Skip to main content
Glama
metacoma

freeplane-mcp

by metacoma
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    export_map and import_map have clearly opposite purposes and there is no overlap. An agent can unambiguously determine which to call based on the direction of data transfer.

    Naming Consistency5/5

    Both tool names follow the same verb_noun pattern: export_map and import_map. The naming is simple, predictable, and perfectly matches the server's purpose.

    Tool Count3/5

    Two tools is a very minimal set, but each covers one half of the map exchange workflow. It feels slightly thin for a general MCP server, though the scope is focused.

    Completeness5/5

    The two tools provide a complete round-trip: export the map as JSON and import it back. For this server's apparent purpose of whole-map synchronization, there are no obvious gaps.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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.

  • 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?

    No annotations are provided, so the description must carry the behavioral burden. It says 'Import', which implies mutation, but it does not disclose whether importing replaces, appends, or merges the current map; it also does not mention prerequisites, response shape, or potential side effects. The description adds only the 'running instance' context.

    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 a single, focused sentence with no filler. The verb, object, format, and destination are all front-loaded, making it instantly scannable by an agent.

    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 tool is overall simple enough to be called correctly from the description plus the comprehensive input schema and presence of an output schema. The main gap is the lack of behavioral context around import side effects, but this is not essential for basic invocation.

    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 has 100% parameter coverage and already explains map_json, insert_mode, and insert_node_id in detail. The description adds little beyond repeating 'canonical JSON', so the schema is doing the heavy lifting. Baseline 3 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 clearly states a specific verb ('Import'), resource ('mind map'), and target ('running Freeplane instance'). It also distinguishes itself from the sibling export_map by making the directionality explicit: this tool brings data in through canonical JSON, while export_map would presumably produce it.

    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 gives clear context that this tool is for injecting a canonical JSON mind map into a live Freeplane instance. It does not explicitly say when to use export_map instead or mention exclusions, but the import/export contrast is strongly inferable from the sibling name and the mention of the canonical JSON format.

    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 provided, the description carries the full behavioral burden and does well by disclosing that the return value is a complete JSON string representation of the map and listing node fields. It implicitly signals a read-only operation through the word 'export' but does not explicitly state that nothing is modified, which is a minor omission.

    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 compact and well-structured: a single purpose-defining first sentence followed by concise details about output shape and optional fields. No redundant or filler content.

    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 is complete for a zero-parameter export tool: it states the action, the object, the output format, and the node shape. An output schema exists to further document return values. It could briefly relate to import_map or confirm non-destructiveness, but nothing critical is missing.

    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 and the schema coverage is 100%, so there is no parameter documentation burden. The description appropriately notes the operation applies to the current map, which supplies the only relevant context.

    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 states a specific verb and resource: "Export the current Freeplane mind map to canonical JSON." This clearly distinguishes it from the sibling import_map, and the following details about JSON structure leave no doubt about what the tool does.

    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 clearly implies when to use this tool (whenever the current mind map needs to be exported as JSON), and the sibling tool name import_map makes the alternative obvious. However, it does not explicitly call out import_map or state conditions for choosing one over the other, so it stops short of full guidance.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

mcp-freeplane MCP server

Copy to your README.md:

Score Badge

mcp-freeplane MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/metacoma/mcp-freeplane'

If you have feedback or need assistance with the MCP directory API, please join our Discord server