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jiweiqi

fluent-mcp-server

by jiweiqi
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.2.0

  • Disambiguation5/5

    Each tool has a clear, distinct purpose: get_manual_link fetches a direct link to a specific manual section, list_topics provides pre-mapped common topics, and search_help performs general searches. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern: get_manual_link, list_topics, search_help. The naming is predictable and readable.

    Tool Count4/5

    With 3 tools, the set is compact but covers the essential operations for Fluent documentation access: direct linking, topic listing, and search. This count is appropriate for a focused server, though slightly lower than typical ranges.

    Completeness4/5

    The tools cover the main use cases for finding documentation: specific manual links, common topics, and free-text search. Minor gaps like browsing by category or full table of contents are acceptable for the scope.

  • Average 4.2/5 across 3 of 3 tools scored.

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

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 the burden. It mentions the tool returns a list of pre-mapped topics with links, but does not disclose any limitations, ordering, or whether the list is static/dynamic. Adequate but lacks depth.

    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?

    Extremely concise with two short sentences plus a returns line. Front-loaded with the verb 'List' and 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?

    Given no parameters and an output schema (as per context), the description adequately covers the purpose. However, it could be slightly improved by clarifying what 'common' or 'pre-mapped' means in this context.

    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?

    The tool has zero parameters, so baseline is 4. The description adds no parameter information, which is acceptable as there is nothing to document.

    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 lists common Fluent topics with documentation links, using a specific verb and resource. It is distinguishable from siblings 'get_manual_link' and 'search_help' which serve different purposes.

    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?

    No guidance on when to use this tool versus its siblings or other alternatives. The description does not include any when-to-use or when-not-to-use context.

    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?

    The description clearly states the input parameters and the return value (direct URL). For a read-only tool with no annotations, it adequately covers behavior. No side effects or additional traits disclosed, but sufficient for the simple operation.

    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 concise: a single sentence summarizing purpose followed by clearly structured Args and Returns sections. All information is relevant and efficiently presented.

    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 description covers the essential aspects: purpose, parameters with examples, and return type. Given the tool's simplicity and existing output schema, it is complete enough. Minor gap: no mention of behavior for invalid manual names.

    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 provides essential semantics: it lists valid manual names (user_guide, tui, theory, udf) and gives examples for section paths. This fully compensates for 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's purpose: retrieving a direct link to a specific manual or section. It lists valid manual names and the optional section path, distinguishing it from sibling tools like list_topics and search_help.

    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 for getting a direct URL, but does not provide explicit guidance on when to use this tool versus siblings or exclude other cases. No when-not or alternatives are mentioned.

    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?

    Describes what the tool returns (URLs and navigation hints) and what is not done (actual content retrieval). No hidden behaviors are implied. Since annotations are absent, the description carries the burden and does well.

    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?

    Concise, front-loaded description with no unnecessary words. Clear separation of purpose, usage, and parameter details.

    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?

    Provides enough context for an agent to understand the tool's role, including the need for WebFetch and the nature of the return. Output schema is not shown but mentioned in description; completeness is adequate for a search tool.

    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?

    Adds examples for the 'query' parameter and explains the purpose of 'max_suggestions' with default value. Though schema coverage is 0%, the description compensates by providing practical guidance beyond the 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?

    Clearly states it finds ANSYS Fluent documentation URLs. Example queries and the specific parameter descriptions reinforce the purpose. Differentiates from siblings by being a search tool.

    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?

    Advises using WebFetch to retrieve content from URLs, which is a useful follow-up step. However, it does not explicitly state when to use this tool over siblings like get_manual_link or list_topics.

    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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