Skip to main content
Glama
UtakataKyosui

PR Review MCP Server

by UtakataKyosui
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation3/5

    The tools have overlapping purposes that could cause confusion. 'reply_and_resolve' combines the functionality of 'reply_to_review_thread' and 'resolve_review_thread', making it ambiguous when to use the combined tool versus the individual ones. However, the descriptions clarify the distinctions, so agents can differentiate with careful reading.

    Naming Consistency5/5

    All tool names follow a consistent snake_case pattern with clear verb_noun structure (e.g., list_review_threads, reply_to_review_thread). The naming is predictable and readable throughout the set, with no deviations in style or convention.

    Tool Count4/5

    With 4 tools, the count is reasonable for a focused PR review server. It covers core operations like listing, replying, and resolving threads. However, it feels slightly thin, as it lacks tools for creating review threads or handling other review aspects like approvals, which might be expected in this domain.

    Completeness3/5

    The server covers basic operations for managing review threads but has notable gaps. There is no tool to create a new review thread, which is a core function in PR reviews. Additionally, it lacks coverage for other review actions like submitting reviews, approving, or requesting changes, limiting its completeness for the stated purpose.

  • Average 3.2/5 across 4 of 4 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
    • Last stable release on
    • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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

  • 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, but it only repeats the action. It does not mention prerequisites (e.g., thread must be active), side effects, or response 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?

    The description is a single sentence with no redundant content. It is as concise as possible for the core purpose.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (5 required params, no output schema, no annotations), the description is too sparse. It lacks context on return values, error handling, or how to obtain the thread_id (though it references sibling).

    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 fully describes all 5 parameters with clear descriptions, so the description adds no extra meaning. Baseline score of 3 is appropriate as schema carries the load.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Add a reply') and the resource ('review thread'), making the purpose immediately understandable. However, it does not differentiate from the sibling tool 'reply_and_resolve', which also adds a reply.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does 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 its siblings, such as 'reply_and_resolve' or 'resolve_review_thread'. The agent lacks context for appropriate selection.

    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?

    With no annotations, the description carries full burden. It only states the action without disclosing behavioral traits: whether it's reversible, if permissions are needed, or what happens to the thread (e.g., collapsed, status change). This is insufficient for an agent to understand consequences.

    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 a single concise sentence, front-loading the action. However, it is too brief and could benefit from slight expansion without losing conciseness.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    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, no output schema), the description is minimally adequate. However, it lacks behavioral context (e.g., effects, permissions) that would help an agent use it correctly, especially with no annotations.

    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 thread_id clearly described. The description adds no extra meaning beyond the schema, so 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 'Mark a review thread as resolved' clearly states the action (resolve) and resource (review thread). It distinguishes from sibling tools: list_review_threads lists threads, reply_and_resolve both replies and resolves, and reply_to_review_thread only replies.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No usage guidance is provided. The description does not indicate when to use this tool vs alternatives, nor does it mention any prerequisites or conditions like requiring ownership or resolved state.

    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 present, so the description must convey behavioral traits. It only states the action without mentioning that it is a read-only operation, or any details on filtering (e.g., unresolved_only default) or pagination. The description is too minimal for a tool with no annotations.

    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 a single, clear sentence with no waste. However, it could be slightly expanded to include key behavioral details without losing conciseness. It is appropriately front-loaded but leans toward under-specification.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    There is no output schema, so the description should explain what the tool returns (e.g., thread details, line numbers, comments). It does not, leaving the agent uninformed about the output structure. For a listing tool, this is a significant gap.

    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 descriptions for all 4 parameters. The description adds no additional meaning beyond confirming the action. Baseline 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.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb 'List' and the resource 'review threads' scoped to a GitHub pull request. It distinguishes from sibling tools (reply_to_review_thread, resolve_review_thread) which focus on actions on threads, 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 Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No explicit when-to-use or when-not-to-use guidance is provided. The description implies usage when needing to see existing review threads, but it does not mention alternatives or context compared to sibling tools. Basic implied usage exists.

    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 full burden. It discloses the combined behavior (reply and resolve) but does not mention any side effects, such as whether the thread must be unresolved, what happens on failure, or any atomicity guarantees. More behavioral context would be helpful.

    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 a single, front-loaded sentence that clearly states the tool's purpose without unnecessary words. It is concise and understandable, though it could be slightly more structured with bullet points or additional context for completeness.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given that there are 5 required parameters and no output schema or annotations, the description is insufficiently complete. It does not explain the resolve aspect in detail, such as whether the session's reviewer is the one who resolves, or any preconditions. For a combined operation, more context is needed.

    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 covers all 5 parameters with descriptions, so schema coverage is 100%. The description adds no extra meaning to the parameters beyond what is in the schema. Baseline score of 3 is appropriate as the description does not enhance parameter understanding.

    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 does two things: reply to a review thread and resolve it. The verb 'Reply and resolve' combined with the resource 'review thread' makes the purpose unambiguous. It distinguishes itself from sibling tools like 'reply_to_review_thread' and 'resolve_review_thread' by offering a combined action.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies when to use this tool: when you want to both reply and resolve in one step. However, it does not explicitly state when NOT to use it, nor does it compare with the separate reply or resolve tools. No guidance on prerequisites or context is provided.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

PR-Review-Resolve-MCP MCP server

Copy to your README.md:

Score Badge

PR-Review-Resolve-MCP MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/UtakataKyosui/PR-Review-Resolve-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server