Eventfinda MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and distinct by default.
Naming Consistency5/5The single tool follows a consistent verb_noun pattern (list_events), and with no other tools, there is no inconsistency in naming conventions. The naming is straightforward and predictable.
Tool Count2/5A single tool is too few for a server named 'Eventfinda MCP', which implies a broader scope for event discovery and management. This minimal toolset likely leaves significant gaps in functionality, such as searching, filtering, or accessing event details beyond this week.
Completeness2/5The tool surface is severely incomplete for an event-finding domain. It only lists events for this week in a given location, missing essential operations like searching events by date range, category, or keyword, getting event details, or managing user interactions.
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 is passing
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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?
No annotations are provided, so the description carries full burden. It states 'List all events,' implying a read-only operation, but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, error handling, or what 'all events' entails (e.g., completeness guarantees). It adds minimal context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose ('List all events') and specifies constraints ('within this week for a given location'). There is zero waste, and every word earns its place, making it highly concise and well-structured.
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 no annotations, no output schema, and a simple single-parameter tool, the description is adequate but has gaps. It covers the basic what and scope but lacks details on behavior, output format, or error cases. For a read operation with low complexity, it's minimally viable but not fully complete.
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?
Schema description coverage is 100%, with the parameter 'location' fully documented in the schema. The description adds no additional meaning about the parameter beyond implying it's used to filter events. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
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 action ('List all events') and resource ('events'), specifying scope ('within this week for a given location'). It's specific about time frame and location filtering. However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating 'within this week for a given location,' suggesting it's for current-week events at specific locations. However, it lacks explicit guidance on when not to use this tool or alternatives, and no prerequisites are mentioned. With no sibling tools, it cannot provide comparative guidance.
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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