xmind-mcp
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: parse the entire document, search nodes by keyword, and extract a specific branch. No ambiguity between these operations.
Naming Consistency5/5All tool names follow the consistent verb_noun snake_case pattern (parse_xmind, search_xmind_nodes, get_xmind_node_branch), making the API predictable.
Tool Count5/5Three tools is a well-scoped count for an XMind reading/exploration server. Each tool serves a distinct need without redundancy.
Completeness5/5The tool set covers the full read-side lifecycle: full-document parsing, keyword search, and subtree extraction. There are no obvious gaps for the apparent purpose of exploring XMind files.
Average 4/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 9 commits 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.
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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?
With no annotations, the description must convey behavior. It discloses that the tool extracts a subtree up to a specified depth and mentions 'token overload' indicating output size considerations. However, it does not explicitly state whether the operation is read-only, how missing nodes are handled, or the output format. The term 'extract' implies a read operation, but more detail is needed for full transparency.
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?
The description is two sentences long, front-loaded with the action and then a brief use case. Every word serves a purpose; no unneeded filler or repetition.
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?
The tool has 3 parameters and no output schema or annotations. The description explains purpose and use case but does not describe the return format or error behavior. While the purpose is clear, the lack of output details leaves the agent with uncertainty about what to expect, especially for a tool with no schema-based return structure.
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 parameters are already well-documented (path, depth, nodeId). The tool description adds minimal parameter meaning beyond the schema, only referencing 'specific node' and 'specified depth' which are already in the parameter descriptions. With full schema coverage, a baseline score of 3 is appropriate.
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 states a specific action: 'Extract a specific node and its subtree (up to specified depth) from an XMind file.' This clearly identifies the tool's function and distinguishes it from siblings (parse_xmind, search_xmind_nodes) by emphasizing targeted branch extraction rather than full parsing or node search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: 'Useful for large mind maps to avoid token overload by retrieving only relevant branches.' This implies when to use it (large maps where you want to avoid excessive output) but does not explicitly name alternative tools or state when not to use it. The schema's nodeId description suggests using search_xmind_nodes/parse_xmind to get IDs, but this is not in the tool description itself.
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?
Since no annotations are provided, the description must address behavior. It discloses support for both Zen and Legacy formats and explains differences between Markdown and JSON outputs (token efficiency, structure summary, metadata). This adds useful context beyond a minimal 'parse' statement, though it stops short of detailing error cases or side effects.
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?
The description is three sentences, front-loaded with the primary action. Each sentence adds value: purpose, format guidance, and compatibility. No redundant or filler content.
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?
For a simple tool with two parameters and no output schema, the description sufficiently covers the primary action, output options with recommendations, and file format compatibility. It does not detail error handling or file size limits, but that depth is not necessary at this level of complexity.
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 coverage is 100% and both parameters are already well-described in the schema. The description repeats the format recommendation and mentions JSON contents, but it does not add new parameter-level semantics beyond the schema. Therefore the baseline score of 3 is appropriate.
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 clearly identifies the tool's purpose: 'Parse an entire XMind document (.xmind file) and return formatted output (Markdown or JSON).' The verb 'parse' and resource 'XMind document' are specific, and the word 'entire' distinguishes it from sibling tools like search_xmind_nodes or get_xmind_node_branch.
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?
The description offers guidance on choosing output format ('Markdown format is more token-efficient and recommended for large documents'), but it does not explicitly state when to use this tool versus the sibling tools. The 'entire' qualifier implies for full-document processing, but there is no direct comparison or exclusion.
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 burden and provides useful behavioral details: returns matching nodes with breadcrumb paths, and avoids loading the entire document. It lacks explicit read-only confirmation but the action is inherently non-destructive.
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?
Three clean sentences: action+criteria, return format, and use case. Front-loaded, concise, and every sentence adds value.
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?
The description explains purpose, return format, and context without an output schema. It could mention edge cases like empty results or invalid files, but for a search tool it is reasonably 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 baseline is 3. The description's mention of 'title/notes' and 'label' loosely maps to the searchIn parameter but does not add material semantics beyond the schema's per-parameter descriptions.
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 clearly states the action ('Search'), resource ('nodes in an XMind file'), and criteria ('by keyword (title/notes) or label'). It also distinguishes from sibling tools by emphasizing search over full parse or branch retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear use context: 'Useful for large mind maps to find specific topics without loading entire document.' This implies when to use it, though it does not explicitly name alternatives or state when not to 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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