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Pythagraph RED MCP Server

by pythagraph
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JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation4/5

    The two tools have distinct purposes: get_graph_data provides detailed, comprehensive graph data, while get_graph_summary offers a concise overview. However, the inclusion of an includeDetails parameter in get_graph_summary could cause some overlap or confusion, as it blurs the line between summary and detailed data retrieval.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (get_graph_data and get_graph_summary), using snake_case and the same verb 'get'. This makes them predictable and easy to understand, with no deviations in naming style.

    Tool Count2/5

    With only 2 tools, the server feels thin for a graph analysis domain, as it lacks essential operations like creating, updating, or deleting graph data. This limited scope may hinder agents from performing full workflows, making it inappropriate for comprehensive graph management.

    Completeness2/5

    The server is severely incomplete for graph analysis, covering only retrieval operations (detailed and summary). There are significant gaps, such as no tools for creating, modifying, or deleting graphs, which are core to graph lifecycle management and will likely cause agent failures in broader tasks.

  • Average 3.5/5 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

  • This repository is licensed under ISC License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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 of behavioral disclosure. It mentions the return format ('formatted as tables and descriptions') and purpose ('analyzing graph structure'), but lacks critical details such as whether this is a read-only operation, potential rate limits, authentication requirements, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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

    Conciseness4/5

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

    The description is concise and front-loaded, with two sentences that efficiently convey the tool's purpose and usage. The first sentence covers retrieval and return format, while the second provides context. There's minimal waste, though the phrase 'Perfect for' could be slightly more formal.

    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 complexity (retrieving detailed graph data with 1 parameter), no annotations, and no output schema, the description is moderately complete. It covers the purpose and return format but lacks details on behavioral traits and output structure. For a tool with no structured output information, more elaboration on what 'comprehensive information' includes would be beneficial, making it adequate but with clear gaps.

    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 input schema has 100% description coverage, with the single parameter 'graphId' documented as 'The unique identifier for the graph to retrieve'. The description adds no additional parameter information beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to given the schema's completeness.

    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's purpose with a specific verb ('Retrieve') and resource ('detailed graph data from Pythagraph RED API'), and distinguishes it from the sibling tool 'get_graph_summary' by emphasizing 'detailed' and 'comprehensive information'. However, it doesn't explicitly contrast with the sibling tool's functionality beyond implying more detail.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage context by stating it's 'Perfect for analyzing graph structure and getting detailed insights', which suggests when to use this tool. However, it doesn't provide explicit guidance on when to choose this over 'get_graph_summary' or any exclusions, leaving the distinction somewhat vague.

    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?

    No annotations are provided, so the description carries the full burden. It discloses that the tool provides a 'concise summary' and mentions an optional parameter for more details, but it lacks information on behavioral traits such as permissions required, rate limits, error handling, or response format. The description doesn't contradict annotations, but it's insufficient for a mutation tool with no annotation coverage.

    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 appropriately sized and front-loaded, with two sentences that efficiently convey the tool's purpose and usage. Every sentence earns its place by providing essential information without redundancy or waste.

    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 complexity (read operation with 2 parameters, no output schema), the description is somewhat complete but has gaps. It explains the purpose and basic usage, but without annotations or output schema, it lacks details on behavioral aspects like response format, error cases, or integration with the sibling tool. This is adequate but not fully comprehensive.

    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 the schema already documents both parameters ('graphId' and 'includeDetails'). The description adds some value by explaining that 'includeDetails=true' enables 'more comprehensive analysis', but it doesn't provide additional syntax, format details, or examples beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

    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's purpose with a specific verb ('Get') and resource ('graph data from Pythagraph RED API'), and it distinguishes the tool by specifying it provides a 'concise summary' with 'overview statistics, node/edge type distributions, and key insights without overwhelming detail'. However, it doesn't explicitly differentiate from the sibling tool 'get_graph_data', which likely provides more detailed data.

    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 on when to use this tool: for a 'concise summary' with 'overview statistics' and 'key insights without overwhelming detail'. It also mentions an alternative usage mode ('Use includeDetails=true for more comprehensive analysis'), but it doesn't explicitly state when to use this tool versus the sibling 'get_graph_data' or provide exclusions.

    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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