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adithyx-0

CodeWeaver

by adithyx-0
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools serve clearly distinct purposes: one generates diagnosis code candidates from a clinical note, while the other retrieves detailed information for a specific ICD-10 code. There is no functional overlap.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern in snake_case: 'code_clinical_note' and 'lookup_icd10_code'. The naming is predictable and clear.

    Tool Count3/5

    With only 2 tools, the server feels thin for a coding/ICD-10 domain. While the two covered operations are useful, the scope is narrow and one could expect additional utilities like searching for codes or exploring categories.

    Completeness4/5

    The server covers the primary workflows: submitting a note for code suggestions and looking up code details. Minor gaps exist, such as no direct search-by-description or category-browsing tool, but the core use cases are addressed.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 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.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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.

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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

  • Behavior4/5

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

    With no annotations, the description carries the full burden and does well: it discloses the scoped data set, data source (CMS FY2026), supplemented inclusion terms, and the unvalidated NLM API fallback under externalSuggestions. It does not detail output statuses, confidence thresholds, or constraint-validation mechanics, though it is substantially transparent.

    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 dense but well-organized: purpose first, then scope, data source, and fallback. All sentences carry information and there is no filler. It is somewhat long, but each section earns its place.

    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?

    Despite high complexity (no annotations, no output schema, fallback API), the description covers purpose, scope, data source, and fallback behavior. However, it omits explanation of the output statuses hinted at by max_results, the confidence threshold, and what 'constraint validation' means, leaving notable gaps for a complex tool.

    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 100%, so the baseline is 3. The description adds little beyond the schema: it refers to 'clinical note' and ranked results but does not elaborate on max_results or note_text format beyond what the schema already contains.

    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 'Accepts a clinical note and returns ranked ICD-10-CM diagnosis code candidates with constraint validation' and clarifies it is not a diagnostic tool. However, it does not explicitly contrast with the sibling lookup_icd10_code, so it misses the differentiation that would merit a 5.

    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 by defining the tool as a coding assistance tool, listing explicit scoped categories, and describing the NLM fallback behavior. It also warns 'not a diagnostic tool,' which provides a when-not. It does not name alternatives or state when to use lookup_icd10_code instead, so it stops short of a 5.

    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?

    The description discloses the data source (CMS FY2026) and scope (cardiac/diabetes/respiratory/musculoskeletal, curated), which are beyond the schema and useful for setting expectations. Since no annotations are provided, the description carries the burden, and it does so well, though it does not mention error behavior or edge cases.

    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 two sentences, front-loaded with the primary purpose and followed by essential data scope information. Every word earns its place, making it concise and well-structured.

    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 (one parameter, no output schema), the description provides sufficient context: it explains what is returned (full detail including inclusion terms and Excludes1/2 relationships) and the data scope. It does not specify return format or error handling, but for a lookup tool with this simplicity, it is reasonably 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?

    The input schema provides a clear description of the parameter 'code' with examples. The tool description adds little beyond the schema's own parameter description, so the baseline of 3 applies here.

    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 tool returns full detail for a single ICD-10-CM code, specifying the resource (ICD-10-CM) and the verb (Returns). It distinguishes from the sibling tool (code_clinical_note) by focusing on code lookup rather than note interaction.

    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 for looking up a single code but does not explicitly state when to use this tool versus code_clinical_note. It lacks explicit alternative names or exclusion criteria, so it only provides implied context.

    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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