StarRocks MCP Server
Server Quality Checklist
Latest release: v2.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.
Naming Consistency5/5A single tool inherently follows a consistent naming pattern. The tool name 'get_query_profile' uses a clear verb_noun structure, which is appropriate and consistent within this minimal set.
Tool Count2/5A single tool is too few for a server named 'StarRocks MCP Server', which suggests a database or query system. This minimal toolset severely limits functionality and likely leaves many essential operations (e.g., query execution, data management, monitoring) uncovered.
Completeness1/5The server is severely incomplete. For a database/query system, only having a tool to get query profiles leaves massive gaps in core operations like running queries, managing data, or accessing system metrics. This toolset does not support basic workflows.
Average 3.2/5 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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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the tool saves the profile to a local file and returns summary information, which are important behavioral traits beyond basic functionality. However, it doesn't cover aspects like error handling, performance characteristics, or whether this operation is read-only or has side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that each serve a purpose: the first states the core functionality, the second explains the output's utility. There's no wasted verbiage, though it could be slightly more front-loaded with the most critical information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations, no output schema, and moderate complexity (it performs file operations and returns summary information), the description provides basic completeness but lacks details about the return format, file location, or error conditions. It's adequate for understanding what the tool does but leaves gaps for practical implementation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the query_id parameter fully documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema. According to the rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '获取指定 Query ID 的执行 Profile' (get the execution profile for a specified Query ID). It specifies the action (获取/get) and resource (执行 Profile/execution profile) with the query_id parameter. However, without sibling tools for comparison, it cannot demonstrate differentiation, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 mentions that the profile file can be used for subsequent detailed analysis, but this is a feature explanation rather than usage context. There are no explicit when/when-not instructions or named alternatives.
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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