MCP Think Tool
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap between tools. The tool 'think' has a clearly defined and distinct purpose as described.
Naming Consistency5/5There is only one tool, so naming consistency is inherently perfect. The tool name 'think' follows a simple verb pattern, which is appropriate for its function.
Tool Count2/5A single tool is too few for most practical server purposes, as it severely limits functionality and scope. This feels thin and inadequate for handling complex tasks beyond basic reasoning.
Completeness1/5The tool surface is severely incomplete; with only a 'think' tool, there are no operations for data retrieval, modification, or interaction with external systems, making it impossible to perform meaningful work in most domains.
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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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
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral traits beyond what annotations provide: it explicitly states 'It will not obtain new information or change the database, but just append the thought to the log.' This clarifies that it's a read-only, non-destructive operation with a logging effect. Since no annotations are provided, the description carries the full burden and does so thoroughly by addressing safety and behavior.
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: two sentences that efficiently convey purpose, behavior, and usage guidelines without any wasted words. Every sentence adds value, making it concise and well-structured for quick understanding.
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 low complexity (one parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, behavior, and usage context effectively. However, it lacks details on the log's nature or output format, which could be useful but isn't critical here. Overall, it's sufficient for the tool's scope.
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 description does not add meaning beyond what the input schema provides. The schema has 100% coverage with a clear parameter 'thought' described as 'A thought to think about.' The description mentions 'think about something' but doesn't elaborate on parameter usage or constraints. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool is used 'to think about something' and appends thoughts to a log, which clarifies its basic function. However, it's somewhat vague—'think about something' is abstract, and while it mentions appending to a log, it doesn't specify what kind of log or resource this involves. No sibling tools exist for differentiation, so it avoids being tautological but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use it: 'when complex reasoning or some cache memory is needed.' This gives explicit guidance on appropriate scenarios. However, it doesn't mention when not to use it or alternatives, as there are no sibling tools, so it's not fully comprehensive but still effective.
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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