MindManager MCP Server

by robertZaufall

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but there is some overlap between 'get_mindmap' and 'serialize_current_mindmap_to_json' as both retrieve mindmap data in structured formats. The three serialization tools (to JSON, Markdown, Mermaid) are clearly differentiated by output format, and creation vs. retrieval tools are well separated.
Naming Consistency5/5
All tools follow a consistent snake_case verb_noun pattern with clear action prefixes (create_, get_, serialize_). The naming is highly predictable, with 'serialize_current_mindmap_to_*' showing excellent consistency for the serialization family.
Tool Count5/5
11 tools is well-scoped for a MindManager automation server. The set covers creation (2 tools), retrieval (4 tools), serialization (3 tools), and utility functions (2 tools) without being overwhelming. Each tool appears to serve a distinct role in the mindmap workflow.
Completeness4/5
The server provides comprehensive coverage for mindmap creation, retrieval, and serialization workflows. Minor gaps exist in update/delete operations for mindmap content, but the core functionality for importing, exporting, and inspecting mindmaps is well covered. The inclusion of both simple and advanced creation tools shows thoughtful design.
Average 3.4/5 across 11 of 11 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
- Last stable release on November 16, 2025
- 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.
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but provides minimal behavioral insight. It mentions retrieval and returns a structure or error, but lacks details on permissions, rate limits, or side effects. This is inadequate for a tool with no annotation coverage.
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 well-structured with clear sections for Args and Returns, using bullet-like formatting. Every sentence adds value, and it's front-loaded with the core purpose, making it efficient and easy to parse.
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 2 parameters with 0% schema coverage and an output schema exists, the description partially compensates by explaining parameters and return type. However, for a retrieval tool with no annotations, it should provide more behavioral context (e.g., read-only nature, potential errors) to be fully 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?
Schema description coverage is 0%, so the description must compensate. It explains the two parameters ('mode' with detail levels and 'turbo_mode' as text-only), adding meaning beyond the schema. However, it doesn't fully detail how these parameters affect the output, leaving some gaps.
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 verb ('Retrieves') and resource ('current mind map structure from MindManager'), making the purpose unambiguous. It distinguishes from siblings like 'serialize_current_mindmap_to_json' by focusing on retrieval rather than serialization, though it doesn't explicitly compare to all siblings.
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 is provided on when to use this tool versus alternatives like 'serialize_current_mindmap_to_json' or 'get_selection'. The description only states what it does without context for selection among similar tools.
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 provided, the description carries the full burden of behavioral disclosure. It mentions the return type ('Mermaid formatted string or error dictionary'), which is helpful, but lacks other critical information: it doesn't specify whether this is a read-only operation (though implied by 'serializes'), what happens if no mindmap is loaded, potential side effects, or performance characteristics. The description adds some value but leaves significant gaps for a tool with mutation implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, Args, Returns) and front-loaded the core functionality. Each sentence earns its place: the first states the purpose, and the subsequent sections efficiently document parameters and returns. It could be slightly more concise by integrating parameter defaults more seamlessly, but overall it's appropriately sized without 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 the tool's moderate complexity (3 parameters, no annotations, but has output schema), the description is reasonably complete. It covers the purpose, parameters, and return values. The output schema existence means the description doesn't need to detail return structures, which it acknowledges. However, it lacks context about the 'currently loaded mindmap' state dependency and doesn't reference sibling tools, leaving some gaps in full contextual understanding.
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 description provides detailed parameter information in the 'Args' section, explaining the purpose of each parameter: 'id_only' controls ID inclusion, 'mode' sets detail level with specific options, and 'turbo_mode' enables text-only output. With 0% schema description coverage (schema only has titles like 'Id Only'), this compensates well by adding meaningful semantics beyond the bare schema. However, it doesn't fully explain interactions between parameters (e.g., how 'turbo_mode' relates to 'mode').
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's purpose: 'Serializes the currently loaded mindmap to Mermaid format.' This is a specific verb ('serializes') and resource ('currently loaded mindmap') with a clear output format ('Mermaid format'). It distinguishes from siblings like 'serialize_current_mindmap_to_json' and 'serialize_current_mindmap_to_markdown' by specifying the target format. However, it doesn't explicitly contrast with these siblings in the description text.
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. It doesn't mention sibling tools like 'serialize_current_mindmap_to_json' or 'serialize_current_mindmap_to_markdown' to help the agent choose between different serialization formats. There's also no context about prerequisites (e.g., whether a mindmap must be loaded first) or typical use cases.
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 provided, the description carries full burden. It implies a write operation ('creates') but doesn't disclose behavioral traits like required permissions, whether it overwrites existing mindmaps, error handling, or performance characteristics. The 'Guidance for callers' section focuses on input syntax rather than tool behavior, leaving significant gaps for a mutation 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 appropriately front-loaded with the core purpose, but the 'Guidance for callers' section is lengthy and could be more structured. While detailed, some information (like the full icon list) might be excessive for a description. It's comprehensive but not optimally concise, with room for better organization.
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 the complexity of the tool (single parameter but with intricate syntax), the description provides substantial context for the input. With an output schema present, it doesn't need to explain return values. However, for a mutation tool with no annotations, it lacks behavioral context about side effects, error conditions, and integration considerations.
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?
The schema description coverage is 0%, so the description must fully compensate. It provides extensive semantic details for the single parameter 'mermaid', including syntax examples, metadata structure, and a comprehensive list of icon options. This goes far beyond what the bare schema offers, making the parameter fully understandable for implementation.
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 'deserializes a Mermaid mindmap and creates a MindManager mindmap from it', which is a specific verb+resource combination. It distinguishes from the sibling 'create_mindmap_from_mermaid_simple' by implying this version handles more complex metadata, though not explicitly stated. The purpose is clear but could be more explicit about 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 explicit guidance on when to use this tool versus alternatives like 'create_mindmap_from_mermaid_simple' or other serialization tools. It mentions 'caller must follow the guidance', but this refers to input construction, not tool selection. There is no context on prerequisites, error conditions, or comparative use cases.
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 provided, the description carries the full burden of behavioral disclosure. It states the tool 'Gets' information (implying a read-only operation) and mentions the return type, but lacks details on potential errors, performance characteristics, or system dependencies. For a tool with zero annotation coverage, this is insufficient to fully inform the agent about its behavior.
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 extremely concise and well-structured: one sentence states the purpose, and a second sentence clarifies the return type. Every word earns its place with no wasted text, making it easy for an agent to parse quickly.
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 the tool's simplicity (0 parameters, output schema provided), the description is reasonably complete. It explains what the tool does and the return format. However, it could be more complete by including error handling details or usage context, especially since no annotations are present to fill those gaps.
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 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to add parameter semantics, so it appropriately focuses on the return value. A baseline of 4 is given as it avoids redundancy while clearly stating the output.
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's purpose with a specific verb ('Gets') and resource ('the path to the MindManager library folder'), making it immediately understandable. However, it doesn't explicitly differentiate this tool from its siblings (like get_mindmanager_version or get_grounding_information), which are also read-only information retrieval tools but for different resources.
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. It doesn't mention any prerequisites, context for usage, or comparisons with sibling tools (e.g., when to retrieve the library folder path versus other MindManager information). This leaves the agent without direction on appropriate use cases.
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?
No annotations are provided, so the description carries the full burden. It discloses that the tool extracts information (a read operation) and hints at behavioral traits like different modes and turbo mode, but lacks details on permissions, error conditions, or side effects. It doesn't contradict annotations, but for a tool with no annotations, more behavioral context would be beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose followed by parameter and return details. Each sentence adds value, with no wasted words. The structure is clear, though the return type explanation could be slightly more concise.
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 the tool's moderate complexity, no annotations, and an output schema that documents return types, the description is fairly complete. It covers purpose, parameters, and returns adequately. However, it lacks context on prerequisites (e.g., needing an open mindmap) and doesn't fully explain sibling differentiation, leaving minor gaps.
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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains the parameters 'mode' and 'turbo_mode' with details on allowed values ('full', 'content', 'text') and effects ('text only'), and specifies defaults. This compensates well for the schema's lack of descriptions, though it could clarify the exact differences between modes.
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's purpose: 'Extracts grounding information (central topic, selected subtopics) from the mindmap.' It specifies the verb ('extracts'), resource ('grounding information'), and source ('mindmap'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from siblings like 'get_mindmap' or 'get_selection', which might also retrieve mindmap data.
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. It doesn't mention sibling tools such as 'get_mindmap' or 'get_selection', nor does it specify prerequisites like requiring an open mindmap. Usage is implied through the action of extraction, but no explicit context or exclusions are provided.
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 provided, the description carries full burden but offers minimal behavioral insight. It mentions the tool serializes a 'currently loaded mindmap' (implying state dependency) but doesn't cover error conditions, performance implications, or what 'turbo mode' entails beyond 'text only'. More context on these aspects would improve transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by parameter and return details. It uses bullet-like formatting for clarity without unnecessary verbosity, though the 'Args' and 'Returns' sections could be integrated more seamlessly.
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 tool's moderate complexity (3 parameters, state dependency) and no annotations, the description is partially complete. It covers parameters and return types (aided by an output schema), but lacks usage context, error handling details, and behavioral nuances like how 'mode' and 'turbo_mode' interact, leaving gaps for an agent.
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 description adds meaningful semantics beyond the input schema, which has 0% coverage (no descriptions for parameters). It explains 'include_notes' controls note inclusion, 'mode' defines detail levels with examples ('full', 'content', 'text'), and 'turbo_mode' enables text-only output. This compensates well for the schema's lack of documentation.
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 specific action ('serializes') and resource ('currently loaded mindmap') with the output format ('to Markdown format'). It distinguishes from sibling tools like 'serialize_current_mindmap_to_json' and 'serialize_current_mindmap_to_mermaid' by specifying the target format.
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 is provided on when to use this tool versus alternatives like the JSON or Mermaid serialization siblings. The description lacks context about prerequisites (e.g., needing a loaded mindmap) or scenarios favoring Markdown output over other formats.
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 provided, the description carries the full burden of behavioral disclosure. It mentions the return type (version string or error dictionary), which adds some context beyond the basic purpose. However, it does not cover other behavioral aspects like error conditions, performance, or dependencies, leaving gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and well-structured: a single sentence states the purpose, followed by a clear 'Returns' section. Every sentence adds value without waste, making it easy to parse and understand quickly.
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 the tool's simplicity (0 parameters, output schema exists), the description is complete enough for its purpose. It explains what the tool does and the return format, though it could benefit from more behavioral context (e.g., error handling). The output schema reduces the need for detailed return value explanations.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately avoids redundant parameter details, earning a high score for not cluttering with unnecessary information.
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's purpose with a specific verb ('Gets') and resource ('version of the MindManager application'), making it immediately understandable. However, it does not explicitly differentiate from sibling tools like 'get_versions', which might serve a similar purpose, preventing a perfect score.
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, such as 'get_versions' or other sibling tools. It lacks context on prerequisites, timing, or exclusions, leaving the agent without 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?
No annotations are provided, so the description carries the full burden. It discloses the return type ('Dict[str, str]') and that it fetches versions, implying a read-only operation, but lacks details on permissions, rate limits, or error handling. This is a minimal but adequate disclosure for a simple read tool.
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 front-loaded with the purpose in the first sentence and efficiently adds return details in the second. Every sentence earns its place, making it appropriately sized and structured without waste.
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 the tool's simplicity (0 parameters, output schema exists), the description is complete enough. It states the purpose and return type, and with an output schema, it needn't explain return values further. However, it could benefit from more context on usage versus siblings.
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 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add param info, but with no params, a baseline of 4 is appropriate as there's nothing to compensate for.
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's purpose: 'Get the versions of the MindManager Automation MCP Server components.' It specifies the verb ('Get') and resource ('versions'), though it doesn't explicitly differentiate from sibling tools like 'get_mindmanager_version' or 'get_grounding_information', which may have overlapping or related 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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_mindmanager_version' or explain the context for retrieving component versions versus other version-related or informational tools, leaving the agent without usage context.
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 provided, the description carries full burden but offers limited behavioral insight. It mentions 'turbo mode (text-only operations)', which hints at performance or mode differences, but does not disclose critical traits like permissions needed, side effects (e.g., file creation), error handling, or rate limits. For a tool that likely creates resources, this is insufficient transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by structured sections for Args and Returns. Each sentence earns its place by defining parameters and output without redundancy. It is appropriately sized and well-organized for quick comprehension.
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 the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is reasonably complete. It covers the purpose, parameters, and return type. The output schema exists, so detailed return values aren't needed. However, it lacks context on behavioral aspects like error cases or integration with siblings, leaving minor gaps.
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?
Schema description coverage is 0%, so the description must compensate. It adds meaningful context for both parameters: 'mermaid' is explained as 'Mermaid text describing the desired mindmap', and 'turbo_mode' is clarified as 'Enable turbo mode (text-only operations)' with a default. This provides semantic understanding beyond the bare schema, though it could detail Mermaid syntax or turbo mode implications further.
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 specific action ('Deserializes... and creates') and resource ('Mermaid mindmap... MindManager mindmap'), distinguishing it from siblings like 'create_mindmap_from_mermaid' by specifying 'simplified syntax'. It provides a precise verb+resource combination that differentiates its scope.
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 lacks explicit guidance on when to use this tool versus alternatives. While it implies usage for Mermaid mindmaps in simplified syntax, it does not specify when to choose this over 'create_mindmap_from_mermaid' or other serialization tools, nor does it mention prerequisites or exclusions. No contextual usage advice is provided.
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 provided, the description carries the full burden of behavioral disclosure. It describes the return type (list of serialized topics or error dictionary) and implies read-only behavior through 'Retrieves', but lacks details on permissions, error conditions, or side effects. This is adequate for a read operation but misses context like what happens if nothing is selected.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized, with a clear purpose statement followed by Args and Returns sections. Every sentence adds value, though it could be slightly more concise by integrating the purpose with parameter explanations.
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 the tool's moderate complexity (2 parameters, read-only operation) and the presence of an output schema (which covers return values), the description is reasonably complete. It explains parameters thoroughly and states the return type, though it could benefit from more behavioral context (e.g., error scenarios).
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?
The description adds significant value beyond the input schema, which has 0% description coverage. It explains the meaning of 'mode' with detail levels ('full', 'content', 'text') and clarifies that 'turbo_mode' enables text-only output, providing essential context not present in the schema's generic titles ('Mode', 'Turbo Mode').
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's purpose with a specific verb ('Retrieves') and resource ('currently selected topics in MindManager'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_mindmap' or 'serialize_current_mindmap_to_json', which might also retrieve MindManager data but with different scopes or formats.
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. It doesn't mention prerequisites (e.g., requires an open MindManager document), exclusions, or compare it to sibling tools like 'get_mindmap' or serialization tools, leaving the agent to infer usage context from the tool name alone.
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 provided, the description carries full burden. It describes the core behavior (serialization to JSON) and mentions error handling in the return type, but lacks details about permissions, rate limits, or what happens if no mindmap is loaded. The description doesn't contradict any annotations.
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 efficiently structured with a clear purpose statement followed by Args and Returns sections. Every sentence adds value: the first establishes core functionality, parameter explanations clarify usage, and return statement manages expectations.
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 3 parameters with no schema descriptions and an output schema present, the description provides good parameter semantics and mentions return types. It could be more complete by explaining what 'currently loaded' means or error conditions, but covers the essentials for a serialization 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?
With 0% schema description coverage, the description compensates by explaining all 3 parameters: 'ignore_rtf' controls RTF content handling, 'mode' specifies detail level with enum values, and 'turbo_mode' enables text-only mode. This adds meaningful context beyond 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 specific action ('serializes'), target resource ('currently loaded mindmap'), and output format ('detailed JSON object with ID mapping'). It distinguishes from siblings like 'serialize_current_mindmap_to_markdown' and 'serialize_current_mindmap_to_mermaid' by specifying JSON output.
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 when needing JSON serialization of a loaded mindmap, but doesn't explicitly state when to use this tool versus alternatives like markdown or mermaid serialization. No explicit exclusions or prerequisites are mentioned.
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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