pr-mcp-server

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation4/5
The tools have distinct purposes, but the similar names create_pr and create_pr_complete could confuse an agent without careful reading. The generate_* tools are clearly separated as prompt generators, and review is distinct. Detailed descriptions help, but the cognitive load is moderate.
Naming Consistency4/5
Most tools follow a verb_noun pattern in snake_case, e.g., create_pr, generate_pr_title. However, 'review' is a single verb without a noun, and 'suggest_reviewers' uses plural. The pattern is largely consistent with minor deviations.
Tool Count5/5
With 8 tools, the server covers the essential PR creation workflow (branch analysis, title/description generation, review, creation, reviewer suggestion) without being bloated or sparse. The count feels well-scoped for its purpose.
Completeness3/5
The server focuses on creating PRs with AI review, but lacks common PR lifecycle operations like listing, updating, merging, or commenting. For a 'pr-mcp-server', the surface is incomplete beyond the creation workflow, leaving notable gaps.
Average 4.2/5 across 8 of 8 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 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?
No annotations are provided, so the description must fully disclose behavioral traits. It mentions automatic branch analysis but does not specify whether the tool has side effects, required permissions, or the nature of the operation (read/query vs. modification). This is a significant gap for a generation tool.
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 three precise sentences, front-loaded with the primary purpose, followed by combination info and usage hint. No redundant or unnecessary words.
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?
With 5 parameters and no output schema, the description could be more complete. It doesn't explain the output or provide higher-level parameter interaction context. The automatic branch analysis is noted, but missing details like return format or limits.
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%, so the baseline is 3. The description does not add any extra meaning or context for the parameters beyond what is already in the schema. It provides no parameter-level guidance.
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 generates a PR title and description in one operation, combining functionality of two sibling tools. It uses specific verbs and resources, and implicitly distinguishes from siblings by noting the combined operation.
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 explicitly says 'Use this when the user wants both title and description generated together', providing clear usage context. However, it doesn't explicitly mention when not to use or alternatives like individual tools, though the sibling context provides some guidance.
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?
No annotations are provided, so the description carries full burden. It discloses that the tool requires GITHUB_TOKEN, auto-generates fallbacks for omitted parameters, and expects AI-generated content for title/description/review. It also mentions it creates a smart GIF and assigns reviewers. However, it does not describe potential side effects or permission requirements beyond the token, and could be more explicit about mutation behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose, using emojis, bold, all caps, and step-by-step instructions that blend workflow guidance with tool documentation. While detailed, it is not concise and may overwhelm agents with unnecessary formatting and redundant explanations. A more streamlined description would serve better.
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 complexity (12 parameters, no output schema, multiple siblings), the description covers the workflow and parameter usage well. However, it lacks information about the return value or success/failure behavior. The mention of 'creates professional PR' gives some output context, but more detail on what the agent can expect from the call would improve completeness.
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?
With 100% schema coverage, baseline is 3. The description adds significant value by explaining the workflow for key parameters (title, description, aiReviewText) and emphasizing that they should be AI-generated content, not direct tool outputs. It also clarifies defaults and fallbacks for other parameters (draft, language, template, etc.), improving understanding beyond the schema alone.
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 tool creates a 'UNIFIED PR CREATION WORKFLOW' and generates a professional PR with title, description, AI review, etc. It distinguishes itself from sibling tools by positioning as the final step after calling generate_pr_title, generate_pr_description, and review. However, the description is dense and mixes workflow instructions with tool purpose, slightly reducing clarity.
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 outlines a step-by-step workflow: (1) call generate_pr_title, (2) YOU analyze, (3) call generate_pr_description, (4) YOU analyze, (5) call review, (6) YOU analyze, (7) call this tool. It also explains when to use fallbacks if parameters are omitted. This provides clear context and distinguishes from alternative sibling tools like generate_pr_title, etc.
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?
Discloses it automatically analyzes the working directory's Git repository, which is helpful. However, with no annotations, it lacks details on side effects, required permissions, or output format.
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 with no wasted words. Front-loaded with purpose then usage guidance.
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 one optional parameter and no output schema, the description covers essential behavior and usage. Could mention that it requires a Git repo with changes, but not critical.
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% (limit parameter described). Description does not add meaning beyond schema; repeats the limit parameter indirectly but adds no extra semantics.
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 it suggests code reviewers based on Git history of modified files. Differentiates from sibling tools like create_pr or analyze_branch which have distinct purposes.
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 says 'Use this when the user asks who should review the PR or to suggest reviewers.' Provides clear usage context, though does not explicitly mention when not to use it.
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?
Discloses auto-detection behavior but no annotations provided. Does not mention error handling, permissions, or if it is read-only. Moderate transparency.
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?
Three concise sentences, front-loaded with purpose, no unnecessary detail.
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?
Adequate for a simple tool with two optional params. Lacks output description but acceptable given no output schema.
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 covers 100% of parameters with descriptions. Description adds context on auto-detection of baseBranch, enhancing 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?
Clearly states it analyzes differences between current and base branch, with auto-detection. Distinguishes from siblings focused on PR creation.
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 says when to use (analyze branch, check changes). Implies alternatives for PR generation. Could add when not to use.
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?
The description is highly transparent, warning with ⚠️ that the tool only returns a prompt, not a final review. It explains the exact output, the AI's job, and the review format. No annotations exist, so the description fully covers behavioral aspects.
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 fairly long but necessary given the tool's unusual workflow. It front-loads the critical warning and structures the explanation clearly. Slight redundancy could be trimmed, but overall it earns its length.
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?
The description fully explains the tool's purpose, output, and the agent's role, including the expected review format. Despite the lack of an output schema, the description provides complete contextual information for proper usage.
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% for the single parameter 'baseBranch'. The description adds value by mentioning it is auto-detected if not provided, which goes beyond the schema's description. This extra context justifies a score above baseline 3.
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 that the tool returns an analysis prompt for the AI to generate a code review. It mentions using it when the user asks to review code, but does not explicitly differentiate from sibling tools like analyze_branch or create_pr.
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 explicitly says 'Use when user asks to review code or when creating PR with review.' It provides clear context for when to use, though it does not mention when not to use or compare to alternatives.
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?
With no annotations, the description fully discloses the critical behavior: the tool returns a prompt, not a final description, and requires the AI to generate the description from that context. This is transparent and sets proper expectations.
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 front-loaded with the critical warning and is well-structured, but somewhat lengthy. Every sentence is informative, but could be slightly more concise.
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 complexity of a meta-tool and no output schema, the description provides comprehensive context: output format, AI's role, workflow, and references to sibling tools. It is sufficient for effective use.
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%, so the schema already describes parameters. The description adds context that language and template affect the prompt generation, but does not add significant new meaning beyond what is in the schema.
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 returns an analysis prompt for the AI, not a final description. It uses a strong warning and explicitly distinguishes itself from siblings like create_pr and generate_pr_title.
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?
States when to use ('when creating PR or when user asks for PR description') and implies that the AI must process the output rather than returning it. Could be slightly more explicit about when not to use, but overall clear.
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?
With no annotations provided, the description carries full burden. It discloses key behaviors: no AI review, branch analysis, auto-update, automatic reviewer addition, and token requirement. Update behavior could be clearer.
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 well-structured with workflow steps and notes, but somewhat verbose. Every sentence earns its place, though some repetition exists (e.g., 'RECOMMENDED' emphasis).
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 11 parameters and no output schema, the description comprehensively covers the workflow, auto-generation fallback, and sibling distinction. It could explicitly mention what the tool returns (e.g., PR object) but is otherwise complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 11 parameters have schema coverage, but the description adds extensive workflow context (e.g., how to use title/description parameters, guidance on NOT passing tool output directly) that significantly enhances understanding beyond the schema.
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 'Creates a Pull Request on GitHub WITHOUT AI review', uses specific verbs and resource, and explicitly distinguishes from sibling 'create_pr_complete'.
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?
Provides a step-by-step RECOMMENDED WORKFLOW, explicitly tells when to use this tool vs 'create_pr_complete', and advises against passing tool outputs directly.
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?
No annotations provided, so the description carries full burden. It discloses that the tool returns a prompt for analysis, not a final answer, and warns with 'CRITICAL'. It explains the AI must read the context and generate a title, which is transparent about the unusual input-output relationship.
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 lengthy but necessary given the unusual tool behavior. It is front-loaded with a warning and provides clear steps. Could be slightly more concise, but still well-structured.
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 complexity (tool returns a prompt for AI to process), the description is complete. It explains output, AI's role, and how to use the result. No output schema, but return behavior is described clearly.
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?
Only one parameter (maxLength) with 100% schema coverage. The description does not add details about how maxLength affects the prompt or title, but the schema already describes it. Baseline 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 that the tool returns an analysis prompt for the AI to generate a PR title, not a final title. It specifies the verb 'generate' and the resource 'PR title' with an explicit twist, distinguishing it from siblings like create_pr.
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 says 'Use when creating PR or when user asks for a PR title' and instructs the AI to not return the prompt but generate a title. It also references sibling tools: 'should then be passed to create_pr or create_pr_complete tools.'
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

Copy to your README.md:

[![pr-mcp-server MCP server](https://glama.ai/mcp/servers/valentin-harrang/pr-mcp-server/badges/card.svg)](https://glama.ai/mcp/servers/valentin-harrang/pr-mcp-server)

Score Badge

Copy to your README.md:

[![pr-mcp-server MCP server](https://glama.ai/mcp/servers/valentin-harrang/pr-mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/valentin-harrang/pr-mcp-server)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/valentin-harrang/pr-mcp-server'

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