github-ruleset-mcp
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool targets a distinct operation: applying a ruleset, checking protection status, listing rulesets, deleting a ruleset, and listing templates. The purposes are clearly separated and descriptions reinforce the differences. No two tools appear to perform the same function.
Naming Consistency5/5All tool names follow a consistent verb_noun snake_case pattern (apply_ruleset, check_protection, list_rulesets, delete_ruleset, list_templates). The verbs clearly indicate the action, and there are no stylistic deviations or mixed conventions.
Tool Count5/5With exactly 5 tools, the server is well-scoped for managing GitHub rulesets and branch protection. Each tool serves a distinct purpose without redundancy, and the count is neither too small nor too large for the domain.
Completeness5/5The tool set covers the full lifecycle: apply (create/update), check (read status), list (read all), delete (remove), and list_templates (discover available templates). There are no obvious missing operations for the stated purpose, and agents can accomplish all core ruleset management tasks.
Average 3.6/5 across 5 of 5 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?
With no annotations, the description carries the full burden of behavioral disclosure. It only states the action without indicating return value, error behavior (e.g., branch missing, no protection rules), or read-only nature. This is a significant gap for a tool with no output schema.
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 concise sentence, front-loaded with the verb and resource. It contains zero wasted words and is easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple with well-documented parameters, but the lack of an output schema and behavioral details (e.g., what a positive vs negative result looks like) makes the description incomplete for full context. It is adequate for a minimal check tool but could be richer.
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 descriptions cover 100% of the parameters, so the baseline is 3. The description adds no extra semantic detail beyond what the schema already provides, but it doesn't need to because the schema is already clear.
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 verb 'check' and the resource 'branch protection rules', making the tool's purpose evident. However, it does not explicitly differentiate this from sibling tools like list_rulesets or apply_ruleset, which could also be related to protection concepts, leaving minor ambiguity.
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 guidance is provided on when to use this tool versus the alternatives. There is no mention of scenarios where check_protection is preferred over list_rulesets or apply_ruleset, nor any exclusions or prerequisites.
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?
The tool has no annotations, so the description carries full responsibility for behavioral disclosure. It only says 'Delete' without mentioning that the action is irreversible, may require specific permissions, or could fail if the ruleset is in use. For a destructive operation, this lack of warning is a notable gap.
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, front-loaded sentence that directly states the action and object. It contains no filler words and clearly communicates the tool's primary function without unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with complete parameter coverage and no output schema, so the description doesn't need to explain return values. However, it lacks crucial behavioral context for a delete operation, such as irreversibility or permission requirements. The core action is clear, but the absence of annotations makes the description only partially complete for an agent to use safely.
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 already covers all three parameters (owner, repo, ruleset_id) with descriptions, and schema coverage is 100%. The description adds no extra meaning to the parameters, such as how to obtain the ruleset_id. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
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 uses a specific verb 'Delete' with a clear resource 'a ruleset from a repository' and identifies the lookup key ('by its ID'). This clearly distinguishes it from sibling tools like list_rulesets or apply_ruleset, which serve different actions.
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 through the action 'Delete'—an agent would know to use this when a ruleset needs to be removed. However, it provides no explicit context about when not to use it, prerequisites (e.g., the ruleset must exist), or alternatives (e.g., checking protection first). This is a basic implied-usage case, not a clearly guided one.
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 full burden. It discloses only the basic action and does not mention side effects, permissions, pagination, or return format. This is a thin disclosure for a read operation.
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, front-loaded sentence with no redundant words. It earns its place and communicates the core purpose efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list operation with two fully described parameters, the description is adequate but lacks output schema or behavioral details. It doesn't mention what the returned list contains, pagination, or any access requirements, leaving some context gaps.
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 descriptions cover both parameters (owner and repo) with basic descriptions. The tool description adds no additional meaning beyond that, so it meets the baseline for high schema coverage.
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 uses a specific verb 'List' and resource 'rulesets' with scope 'configured for a repository'. This clearly distinguishes it from siblings like apply_ruleset, delete_ruleset, and list_templates.
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 usage is implied by the action 'List all rulesets', but there is no explicit guidance on when to use this tool versus siblings or any prerequisites. No alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the burden. It discloses the dry_run preview behavior and implies actual application when dry_run is false. However, it does not mention permissions, overwrite behavior, or error outcomes, which leaves potential side effects undisclosed.
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 two concise sentences that immediately convey the action and the dry_run option. There is no wasted phrasing, though the second sentence could be integrated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the core purpose and preview capability, but with no output schema or annotations, it omits details like response format, prerequisites, or whether the ruleset replaces existing ones. Adequate for a straightforward tool but not comprehensive.
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?
All parameter descriptions are already present in the input schema (100% coverage). The description adds no extra parameter semantics beyond what the schema provides, so the baseline score of 3 is appropriate.
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 function: 'Apply a branch protection ruleset to a GitHub repository.' It uses a specific verb and resource, and given sibling tools (list_rulesets, delete_ruleset, check_protection), this tool uniquely performs the apply action.
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 gives a usage hint about dry_run for previewing, but it does not explicitly state when to use this tool versus alternatives like check_protection or list_rulesets. Usage is implied from the verb 'apply' rather than explicitly contrasted with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It uses 'List' which implies read-only, and adds that results include descriptions, but does not explicitly state side effects, permissions, or other behavioral traits.
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?
One sentence, direct, and front-loaded. No unnecessary information.
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?
For a simple 0-parameter list tool, the description adequately explains the purpose and return content. It could mention the specific fields returned or differentiate from list_rulesets, but it's sufficient.
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 tool has zero parameters, so the schema covers everything. Baseline is 4, and the description doesn't need to add parameter semantics.
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 a specific action ('List') on a specific resource ('branch protection templates') and mentions output detail ('with descriptions'), distinguishing it from sibling tools like list_rulesets (rulesets vs templates).
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?
No explicit guidance on when to use this tool versus alternatives like list_rulesets. The usage is implied by the verb and resource, but no context or exclusions are provided.
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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