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Vaishnavi-Raykar

Ressl AI MCP Server

by Vaishnavi-Raykar
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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation2/5

    The two tools have overlapping purposes, both searching within files, with only subtle differences in matching behavior (exact word vs. keyword with partial matches). This creates ambiguity, as an agent might struggle to choose between them without understanding the nuanced distinction, and they could easily be confused for similar tasks.

    Naming Consistency4/5

    The tool names follow a consistent snake_case pattern (exact_word_search, search_in_file) and use clear verbs ('search'), which aids readability. However, the naming is not perfectly consistent, as one specifies 'exact_word' while the other uses 'in_file', but overall, the pattern is predictable and coherent.

    Tool Count2/5

    With only 2 tools, the server feels thin and under-scoped for a general-purpose AI MCP server, suggesting limited functionality. This low count may not adequately cover the domain implied by the server name, potentially leaving gaps in capabilities and making it less useful for agents.

    Completeness2/5

    Inferred as a file search domain, the tool set is severely incomplete, lacking basic operations like listing files, reading file contents, or handling multiple files. The two tools only cover specific search variations, leaving significant gaps that will likely cause agent failures in broader tasks.

  • 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
    • 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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  • This repository includes a README.md file.

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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?

    No annotations are provided, so the description carries the full burden. It discloses the tool's behavior of finding all occurrences with partial matches, which is useful. However, it lacks details on error handling (e.g., what happens if the file doesn't exist), output format (e.g., line numbers, context), or performance aspects like case sensitivity, which are important for a search operation.

    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 two sentences with zero waste: the first sentence states the core purpose, and the second adds critical behavioral detail (partial matches). It is front-loaded and appropriately sized, with every sentence earning its place by providing essential information.

    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?

    Given the tool's moderate complexity (search operation with two parameters), no annotations, and no output schema, the description is minimally complete. It covers the basic purpose and key behavior (partial matches) but lacks details on output format, error handling, or advanced usage, leaving gaps that could hinder an AI agent's effective 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%, meaning the input schema already fully documents the parameters 'filePath' and 'keyword'. The description adds no additional semantic details beyond what the schema provides (e.g., no examples, constraints, or usage tips), so it meets the baseline of 3 without compensating further.

    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 specific action ('Search for a keyword'), target resource ('within a specified file'), and scope ('Finds all occurrences including partial matches'). It distinguishes from the sibling tool 'exact_word_search' by explicitly mentioning partial matches, which implies the sibling likely does exact matches only.

    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 context for when to use this tool (searching within files for keywords with partial matching). However, it does not explicitly state when not to use it or name the alternative sibling tool 'exact_word_search' as a direct comparison, though the distinction is implied through the mention of partial matches.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly describes the matching behavior ('exact word match', 'complete words, not partial matches'), which is valuable. However, it doesn't mention error handling, performance characteristics, or what happens if the file doesn't exist, leaving some behavioral aspects unspecified.

    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 perfectly concise with two sentences that each earn their place. The first sentence states the core functionality, and the second sentence provides crucial behavioral clarification about exact vs. partial matching. No wasted words or redundancy.

    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 moderate complexity (search operation with 3 parameters), no annotations, and no output schema, the description does well by clearly explaining the exact matching behavior. However, it doesn't describe the return format or what happens on no matches, which would be helpful for a search tool without output schema documentation.

    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 schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions, but it does reinforce the 'exact word' concept for the 'word' parameter. This meets the baseline of 3 when schema coverage is high.

    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 specific action ('Search for an exact word match') and resource ('within a specified file'), with explicit differentiation from partial matching. It distinguishes from the sibling tool 'search_in_file' by emphasizing exact word matching, providing clear purpose distinction.

    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 context about when to use this tool ('Only matches complete words, not partial matches'), which implicitly suggests alternatives for partial matching. However, it doesn't explicitly name the sibling tool 'search_in_file' or provide explicit when-not-to-use guidance, keeping it at a 4 rather than a perfect 5.

    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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