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HEP-KE

FastMCP Science Demo

by HEP-KE
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one generates random point data, the other plots a sine wave. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: generate_random_points and plot_sine_wave. The naming is clear and predictable.

    Tool Count3/5

    With only 2 tools, the server feels minimal for a science demo. While it might be intentionally lightweight, it is on the low end of reasonable scope.

    Completeness2/5

    The tool set lacks integration between the two capabilities. There is no tool to visualize the random points or generate data for the sine wave plot, leaving a gap in typical scientific workflows.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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?

    With no annotations provided, the description must disclose behavioral traits. It mentions output is a CSV with deterministic random data via a seed. However, it lacks details on file overwriting, directory creation, or any constraints on 'small' dataset size. Some gaps remain.

    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, with a clear opening sentence and a brief usage note. The Args section is structured. Every sentence adds value, though some detail (like exactly what 'point data' means) is missing.

    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?

    Output schema exists, so the description does not need to detail return values. However, it doesn't describe the CSV columns or the nature of the point data (e.g., 2D coordinates). This omission means the agent may not fully understand what the tool produces.

    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?

    Schema coverage is 0%, so the description bears the burden. It explains each parameter: output_dir (directory for CSV), count (number of rows, min 1), seed (deterministic). This adds constraints and purpose beyond the schema's types and defaults.

    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 generates random point data and saves as CSV. It uses specific verbs and resources (generate, save as CSV), and while it doesn't explicitly differentiate from its sibling 'plot_sine_wave', the purposes are clearly distinct.

    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 says 'use this tool when a workflow needs a small deterministic dataset for downstream analysis or visualization', providing some context. However, it does not specify when not to use it or mention the sibling tool as an alternative, leaving ambiguity.

    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 must carry the full burden. It mentions saving a PNG to output_dir but does not disclose potential side effects like overwriting existing files, permission requirements, or whether any temporary files are created. However, for a simple plotting tool, this is minimally acceptable.

    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?

    Two brief sentences plus a structured Args section. Every sentence adds value: the first sentence states the core function, the second provides usage context, and the Args section clarifies parameters. No wasted words.

    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?

    The tool has an output schema, so the description need not cover return values. It adequately describes the main behavior and parameters. Minor omission: it doesn't mention that the sine wave amplitude or frequency might be hardcoded, but for a simple tool this is acceptable.

    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?

    Schema coverage is 0%, but the description includes an Args section that adds meaningful context: 'Directory where the PNG should be written' for output_dir and 'Must be at least 2' for num_points. This goes beyond the schema's default value and title.

    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?

    Description clearly states 'Plot a sine wave and save it as a PNG', specifying the verb, resource, and output format. This distinguishes it from the sibling 'generate_random_points' which likely generates random points instead of a deterministic sine wave.

    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?

    Explicitly states when to use: 'when a workflow needs a simple generated plot artifact for downstream inspection or reporting'. While it doesn't explicitly list when-not-to-use or alternatives, the context is clear and sufficient for typical scenarios.

    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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