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evgenygurin

R2R FastMCP Server

by evgenygurin
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Python
Hybrid

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation2/5

    The two tools have significant functional overlap and ambiguous boundaries. Both 'rag' and 'search' perform retrieval from the knowledge base with nearly identical search parameters and presets, differing mainly in that 'rag' includes LLM generation while 'search' returns raw results. This overlap could easily cause misselection, as agents might struggle to choose between them for retrieval-focused tasks.

    Naming Consistency5/5

    Tool names follow a perfectly consistent pattern with simple, descriptive verbs ('rag' and 'search') in lowercase. Both names clearly indicate their primary function without mixing conventions, making them predictable and easy to understand within the server's scope.

    Tool Count2/5

    With only 2 tools, the server feels severely under-scoped for a RAG/knowledge base system. While the tools are feature-rich individually, the set lacks basic operations like knowledge base management (e.g., add/remove documents), configuration updates, or status checks. This minimal count limits agent workflows and suggests an incomplete surface.

    Completeness2/5

    The tool surface has significant gaps for a RAG server. There are no tools for managing the knowledge base (ingesting, updating, or deleting documents), monitoring system status, or configuring core settings. While 'rag' and 'search' cover querying comprehensively, the absence of management operations creates dead ends for agents needing to interact with the underlying data.

  • Average 4.3/5 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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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?

    The description adds significant behavioral context beyond what annotations provide. While annotations declare readOnlyHint=true, openWorldHint=true, and idempotentHint=true, the description details the different search modes (semantic, hybrid, graph, web), result formats, and the comprehensive nature of returns. It doesn't contradict annotations and provides rich operational context about search capabilities.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with clear sections (overview, Args, Returns, Examples) but is quite lengthy. While most content is valuable given the complex parameter set, some redundancy exists (e.g., repeating default values that are in the schema). The front-loaded overview is effective, but the overall length could be more concise.

    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 complexity (13 parameters, 0% schema coverage), the description provides complete context. It explains all parameters in detail, includes return format information (though an output schema exists), provides multiple usage examples, and covers the tool's comprehensive capabilities. For a complex search tool with rich functionality, this description leaves few gaps.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage for 13 parameters, the description carries the full burden of parameter documentation and excels at this. It provides detailed explanations for all parameters including query, preset options with descriptions, boolean flags with defaults, numeric ranges, and advanced options. The 'Args' section comprehensively documents what each parameter does beyond the bare schema.

    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 tool performs 'comprehensive search on R2R knowledge base with full parameter control' and mentions semantic, hybrid, graph, and web search capabilities. It distinguishes from the sibling 'rag' tool by focusing on search rather than retrieval-augmented generation. However, it doesn't explicitly contrast with 'rag' beyond the different verb focus.

    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 good usage guidance with 'Use presets for common scenarios or customize all parameters manually' and includes multiple examples showing different use cases. It doesn't explicitly state when to use this vs the 'rag' sibling tool, but the presets (development, refactoring, debug, research, production) suggest appropriate contexts for this search tool.

    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 indicate readOnlyHint=false, openWorldHint=true, destructiveHint=false, covering basic safety. The description adds valuable context beyond annotations by detailing search modes (semantic, hybrid, graph), generation parameters, and presets for common use cases, though it lacks explicit rate limits or authentication needs.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is appropriately front-loaded with a clear purpose, but it is lengthy due to extensive parameter details. While informative, some redundancy exists (e.g., repeating defaults in descriptions that are also in the schema), reducing efficiency. Every sentence adds value, but structure could be more streamlined.

    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 tool's high complexity (17 parameters, no schema descriptions, annotations present, output schema exists), the description is highly complete. It covers purpose, usage, detailed parameter semantics, and examples, compensating fully for schema gaps and leveraging annotations for behavioral basics, making it sufficient for agent use.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage for 17 parameters, the description fully compensates by providing detailed explanations for each parameter, including defaults, ranges, options (e.g., preset and model examples), and behavioral effects (e.g., temperature impact). This adds significant meaning beyond the bare 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?

    The description clearly states the tool performs 'Retrieval-Augmented Generation (RAG) query' with 'full parameter control,' specifying the verb (perform RAG) and resource (knowledge base). It distinguishes from the sibling 'search' tool by emphasizing generation with an LLM rather than just retrieval.

    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 usage through presets (e.g., 'development' for code questions, 'debug' for precise answers) and examples, but does not explicitly state when to use this tool versus the 'search' sibling tool or other alternatives beyond implied differences.

    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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