confluence-mcp-server
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Fetch and search are completely distinct operations: one retrieves a specific page by ID, the other finds pages via CQL. There is no ambiguity between the two.
Naming Consistency4/5Both names follow a verb_prefix pattern in snake_case, but 'fetch_confluence_page' includes an extra object while 'search_confluence' does not. This is a minor inconsistency, but still predictable.
Tool Count2/5Only two tools for a Confluence server feels too thin. A typical integration requires at least page creation, updates, and space management, not just fetch and search.
Completeness2/5The server covers only read-oriented operations (fetch a page, search) and lacks all write capabilities such as create, update, delete, or list spaces. This is a significant gap for a domain like Confluence.
Average 3.5/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- No commit activity data available
- 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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If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
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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?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is established. The description adds the fact that it performs CQL search via an orchestrator, but it does not disclose any optional behaviors like fuzzy matching, result limits, or potential side effects. These are present in the schema, but the description contributes minimal extra behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence and highly concise, with no filler. However, the phrase 'highlighted' is vague and 'shared Confluence orchestrator' may be unnecessary internal jargon, slightly detracting from clarity. Still, it is appropriately brief and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 8 parameters and an output schema, the description is minimal. It does not explain what 'highlighted CQL search' means, how results are delivered, or any orchestrator-specific behaviors. The schema and annotations cover parameters and safety, but the description lacks sufficient context about the overall search workflow, making it adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The tool description adds no parameter-level detail; the schema fully defines every parameter with types, defaults, and semantics. Therefore the description does not meaningfully enhance parameter understanding beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Execute a ... CQL search') and the resource ('Confluence'), and it distinguishes from the sibling tool fetch_confluence_page by focusing on search. However, the term 'highlighted' is ambiguous and the phrase 'via the shared Confluence orchestrator' introduces unnecessary implementation detail that may confuse rather than clarify.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 such as fetch_confluence_page. It does not state any prerequisites, exclusions, or scenarios where a different tool would be more appropriate. The only contextual hint is in the schema's query parameter description, but the tool description itself lacks usage direction.
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?
Annotations already declare readOnlyHint=true, so the description is not burdened with stating it is read-only. It adds useful context about what data is retrieved (storage body, version data, ancestry), but does not disclose any additional behavioral traits such as pagination, error conditions, or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
One concise sentence with no filler. It front-loads the core action and data scope. Every word is meaningful, and the description is appropriately brief for a straightforward fetch tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, one parameter, and the presence of an output schema (which should explain return values), the description is sufficient for understanding the tool's purpose. It could be slightly more explicit about when to use it versus search_confluence, but overall it provides a complete enough picture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the single parameter (content_id), so the schema already fully explains the parameter. The description adds no additional parameter-level detail, which is acceptable given the schema's completeness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies the action ('hydrate') and the resource (a Confluence page) with specific data components (storage body, version data, ancestry). This clearly distinguishes it from sibling search_confluence, which presumably locates pages rather than fetching full content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied: the tool fetches a page's detailed data by content_id. However, there is no explicit guidance on when to use this instead of search_confluence, nor any mention of prerequisites or exclusions. The context of sibling tools exists but the description itself does not point to it.
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.
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- Evaluate tool definition quality.
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