FlowNoter MCP 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 has a single, clear purpose: saving conversation messages as markdown notes.
Naming Consistency5/5Since there is only one tool, naming consistency is inherently perfect. The tool name 'save_conversation_note' follows a clear verb_noun pattern, but with no other tools to compare to, it cannot be inconsistent.
Tool Count2/5A single tool is too few for a server named 'FlowNoter MCP Server', which suggests a broader note-taking or conversation management domain. While the tool is useful, the server lacks basic operations like listing, retrieving, updating, or deleting notes, making it feel incomplete and under-scoped.
Completeness2/5The server is severely incomplete for a note-taking domain. It only provides a 'save' operation, missing essential CRUD functionality such as listing existing notes, retrieving note content, updating notes, or deleting notes. This creates significant gaps that will hinder agent workflows.
Average 3.8/5 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
This repository is licensed under ISC 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
- Behavior3/5
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 behaviors: automatic filtering of tool calls/intermediate steps, markdown formatting, and file naming/saving location. However, it omits details like error handling, permissions, or rate limits, which are relevant for a write operation. The description doesn't contradict any annotations (none exist).
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 front-loaded with the core purpose in the first sentence, followed by essential details in a logical flow. Every sentence adds value: saving location, filtering behavior, and parameter guidance. It avoids redundancy and is appropriately sized for the tool's complexity, making it efficient to parse.
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 moderate complexity (3 parameters, write operation) and lack of annotations/output schema, the description provides a solid foundation by covering purpose, behavior, and basic usage. It compensates well for missing structured data, though it could be more complete by addressing potential errors or output details. No sibling tools reduce contextual demands.
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 parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'how many recent messages to include' (hinting at num_messages usage) and reinforcing the filtering behavior for messages. This meets the baseline for high schema coverage without significant enhancement.
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 specific action ('save recent conversation messages as a markdown note'), resource ('notes folder'), and scope ('automatically filters out tool calls and intermediate processing steps'). It distinguishes this tool's purpose with precise details about what content is preserved and where it's saved, making it immediately actionable without ambiguity.
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 ('recent conversation messages') and provides some guidance on parameter usage ('specify how many recent messages to include'), but it lacks explicit when-to-use directives, prerequisites, or comparisons to alternatives. Since no sibling tools are listed, the absence of differentiation is acceptable, but overall guidance remains basic.
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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