amendor-mcp

Server Quality Checklist

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Latest release: v1.0.1
Disambiguation4/5
Most tools have distinct purposes, but publish_preview and set_preview_url both mark a change request as ready for review, potentially confusing an agent. start_build is a separate local-mode flow that could be misused if the normal GitHub flow is expected.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., get_change_request, publish_preview, ship_change), making them predictable and easy to understand.
Tool Count5/5
With 7 tools covering listing, detail retrieval, status updates, preview management, and final shipping, the count is well-scoped for managing change requests without excess or deficiency.
Completeness4/5
The tool set covers the main lifecycle but lacks a creation tool, which is acceptable if change requests are always created externally. update_status provides flexibility for status transitions.
Average 4.2/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 12 commits in the last 12 weeks
- Last stable release on June 22, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?
Annotations already declare the tool as read-only, non-destructive, idempotent, and open-world. The description adds the ability to filter by status but does not disclose any additional behavioral traits (e.g., pagination, default sorting, rate limits). The description does not contradict the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only two sentences, each serving a purpose: stating the action and positioning it as the entry point. No redundant fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has an output schema (not shown), so the description does not need to explain return values. The description is sufficient for a list tool with clear annotations, though it could mention that results are paginated or sorted.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides a full description of the status parameter, including the omitted default behavior. The tool description merely repeats 'Optionally filter by status' without adding new meaning. Since schema coverage is 100%, baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists change requests submitted by end users, with optional filtering by status. It also positions itself as the starting point, distinguishing it from sibling tools like get_change_request (single item) and ship_change (mutation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description suggests 'Start here to see what people are asking for,' implying it's the entry point. However, it does not explicitly mention when to use alternative tools (e.g., get_change_request for a specific request) or provide exclusion criteria.
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?
Annotations already declare readOnlyHint=false, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds the context of the 'build and review flow' but does not disclose additional behavioral traits such as possible status transition constraints or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence (12 words) that front-loads the core purpose without any superfluous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple parameters (2), annotations, and existence of an output schema, the description is sufficient to understand the tool's function. Lacks only minor details like possible status transition rules.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for both 'id' and 'status'. The description adds no extra semantic value beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('update') and resource ('change request status') and contextualizes it within the build and review flow. This distinguishes it from sibling tools like 'ship_change' or 'start_build' which imply specific status transitions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage as the status progresses through a workflow but does not explicitly compare to alternatives or specify when this tool should be preferred over sibling tools like 'publish_preview' or 'ship_change'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate idempotent and non-destructive behavior. The description adds that the tool marks the request ready for review, providing context beyond annotations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, concise sentence that front-loads the key action and outcome. No extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two required parameters, annotations, and an output schema, the description adequately covers the purpose and outcome. Minor gap: no mention of return value, but mitigated by existence of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions. The tool description does not add extra meaning beyond what is in the schema, so baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The name and title are clear, and the description precisely states the action: attaching a deploy-preview URL to a change request and marking it ready. It distinguishes well from sibling tools like 'publish_preview' and 'ship_change'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use (when a deploy-preview URL needs to be attached), but does not explicitly state when not to use or mention alternatives among siblings, such as 'publish_preview' or 'update_status'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds value by detailing what data is returned (user request, CSS selector, outerHTML, page URL, screenshot URL), which helps the agent understand the tool's output.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence with a list of returned fields. No unnecessary words; front-loaded with action and purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with high schema coverage, rich annotations, and an output schema, the description is complete. It tells the agent what it returns and when to call it, leaving no gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema's description for 'id' is already informative. The tool description does not add new parameter-specific details beyond the schema, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'get' and the resource 'change request', and lists the exact returned fields: user request, element details, page URL, screenshot URL. It distinguishes from sibling list_change_requests which only lists IDs, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises to call this 'before making the change', giving clear usage context. While it doesn't explicitly list when not to use, the context and sibling tools make alternatives obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description details destructive actions (merge, remove worktree, mark shipped) and states the production effect, going beyond annotations which only provide destructiveHint=true. It adds context about the change's lifecycle but does not specify error cases (e.g., if change not approved).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences, front-loading the main action and providing immediate clarity. Every part is essential with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has a single parameter and an output schema (indicated but not shown), the description sufficiently covers the tool's behavior: what it does, what it affects, and the prerequisite. There are no missing gaps for a simple mutation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already fully describes the single parameter 'id' with the same text as the description ('Change request id to ship (should be approved first).'). The tool description adds no new meaning beyond the schema, resulting in a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's action: 'Ship an approved change: merge the task branch into the project base, remove the worktree, and mark it shipped.' It clearly identifies the resource (change request) and the expected outcome (production reflects the change), differentiating it from sibling tools like get_change_request or list_change_requests.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates the prerequisite that the change must be approved ('Ship an approved change'), guiding when to use it. It does not explicitly list when not to use or alternatives, but the prerequisite and action make usage clear in context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds significant context beyond annotations: explains that the preview is already live, this flips status and surfaces a link. Annotations already indicate idempotent and non-destructive, description enriches with specific behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first states purpose, second provides prerequisite and effect. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given simple tool with one param, output schema present, and annotations covering safety, description fully explains purpose, behavior, and result. Complete for agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage with description for the single parameter 'id'. Description does not add extra meaning beyond the schema, baseline of 3 appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action 'Mark a change request ready for the requester to review' and specifies the effect (flips status to 'in preview' and surfaces link). Distinguishes from siblings like set_preview_url and ship_change by noting the preview is already live.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage after preview is live from worktree but does not explicitly state when not to use or provide alternatives. Clear context for a simple state transition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations: it specifies that the tool modifies local repo state (cuts branch and worktree) and serves a preview at a specific path. It lacks details on potential side effects (e.g., whether it overwrites existing worktrees) but provides useful behavioral scope.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loads the critical note about local-only mode, and separates the alternative flow. It could be slightly more structured (e.g., bullet points) but is efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 param, output schema exists), the description covers purpose, usage, and behavioral context adequately. It doesn't explain prerequisites (e.g., Amendor running) or error cases, but these are minor given the clarity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%; the description reinforces the parameter 'id' by showing its use in the preview path (/preview/<id>/), adding meaning beyond the schema's description. It does not add constraints like allowed formats, but is adequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool starts a local build by cutting a branch and setting up a git worktree served at /preview/<id>/. It explicitly distinguishes from the normal GitHub flow, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool (LOCAL MODE ONLY) and when not to (normal GitHub flow), providing a detailed alternative workflow. This gives clear guidance on selection vs. siblings.
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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