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jeongho54

loadbench-mcp

by jeongho54
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct structural analysis task: beam bending, tipping stability, and support reactions. There is no overlap in functionality, and the descriptions clearly differentiate them.

    Naming Consistency4/5

    Two tools follow a verb_noun pattern (check_tipping, solve_supports) while one is noun_verb (beam_check). The naming is clear and readable, but the slight inconsistency prevents a perfect score.

    Tool Count5/5

    Three tools provide a focused set for basic structural analysis, covering beam checks, tipping, and support reactions. The count is appropriate for the domain without being too sparse or overwhelming.

    Completeness4/5

    The tools cover core structural analysis needs for simple loads and supports. Missing features like shear checks or multiple load combinations are minor gaps, but the set is functional for its stated purpose.

  • Average 4.5/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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 is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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 full burden. It explains the computation and assumptions (level ground, height irrelevant), but could better disclose limitations (e.g., rigid bodies, no friction/dynamics).

    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?

    Well-structured with a short intro, Args, and Returns. Every sentence adds value and is front-loaded with the core purpose.

    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 no output schema or annotations, the description explains the return fields in detail, provides parameter documentation, and lists use cases. Complete for a physics check tool.

    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?

    Schema coverage is 0%, but the description provides detailed Arg docstrings for both parameters, specifying units and structure, thus compensating fully.

    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 checks whether weights tip over a support footprint, with a clear physics explanation and use cases. However, it does not explicitly distinguish from sibling tools like beam_check or solve_supports.

    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 specific use cases ('shelving, stacked loads, machinery...') and explains when to use it, but does not explicitly state when not to use or compare to siblings.

    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?

    With no annotations, the description carries the full burden. It discloses the closed-form method, input combinations, and output fields. It does not mention potential errors, performance, or limitations, but the output list is transparent.

    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 well-structured: a brief summary, then clear sections for input details and output. Every sentence adds value with no redundancy or fluff.

    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 11 parameters (6 required), no output schema, and the domain complexity, the description fully covers inputs, outputs, and usage constraints. It leaves no ambiguity for an agent.

    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?

    Schema description coverage is 0%, so the description compensates fully. It explains all 11 parameters, including optional ones and their relationships (e.g., 'Give the section either directly OR as a solid rectangle'). Provides example values for common materials.

    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 states the tool checks 'whether a beam or shelf holds a load: bending stress and deflection.' It specifies 'closed-form Euler–Bernoulli check' and distinguishes from siblings by focusing on beam bending, not tipping or support reactions.

    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 explains when to use 'point' vs 'udl' load types and how to specify the section either directly or as a solid rectangle. It does not explicitly exclude cases or compare to sibling tools, but provides clear context for appropriate use.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description fully discloses behavioral traits: equal stiffness model, handling any number of supports, flagging over-capacity and lift-off. No contradictions.

    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 well-structured with summary, model details, and clear Args/Returns sections. Every sentence is informative and necessary without redundancy.

    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 no output schema, the description adequately covers return values (reactions, flags, warnings, explanation). It explains assumptions and scope, providing a complete picture for agent invocation.

    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?

    Despite 0% schema coverage, the description provides detailed arg format and semantics (arrays of objects with specific fields like id, x, y, capacity_n, magnitude_n), adding essential 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 it computes vertical force on supports for a rigid object, which is a specific verb+resource pair. It distinguishes from sibling tools like 'check_tipping' by focusing on support reaction forces rather than tipping stability.

    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 explains the modeling scenario (rigid object on point supports of equal stiffness, solving static equilibrium) and lists capabilities. It does not explicitly contrast with alternatives but provides enough context for appropriate use.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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