data is returned, format, pagination) and behavioral context (e.g., how recommendations are generated, limitations). Given the absence of annotations and output schema, the description should provide more operational context to be fully helpful.

    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 description mentions filtering by 'genre, language, or rating preferences,' which aligns with three of the four parameters in the schema (genre, language, min_rating). It doesn't mention 'year_after,' but since schema description coverage is 100% (all parameters are well-documented in the schema), the baseline score of 3 is appropriate. The description adds minimal value beyond what the schema already provides.

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

    Purpose4/5

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

    The description clearly states the verb ('Get') and resource ('Indian movie recommendations') with specific filtering criteria ('based on genre, language, or rating preferences'). It distinguishes from 'get_random_movie' by specifying filtered recommendations rather than random selection, though it doesn't explicitly differentiate from 'search_movie' which might offer broader search capabilities.

    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?

    The description provides no guidance on when to use this tool versus its siblings ('get_random_movie' or 'search_movie'). It mentions filtering criteria but doesn't specify whether this is for personalized recommendations, curated lists, or how it differs from the search functionality. No exclusions or alternative scenarios are mentioned.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns a random Indian movie recommendation but doesn't explain how randomness is implemented, if there are any biases, rate limits, or what the output format looks like. This leaves significant gaps in understanding the tool's behavior beyond its basic purpose.

    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, efficient sentence that directly states the tool's purpose without any wasted words. It's front-loaded with the core functionality, making it easy to parse and understand immediately.

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

    Completeness2/5

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

    Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is minimal but adequate for the basic purpose. However, it lacks details on behavioral aspects like output format, randomness mechanism, or differentiation from siblings, which could help an agent use it more effectively. For a recommendation tool, more context would be beneficial.

    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 input schema has 0 parameters with 100% coverage, so there are no parameters to document. The description appropriately doesn't discuss parameters, which is correct for this case. A baseline of 4 is applied since no parameter information is needed or provided.

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

    Purpose4/5

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

    The description clearly states the verb ('Get') and resource ('random Indian movie recommendation'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_movie_recommendations' or 'search_movie', which might also provide movie recommendations but with different selection criteria or search capabilities.

    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?

    The description provides no guidance on when to use this tool versus alternatives like 'get_movie_recommendations' or 'search_movie'. It doesn't specify if this is for quick suggestions, unbiased picks, or when detailed filtering isn't needed, leaving the agent to infer usage context.

    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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