Skip to main content
Glama
lozit

mcp-freestyle

by lozit
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.4

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one returns the most recent glucose reading, while the other returns a historical series over a requested time range. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow the same 'get_' prefix followed by a descriptive noun (current_glucose, glucose_history). The naming pattern is consistent and predictable.

    Tool Count3/5

    With only two tools, the server feels thin, but the domain is narrow (reading glucose data from a sensor). The two tools cover the essential operations, making the count borderline but not unreasonable.

    Completeness4/5

    The server provides access to both current and historical glucose data, which covers the core read-only functionality for the domain. Minor gaps exist (e.g., no health/sensor status), but the available tools are sufficient for the stated purpose.

  • Average 4.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 29 commits in the last 12 weeks
    • Last stable release on
    • 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

  • Behavior5/5

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

    Annotations already mark readOnlyHint=true, but the description adds critical behavioral context: the value is not a live reading, the measured_at timestamp must be used, and the tool is informational only and not for medical treatment decisions. This goes well beyond the structured 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 three sentences, each serving a distinct purpose: stating what the tool does, warning about the timestamp semantics, and adding the safety disclaimer. No redundant or vague wording.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a simple read tool with no output schema, the description covers the essential semantics: the return includes the value and measured_at, the data may be stale, and there is a safety caveat. This is sufficient for an agent to invoke and interpret the result 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?

    The schema fully describes the single 'unit' parameter with enum, default, and meaning ('Storage is always mg/dL; this only affects display'). The description does not add any parameter-specific details, so baseline 3 applies per the rubric.

    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 verb 'Read' and the resource 'most recent glucose measurement from the FreeStyle sensor', with the additional detail 'with the instant it was actually measured'. This distinguishes it from the sibling tool get_glucose_history, which presumably returns historical data.

    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?

    It provides clear context on when to use the tool (to get the latest upstream value) and gives important usage guidance: do not treat it as a live reading and always report measured_at rather than 'now'. However, it does not explicitly mention get_glucose_history or state when not to use this tool, so it lacks explicit exclusions.

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

  • Behavior5/5

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

    The annotation readOnlyHint already signals a safe read, but the description goes further by disclosing the upstream 12-hour limit, the `truncated` flag behavior, and the instruction to report the actually covered range. It also details how collection gaps are handled, which is critical for interpreting percentages. No contradiction with 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 around 120 words but every sentence adds necessary context: purpose, truncation behavior, gap handling, and medical disclaimer. It's front-loaded with the core function and then layers caveats efficiently.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Despite lacking an output schema, the description covers the critical behavioral details: truncation, range reporting, gap handling, and the non-medical nature. It gives enough for the agent to interpret responses correctly, though it doesn't enumerate all return fields.

    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 coverage is 100%, so the baseline is 3. The description adds value by explaining that hours over 12 are ignored and that the response always reports the actual covered range, which affects how the `hours` parameter result should be interpreted. It doesn't add much about `unit`, but the schema already explains display-only conversion.

    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 'Glucose readings over the last N hours (max 12)', clearly specifying the resource (glucose history) and temporal scope. It distinguishes from the sibling 'get_current_glucose' by focusing on historical data and adds the unique capability of time-in-range computation.

    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?

    It implies when to use (when historical readings are needed) without naming the sibling alternative, but explicitly states when not to use it: not for treatment decisions, not as an HbA1c/GMI estimate, and notes the 12-hour limit. This provides clear exclusions, though no named alternatives.

    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-freestyle MCP server

Copy to your README.md:

Score Badge

mcp-freestyle 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/lozit/mcp-freestyle'

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