MCP Live Events Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as retrieving upcoming music events for a specified city, making it distinct by default.
Naming Consistency5/5The single tool name 'get_upcoming_events' follows a clear verb_noun pattern (get + upcoming_events), which is consistent within this minimal set. There are no other tools to compare against, so no inconsistency exists.
Tool Count2/5A single tool is too few for a server named 'MCP Live Events Server', which suggests a broader domain of live events management. This minimal toolset feels thin and incomplete for handling events beyond basic retrieval, such as creating, updating, or deleting events.
Completeness2/5The tool surface is severely incomplete for the implied domain of live events. While 'get_upcoming_events' allows retrieval, there are significant gaps: no tools for creating events, updating event details, deleting events, or managing other aspects like tickets or venues, which will likely cause agent failures in comprehensive workflows.
Average 3.3/5 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
- Behavior2/5
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 implies a read-only operation ('Get') but lacks details on permissions, rate limits, pagination, error handling, or response format. This leaves significant gaps for a tool with 4 parameters and no output schema.
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 sized and front-loaded with the core purpose in the first sentence. The parameter documentation is structured with bullet points, though the repetition of 'Start date/time' for 'end_dttm_str' is slightly misleading. Overall, it's efficient with minimal waste.
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's moderate complexity (4 parameters, no output schema, no annotations), the description is partially complete. It excels in parameter semantics but lacks behavioral context, usage guidelines, and output details. This results in an adequate but gap-filled description for an agent to use effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It explicitly documents all 4 parameters with clear semantics: 'city' for location, 'start_dttm_str' and 'end_dttm_str' for date/time range in ISO 8601 format with examples, and 'keyword' as optional for filtering. This adds substantial value beyond the bare schema.
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: 'Get upcoming music events for a city.' It specifies the verb ('Get'), resource ('upcoming music events'), and scope ('for a city'). However, since there are no sibling tools, it cannot differentiate from alternatives, preventing a score of 5.
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, prerequisites, or exclusions. It only lists parameters without context about appropriate scenarios or constraints, offering minimal usage direction.
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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