Satori Syntax MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have completely distinct purposes: one generates content (generate_satori_syntax) and the other retrieves metadata about available structure types (get_satori_structure_types). There is no overlap in functionality, making it impossible for an agent to confuse them.
Naming Consistency5/5Both tools follow a consistent verb_noun naming pattern (generate_satori_syntax, get_satori_structure_types) with clear, descriptive verbs that match their actions. The naming is uniform and predictable across the set.
Tool Count2/5With only 2 tools, the server feels thin for its purpose of generating and managing Satori syntax. A typical syntax generation server might include additional tools for validation, customization, or history management, making this set borderline minimal.
Completeness3/5The server covers core generation and type listing, but there are notable gaps. For example, no tools exist for validating syntax, editing generated content, or managing user preferences, which could limit agent workflows in this domain.
Average 3/5 across 2 of 2 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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions generating syntax for 140-character X posts, which implies a creation operation, but doesn't cover aspects like whether this is a read-only or mutative action, any rate limits, authentication needs, or what the output format looks like (e.g., text string). This is a significant gap for a tool with no annotation coverage.
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 in Japanese that directly states the tool's purpose and constraint (140 characters). It is front-loaded with no unnecessary words, making it highly 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.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a generation tool with 5 parameters and no annotations or output schema, the description is incomplete. It doesn't explain the output (e.g., what the generated syntax looks like), behavioral traits like mutability or side effects, or usage context. This leaves gaps that could hinder an AI agent in correctly invoking the tool.
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 schema description coverage is 100%, with detailed descriptions for all parameters, including an enum for 'structure_type' with clear mappings. The description adds no additional parameter semantics beyond what the schema provides, such as examples or usage tips. Given the high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.
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 tool's purpose: 'generate Satori syntax (for 140-character X posts).' It specifies the verb ('generate') and resource ('Satori syntax'), and includes the constraint of 140 characters. However, it doesn't differentiate from its sibling tool 'get_satori_structure_types,' which likely provides structure types rather than generating syntax, so it misses full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool or any other context for usage, such as prerequisites or scenarios where this tool is preferred over others. This leaves the agent with no explicit or implied usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves a list but doesn't describe any behavioral traits such as whether it's read-only, if it requires authentication, rate limits, error handling, or what the return format looks like (e.g., JSON array, plain text). This leaves significant gaps for an agent to understand how to invoke it correctly.
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 in Japanese that directly states the purpose without any wasted words. It's appropriately sized for a simple tool with no parameters, and the information is front-loaded with the core action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations, no output schema, and 0 parameters, the description is minimal. While it states the purpose clearly, it lacks essential contextual details for a tool that retrieves data: no information on return format, error conditions, or behavioral constraints. For a retrieval tool, this leaves the agent with insufficient guidance on what to expect from the invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't mention parameters, which is correct for a parameterless tool. It adds value by specifying what is being retrieved (syntax types for Satori syntax), which goes beyond the empty schema.
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 ('取得します' - get/retrieve) and the resource ('さとり構文の利用可能な構文タイプ一覧' - list of available syntax types for Satori syntax). It distinguishes from the sibling tool 'generate_satori_syntax' by focusing on listing types rather than generating syntax. However, it doesn't specify the format or scope of the list (e.g., all types, filtered, paginated).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives is provided. The description implies this is for retrieving available syntax types, but there's no mention of prerequisites, when not to use it, or how it relates to the sibling 'generate_satori_syntax' tool. Usage is implied from the purpose alone.
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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