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azamlerc

confluence-mcp-server

by azamlerc
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Both tools return the same structured data, but their inputs are clearly distinct (raw HTML vs lat/lon), and the descriptions explicitly state when to use each. There is no realistic selection ambiguity.

    Naming Consistency5/5

    Both tools use the same confluence_ prefix followed by a clear verb-noun pattern (get_point, parse_html). The naming is consistent and predictable.

    Tool Count4/5

    At two tools this is below the typical 3-15 range, but for the narrow read-only domain of fetching and parsing Confluence point pages, it is a reasonable minimal set without redundant tools.

    Completeness5/5

    The server covers the full implied workflow: either fetch a point by coordinates or parse already-fetched HTML into structured point data. There are no missing read operations or dead-end workflows for this domain.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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 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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  • 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

  • Behavior4/5

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

    With no annotations, the description carries the behavioral disclosure burden, and it does well by noting that confluence.org can be slow (10+ seconds) and that the tool has a 30s timeout. It also names the returned fields. This gives an agent useful operational expectations beyond the schema.

    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 short, front-loaded sentences convey the operation, return contents, and key performance warning without any filler. Every sentence earns its place.

    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?

    For a two-parameter tool with no output schema, the description covers the essential call context: what it does, what data it returns, and a critical timeout warning. It lacks only guidance on choosing between this tool and 'confluence_parse_html', but the core invocation context is sufficiently complete.

    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 already fully documents both parameters, including type, range, and examples, with 100% coverage. The description only restates 'latitude and longitude' and adds no additional parameter-specific meaning, so the baseline score applies.

    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 ('Fetch and parse') and a specific resource ('a confluence.org point page by latitude and longitude'), and it clearly states the structured data returned. This distinguishes the tool from the sibling 'confluence_parse_html' by emphasizing coordinate-based point lookup.

    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 the tool is for fetching point data from lat/lon coordinates, which is useful context. However, it does not explicitly mention when to prefer this over the sibling 'confluence_parse_html' or state any exclusions or alternatives.

    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?

    With no annotations, the description carries the burden of behavioral disclosure. It communicates that the tool is a parser rather than a fetcher and that it returns the same data as confluence_get_point, but it does not mention behavior on invalid HTML, error handling, or coordinate validation. Reasonable but incomplete for a tool with no annotation safety profile.

    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 sentences with no filler. The core action is front-loaded, the usage condition follows immediately, and the output comparison to the sibling tool is packed into a short, skimmable clause.

    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?

    All three required parameters are fully documented, and the return type is anchored by referencing confluence_get_point's structured data. The description covers when to use it, what input to provide, and what to expect back. It is not exhaustive about errors or the exact output shape, but it is sufficient for an agent to invoke it correctly.

    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%, with each parameter already meaningfully documented, especially the rationale for needing lat/lon. The description adds no additional parameter semantics beyond labeling html as raw HTML, so it meets the baseline but does not exceed it.

    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 opens with a specific verb and resource: 'Parse raw HTML from a confluence.org point page.' The closing sentence ties it to the sibling tool's output, clearly distinguishing this parse operation from fetching via confluence_get_point.

    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 a concrete use condition: 'Useful when you've fetched the HTML yourself (e.g. via browser).' It implies the alternative is confluence_get_point, but does not explicitly say 'use get_point when you have not fetched the HTML,' so exclusions are only implied.

    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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  • 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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