Think MCP Tool
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool 'think' has a clearly distinct and singular purpose, making misselection impossible.
Naming Consistency5/5A single tool inherently has perfect naming consistency as there are no other tools to compare against. The name 'think' follows a simple verb pattern, which is appropriate for its function.
Tool Count2/5One tool is too few for a server named 'Think MCP Tool', which suggests a broader scope. A single tool for thinking/logging feels thin and insufficient for typical MCP server purposes, indicating a mismatch with the implied domain.
Completeness1/5The server is severely incomplete; with only a 'think' tool, there are no operations for data retrieval, modification, or interaction with external systems. This leaves significant gaps that will cause agent failures in most practical scenarios.
Average 3.4/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 3 community issues answered or closed 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
This repository is licensed under MIT License.
This repository includes a README.md file.
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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. It clearly discloses behavioral traits: 'It will not obtain new information or change the database, but just append the thought to the log,' indicating it's a non-destructive, logging-only operation. This adds useful context beyond the schema, though it could detail more about the log format or persistence.
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 with three sentences that are front-loaded: the first states the purpose, the second clarifies behavior, and the third provides usage context. There's minimal waste, though it could be slightly more structured for clarity.
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 annotations, but has an output schema), the description is complete enough. It covers purpose, behavior, and usage, and since an output schema exists, it needn't explain return values. However, it could benefit from more detail on the log mechanism or examples.
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 parameter 'thought' well-documented. The description adds no additional parameter semantics beyond what the schema provides, such as format or examples for the thought. Given high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to.
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 'append the thought to the log,' which provides a basic purpose. However, it's vague about what 'think' entails operationally and doesn't distinguish from siblings (though none exist). It avoids tautology by adding context about appending to a log, but lacks specificity in verb+resource clarity.
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 provides implied usage guidelines by stating 'Use it when complex reasoning or some cache memory is needed,' which gives context for when to invoke it. However, it lacks explicit alternatives or exclusions, and since there are no sibling tools, this guidance is minimal but adequate for basic 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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