Athena MCP
Server Quality Checklist
Latest release: v0.3.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'think' has a clearly defined and distinct purpose focused solely on deep reasoning without any competing alternatives.
Naming Consistency5/5A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare against. The tool name 'think' is straightforward and follows a simple verb pattern without any conflicting conventions.
Tool Count2/5A single tool is generally too few for most server purposes, as it severely limits functionality and scope. While the tool is well-defined for reasoning tasks, the server lacks breadth, making it feel thin and potentially incomplete for broader applications.
Completeness2/5The server is severely incomplete for any domain beyond basic reasoning, as it only offers a single tool for deep thinking without supporting actions like file modification, command execution, or network access. This creates significant gaps that will likely cause agent failures in practical workflows.
Average 4.4/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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- No high-severity vulnerability alerts
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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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: Athena 'only reasons and returns a concise response,' has 'NO tools,' and 'Does not modify files, run commands, or access the network beyond the reasoning call.' It also notes that 'You must gather relevant context yourself.' However, it lacks details on rate limits, authentication needs, or error handling, which would elevate it to a 5.
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 appropriately sized and front-loaded, with the first sentence stating the core purpose. Each subsequent sentence adds critical information without redundancy: usage context, limitations, and parameter guidance. There is zero waste, and the structure flows logically from general to specific details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (reasoning delegation with no output schema) and lack of annotations, the description is mostly complete. It covers purpose, usage, behavioral traits, and some parameter context. However, without an output schema, it doesn't detail return values or error formats, which is a minor gap. For a tool with no annotations and no output schema, this is strong but not perfect.
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%, so the schema already documents all four parameters thoroughly. The description adds minimal value beyond the schema, mentioning the 'context' arg briefly ('You must gather relevant context yourself and pass it in via the `context` arg.') but not explaining other parameters. This meets the baseline of 3 for high schema coverage, as the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Ask Athena to think. Delegates deep reasoning to a stronger model when you hit a hard problem — architecture decisions, subtle bugs, plan critique, tricky logic.' It specifies the verb ('think', 'delegates deep reasoning') and resource ('Athena', 'stronger model'), and distinguishes it from alternatives by noting 'Athena has NO tools; she only reasons and returns a concise response.' With no sibling tools, this level of specificity is excellent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'when you hit a hard problem — architecture decisions, subtle bugs, plan critique, tricky logic.' It also clearly states when not to use it: 'Does not modify files, run commands, or access the network beyond the reasoning call.' With no sibling tools, this covers all necessary usage context, including exclusions and prerequisites like gathering context manually.
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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