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sinagilassi

PyMemSim-MCP

by sinagilassi
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    Each tool targets a clearly distinct operation: validating YAML content, analyzing feed flow rate bounds, and running a membrane simulation. There is no overlap in functionality.

    Naming Consistency3/5

    Tool names use snake_case but mix verb-noun patterns: 'check_yaml_reference' and 'simulate_gas_hfm' start with verbs, while 'hfm_feed_flow_rate_analyzer' is a noun phrase without a verb, creating inconsistency.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped to core membrane simulation tasks. Each tool serves a necessary, non-redundant purpose.

    Completeness3/5

    The set covers validation, analysis, and simulation, but lacks tools for user input setup, result visualization, or parameter tuning, leaving notable gaps for a complete workflow.

  • Average 3.1/5 across 3 of 3 tools scored. Lowest: 2.4/5.

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

    • No community issues in the last 6 months
    • 26 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

  • This repository is licensed under Apache 2.0.

  • 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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 only states 'run a simulation' without explaining what the simulation does (e.g., steady-state, transient), what inputs are required beyond the YAML, or any side effects. This is insufficient for a complex tool.

    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 a single sentence, which is concise, but it is under-specified and lacks structure. It does not provide enough information to be useful, so conciseness does not compensate for missing content.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (many parameters, no annotations, output schema exists), the description is far too minimal. It fails to cover essential aspects like simulation behavior, parameter roles, or how to structure inputs. The description is completely inadequate.

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

    Parameters1/5

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

    Schema description coverage is 0% according to context, meaning no parameter descriptions in the schema. The description adds no parameter information at all, only mentioning 'using pyThermoDB YAML reference content'. This leaves the many parameters completely unexplained.

    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 runs a gas hollow-fiber membrane simulation and mentions using pyThermoDB YAML reference content. However, it does not distinguish itself from sibling tools like check_yaml_reference or hfm_feed_flow_rate_analyzer, lacking differentiation.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites such as having a valid YAML reference. This omission makes it hard for an agent to decide when to invoke this tool.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries full disclosure burden. However, it does not mention whether the tool is read-only, has side effects, rate limits, or any other behavioral traits beyond computing bounds. The description is too vague to inform the agent about potential impacts.

    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 a single, front-loaded sentence with no redundancy. Every word contributes to the purpose. It is appropriately sized and efficiently structured.

    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 complexity (14 parameters, nested objects, output schema exists), the description is minimal but adequate. It summarizes inputs but lacks detail on output nature or edge cases. However, since an output schema is present, it partially compensates. Overall, it is just sufficient.

    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 baseline is 3. The description adds no additional meaning beyond categorizing inputs (geometry, operating conditions, permeance). It does not explain parameter relationships, units, or constraints beyond what the schema already provides.

    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's purpose: analyzing recommended feed flow rate bounds for a hollow-fiber membrane module using geometry, operating conditions, and permeance inputs. It distinguishes from siblings 'check_yaml_reference' and 'simulate_gas_hfm' by specifying a unique output (feed flow rate bounds) and inputs.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives, or any prerequisites. It simply states what the tool does without contextual advice or exclusion criteria.

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

  • Behavior2/5

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

    With no annotations, the description carries full burden but only states the purpose, not behavioral traits like what validation entails, side effects, or prerequisites. It does not contradict annotations since none exist.

    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 a single, front-loaded sentence with no wasted words, efficiently conveying the tool's core function.

    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 is simple with one parameter and an output schema. The description covers the essential purpose; return values are explained by the output schema, so no further detail needed.

    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 coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema's description of 'yaml_content'.

    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 uses a specific verb ('Validate') and a distinct resource ('pythermodb YAML reference content'), clearly distinguishing it from sibling tools focused on flow rate and gas simulation.

    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?

    No explicit guidance on when to use or avoid this tool, but the domain-specific name and sibling context imply it should be used when validating YAML content for pyThermoDB.

    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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