GitHub PR MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool performs a distinct action on pull requests: listing, retrieving, commenting, reviewing, and merging. There is no overlap in purpose, making it easy for an agent to select the correct tool.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (list_pull_requests, get_pull_request, create_pull_request_comment, merge_pull_request). The naming is uniform and predictable, with only minor singular/plural variations that are appropriate.
Tool Count5/5
With 6 tools, the server is well-scoped for a PR-focused MCP server. Each tool covers a core PR interaction without unnecessary bloat or redundancy.
Completeness4/5
The tool surface covers the main PR lifecycle: list, get, comment, review, and merge. Minor gaps like updating a PR title/body or listing reviews exist, but agents can typically work around them, and the core workflows are well covered.
Average 3.2/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavioral traits. It fails to mention that this is a write operation, potentially destructive, may fail on merge conflicts, or requires specific permissions. The sparse description provides no insight beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, which is structurally clean. However, it is under-specified for a merge operation; while concise, it lacks critical information that would justify the brevity. It is not verbose, but the minimalism is a weakness rather than a strength.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a mutation tool with no annotations and no output schema. The description fails to convey side effects, potential failure modes, return information, or any contextual cues. Given the complexity (5 parameters, destructive action), the completeness is severely lacking.
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 description coverage is 100%, so all five parameters are individually documented in the schema. The description adds no extra semantic meaning about parameter usage or relationships, but the schema already suffices, warranting the 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 clearly states the action ('Mergea un Pull Request') and the target context ('en su rama base'), making the tool's purpose unambiguous. It effectively differentiates from sibling tools (list, get, comment, review) which do not merge.
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 alternatives like create_pull_request_comment or get_pull_request. There is no mention of prerequisites, such as required mergeability checks or whether the PR must be open.
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 provided, the description carries the full burden of behavioral disclosure. It only repeats the basic function and does not mention that it is a read-only operation, pagination behavior, or any other runtime characteristics.
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, front-loaded with the verb 'Lista', and every word contributes to the core purpose. It is appropriately sized for the tool's simplicity.
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?
The description is extremely sparse. While the schema covers parameters well, the lack of an output schema and annotations means the agent receives no information about return format, pagination, or typical usage. For a complete tool description, 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 already describes all parameters with defaults, constraints, and enums, so the description adds no extra meaning. Per the rubric, full schema coverage yields a baseline of 3, which is appropriate here.
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 lists pull requests from a GitHub repository, specifying the verb and resource. However, it does not differentiate from sibling tools like get_pull_request or list_pull_request_comments, so it doesn't fully earn a 5.
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 alternatives. There is no mention of suitable contexts, exclusions, or why one would choose list_pull_requests over get_pull_request or others.
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 carries the full burden of disclosing behavior. It only says 'creates a general comment' but does not mention permissions, idempotency, rate limits, side effects, or response format. This leaves significant behavioral uncertainty for a mutation 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 a single, clear sentence that is front-loaded and free of extraneous content. Every word earns its place, delivering the core purpose without unnecessary detail.
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?
Despite complete parameter documentation, the tool has no annotations and no output schema, leaving the description to provide full operational context. It does not explain when to use the tool, what it returns, or any prerequisites, making it incomplete for an agent to confidently invoke it.
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 complete descriptions for all four parameters (100% coverage), so the description adds little beyond the schema. The qualifier 'general' provides minor context but does not enhance parameter meaning beyond what the schema already conveys. 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 states a specific action ('Crea un comentario general en un Pull Request') with a clear verb and resource, and the word 'general' distinguishes it from line-specific or review comments. This aligns with sibling tools like create_pull_request_review, 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 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 alternatives like create_pull_request_review or list_pull_request_comments. The description lacks any when-to-use or when-not-to-use context.
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?
With no annotations provided, the description bears the full responsibility for behavioral disclosure. It does disclose an important constraint: the body is mandatory for REQUEST_CHANGES and COMMENT events. However, it does not mention permissions, side effects beyond creation, or the response format. This is a moderate level of transparency for a simple mutation 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 concise, consisting of two short sentences. It front-loads the main purpose and then adds a precise condition. Every word earns its place; there is no fluff or redundancy.
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?
The tool has a moderate complexity with 5 parameters and no output schema. The description covers the essential purpose and a key constraint, but it omits other useful context such as what the review creation entails (e.g., whether it requires a commit ID), potential errors, or the shape of the response. Given the absence of an output schema, these gaps leave completeness at a 'minimum viable' level.
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?
All parameters have schema descriptions, giving a baseline of 3. The description adds semantic value by clarifying that 'body' is conditionally required for REQUEST_CHANGES or COMMENT, something not explicit in the schema where body is optional. This enhancement justifies a score above baseline.
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: 'Crea una review de un Pull Request' (Creates a Pull Request review). It is specific enough to distinguish from sibling tools like create_pull_request_comment, though it does not explicitly name that alternative. The purpose is unambiguous.
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?
The description provides no explicit guidance on when to use this tool versus alternatives such as create_pull_request_comment. It only mentions a conditional requirement about the body for certain event types, which is a parameter-level note rather than tool-selection guidance. There is no 'use this when...' or 'instead of...' statement.
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 the full burden of behavioral disclosure. It only states that the tool lists comments, but does not disclose pagination, ordering, what types of comments are included (review comments vs. issue comments), authentication requirements, or any other behavioral nuances. This is a significant gap for a tool with no structured safety hints.
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, short sentence that is front-loaded with the action and resource. Every word earns its place, and there is no irrelevant information. It is efficiently minimal without being vague about 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?
For a simple list tool with no output schema and no annotations, the description is too sparse. It does not explain what the response will include, whether pagination is supported, or how comments are ordered. The ambiguity about which types of comments (e.g., review comments vs. general comments) are listed further limits completeness. The schema covers parameters, but behavioral and output context are missing.
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 provides 100% coverage, describing each parameter (owner, repo, pull_number) with Spanish labels. The description adds no extra meaning beyond what the schema provides, so the baseline score of 3 is appropriate. It does not clarify parameter formats or relationships 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 uses a specific verb 'Lista' (Lists) and clearly identifies the resource as 'los comentarios de un Pull Request' (comments of a Pull Request). This distinguishes it from siblings like list_pull_requests (lists PRs) and create_pull_request_comment (creates a comment), 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?
The usage is implied by the name and description: use it to list comments on a PR. However, there is no explicit guidance on when to prefer this over alternatives, such as when only review comments are needed or when to use create_pull_request_comment. No exclusions or alternative tool mentions are provided.
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. It implies a read operation but does not disclose behavior such as error handling, authentication requirements, rate limits, or the exact structure of the returned data.
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 clear sentence with no filler. It is front-loaded and properly sized for a simple getter tool.
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?
For a straightforward read operation with fully described parameters, the description is minimally adequate. However, since there is no output schema, it does not clarify the return format beyond the vague word 'detalle', leaving some ambiguity about what the response contains.
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 description coverage is 100%, so parameters are well-documented in the schema. The description adds no additional parameter meaning beyond what the schema already provides, so the baseline 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 states a clear verb ('Obtiene' = gets) and resource ('detalle de un Pull Request específico' = detail of a specific Pull Request). It distinguishes from siblings like list_pull_requests by explicitly indicating a single PR.
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 usage context is implied by the name and description (to fetch one PR's details), but there is no explicit guidance on when to use this versus alternatives or any exclusions. It lacks direct sibling differentiation.
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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