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J-X0

lockwood-group-observability-mcp

by J-X0

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    With only a single tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clear and unambiguous.

    Naming Consistency5/5

    The single tool name 'triage_logs' follows a clear verb_noun pattern in snake_case. Without other tools to compare, there is no inconsistency.

    Tool Count2/5

    The server is named for observability yet exposes only one tool. A single tool is too few for the apparent breadth of an observability platform, making the count inadequate.

    Completeness1/5

    The tool only performs log triage via clustering. Missing are core observability operations such as querying, filtering, retrieving, or alerting, leaving the surface severely incomplete for the domain.

  • Average 3.9/5 across 1 of 1 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
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  • 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?

    No annotations exist, so the description carries the behavioral burden. It usefully discloses that clustering is semantic and that clusters are ordered worst-first, but it does not mention latency budget behavior, error modes, or whether the operation is read-only.

    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 dense sentence followed by a short return-value note. Every phrase adds information, and there is no filler or redundant restating of the tool name.

    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?

    This is a complex clustering tool with no annotations and no output schema, so the description must do more. It conveys the high-level purpose but leaves important context unspecified, such as how a 'window' is determined, whether path and records are alternatives, and what the returned cluster objects look like.

    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 schema already documents path, records, and budget_ms. The description adds no extra meaning about how these parameters relate to the 'window' of logs, so it stays at the baseline 3.

    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 names a specific verb and resource: 'Cluster a window of log records by semantic similarity and rank the clusters for incident triage.' It also adds a concrete output behavior, 'Returns clusters worst-first,' which clearly distinguishes it from generic log-processing tools.

    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 phrase 'for incident triage' provides clear intended context, and with no sibling tools, explicit alternative routing is not required. It could be more explicit about when not to use the tool, but the context is clear enough for an agent to select it.

    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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