io.github.DiegoBr4nd/godot-gut-mcp
Server Quality Checklist
Latest release: v0.1.2
- Disambiguation5/5
Each tool has a clear, distinct purpose: running all tests, running a specific test file, and retrieving failures from the last run. There is minimal overlap, as get_failures is a targeted diagnostic rather than a duplicate of the test runners.
Naming Consistency5/5All tool names follow a consistent verb_noun snake_case pattern: run_all_tests, run_test_file, get_failures. No mixed conventions or vague verbs.
Tool Count4/5With 3 tools, the server is slightly lean but well-scoped for its purpose of running GUT tests and reporting failures. It sits at the lower edge of the ideal range but does not feel insufficient.
Completeness4/5The server covers the core testing lifecycle: run all tests, run a specific file, and inspect failures. A minor gap is the lack of running a single test method or listing available tests, but this does not severely hinder typical usage.
Average 3.3/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 11 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.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto 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 carries full responsibility. However, it does not clarify whether the tool runs tests itself or simply reports failures from a previous run. It also fails to disclose any side effects or the role of the 'test_dir' parameter, leaving significant behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, with two short sentences. Both sentences serve a purpose: the first states the core action, the second gives the rationale. It is well-structured and front-loaded, though it sacrifices necessary detail for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given 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 is minimal but incomplete. It does not explain how 'test_dir' affects behavior, nor does it clarify the relationship with sibling tools or the format of the returned failures. This is insufficient for reliable usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one optional parameter ('test_dir') with a default value, but the description does not mention or explain it. With 0% schema description coverage, the description should compensate, but it completely ignores the parameter, leaving its meaning unclear.
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: 'Devolver solo las pruebas que fallaron' (return only failed tests). This distinct verb+resource combination differentiates it from sibling tools like 'run_all_tests' and 'run_test_file', which execute tests rather than retrieve failures.
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 phrase 'Para corregir rápido' implies a usage context (quick fixing), but there is no explicit guidance on when to use this tool versus alternatives, nor any mention of prerequisites or workflow. The usage is implied rather than clearly stated.
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 must fully disclose behavior. It only states the action and summary output, but does not mention side effects, environment requirements, test_dir usage, or potential errors.
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?
A single, focused sentence that clearly communicates the tool's purpose. No wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks context about the test_dir parameter, output summary format, and when to use this tool versus siblings. Given the simplicity (1 optional param) it is still incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 1 parameter (test_dir) with 0% description coverage, and the description does not mention it. The parameter name is somewhat self-explanatory, but the description adds no meaning beyond the schema.
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 runs all tests in the Godot project and returns a summary, using a specific verb and indicating the outcome. This distinguishes it from siblings like run_test_file (single file) and get_failures (retrieves failures).
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 when all tests need to be run, but it does not explicitly mention when to prefer this over alternatives or provide exclusions. Sibling tools exist but are not referenced.
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 exist, so the description must disclose behavioral traits. It only states the action ('run') and provides an example, but fails to mention side effects, output, or any changes to the system. The description is minimal and leaves the actual execution behavior (e.g., displaying results, modifying files) 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 a single, direct sentence followed by an example. It is concise, front-loaded, and every element adds value. There is no redundant or filler content.
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?
Given the tool's simplicity (one parameter) and no output schema, the description is adequate but not complete. It tells the user what the tool does and an example, but does not explain what happens after execution (e.g., test results, error reporting). More detail about the execution outcome would improve completeness, especially given the lack of annotations.
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 schema has one required string parameter with no description. The description partially compensates by providing an example 'res://test_test_player.gd', which clarifies the expected resource-path format. However, it does not fully explain the parameter semantics (e.g., constraints on file type or path structure), relying on the example to convey meaning.
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: 'Ejecutar un archivo de pruebas' (Run a test file). It includes a concrete example of the file path, making the purpose unambiguous. It also distinguishes itself from siblings like run_all_tests by specifying a single test file rather than all tests.
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 example (run a specific test file) but does not explicitly contrast with alternatives like run_all_tests or get_failures. No clear 'when to use' or 'when not to use' guidance is provided, relying on the user to infer from the name and example.
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:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/DiegoBr4nd/godot-gut-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server