GDScript Code Analyzer

by minami110

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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, such as analyze_gdscript_code vs. analyze_gdscript_file vs. get_gdscript_structure, which all seem to extract code structure but with subtle differences in input or output. However, descriptions help clarify some distinctions, like find_gdscript_symbol being for specific symbols versus find_references for broader usage tracking.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with clear verb_noun combinations (e.g., analyze_gdscript_code, find_gdscript_symbol, get_gdscript_dependencies). There are no deviations in naming style, making the set predictable and easy to parse.
Tool Count5/5
With 8 tools, the count is well-scoped for a GDScript code analyzer, covering analysis, searching, dependency tracking, and project management. Each tool appears to serve a distinct role without unnecessary bloat, fitting typical expectations for this domain.
Completeness4/5
The tool set provides strong coverage for static analysis tasks in GDScript, including code structure extraction, symbol lookup, reference tracking, and project setup. A minor gap exists in dynamic or runtime analysis capabilities, but core workflows for code understanding are well-supported.
Average 3.5/5 across 8 of 8 tools scored. Lowest: 2.9/5.
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 carries the full burden of behavioral disclosure. It states the tool searches and returns details but doesn't specify what details are included (e.g., line numbers, type information), error handling (e.g., if symbol not found), or performance aspects (e.g., case sensitivity). This leaves gaps for a tool with no annotation coverage.
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, efficient sentence that front-loads the purpose ('search for a specific symbol') and outcome ('get its details'), with no wasted words. It's appropriately sized for a simple tool and earns its place by clearly stating the action and resource.
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 no annotations and no output schema, the description is incomplete. It doesn't explain what 'details' are returned, how errors are handled, or any behavioral traits. For a tool with two parameters and no structured output, more context is needed to fully guide an agent, making this inadequate.
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%, with both parameters ('file_path' and 'symbol_name') clearly documented in the schema. The description adds minimal value beyond this, mentioning 'symbol' types but not elaborating on parameter usage or constraints. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'search' and the resource 'symbol in a GDScript file', specifying the types of symbols (class, function, signal, etc.) and the outcome 'get its details'. It distinguishes from siblings like 'analyze_gdscript_file' or 'get_gdscript_structure' by focusing on symbol lookup rather than broader analysis. However, it doesn't explicitly contrast with 'find_references', which might overlap in purpose, preventing a perfect score.
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 guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing the file path or symbol name, or compare it to siblings like 'find_references' for broader searches or 'get_gdscript_structure' for structural overview. Usage is implied by the action but lacks explicit context or exclusions.
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 full burden. It states the tool analyzes and extracts structure, but doesn't disclose behavioral traits such as what 'extract its structure' entails (e.g., returns AST, symbols, dependencies), performance characteristics, error handling, or any limitations. This leaves significant gaps for an agent to understand how the tool behaves beyond its basic purpose.
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, efficient sentence that front-loads the key action and resource. It wastes no words and clearly communicates the core functionality, making it easy for an agent to parse quickly.
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 no annotations, no output schema, and a single parameter with full schema coverage, the description is incomplete. It doesn't explain what 'extract its structure' means in terms of return values or behavior, which is critical for an analysis tool. With no structured data to compensate, the description should provide more context about outputs and operational details.
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%, with the parameter 'code' well-documented in the schema as 'GDScript source code to analyze.' The description adds no additional meaning beyond this, as it doesn't elaborate on code format, size limits, or examples. Baseline 3 is appropriate since the schema does the heavy lifting, but no extra value is added.
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 ('analyze') and resource ('GDScript code provided directly'), specifying it extracts structure. It distinguishes from sibling 'analyze_gdscript_file' by specifying 'provided directly' (inline code vs. file). However, it doesn't fully differentiate from 'get_gdscript_structure' which may have overlapping functionality.
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 context through 'provided directly,' suggesting this tool is for analyzing inline code rather than files (contrasting with 'analyze_gdscript_file'). However, it lacks explicit guidance on when to use this vs. other structure-related siblings like 'get_gdscript_structure' or 'find_gdscript_symbol,' and no exclusions or prerequisites are mentioned.
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 of behavioral disclosure. It mentions the search scope (project-wide or file-specific) but fails to describe key behaviors such as the return format (e.g., list of locations, counts), performance characteristics (e.g., speed, limitations), or error handling (e.g., what happens if the symbol isn't found). For a search tool with zero annotation coverage, this leaves significant gaps in understanding how the tool operates beyond its basic purpose.
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, well-structured sentence that efficiently conveys the core functionality and scope without any wasted words. It is front-loaded with the main action ('Find all references'), making it easy for an agent to quickly grasp the tool's purpose. Every part of the sentence earns its place by adding clarity.
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 moderate complexity (search operation with 2 parameters), no annotations, and no output schema, the description is minimally adequate. It covers the basic what and where but lacks details on behavioral aspects like output format, error cases, or performance. While it meets the minimum for a simple search tool, it doesn't provide enough context for an agent to fully predict the tool's behavior in all scenarios, especially without annotations to fill 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?
The input schema has 100% description coverage, with clear documentation for both parameters ('symbol_name' and 'file_path'). The description adds marginal value by reinforcing that 'file_path' is optional and specifying the search scope implications, but does not provide additional semantics beyond what the schema already states, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate with extra insights.
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's purpose with a specific verb ('Find') and resource ('references to a symbol'), and specifies the scope ('across the project or in a specific file'). It distinguishes itself from siblings like 'find_gdscript_symbol' by focusing on references rather than the symbol itself. However, it doesn't explicitly differentiate from other tools like 'analyze_gdscript_code' or 'get_gdscript_dependencies', which might also involve reference-like operations.
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 by mentioning the optional 'file_path' parameter to limit searches, suggesting when to use it for project-wide vs. file-specific queries. However, it lacks explicit guidance on when to choose this tool over alternatives like 'find_gdscript_symbol' or 'get_gdscript_dependencies', and does not state any prerequisites or exclusions, leaving the agent to infer context from parameter descriptions.
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 mentions indexing .gd files, which adds behavioral context beyond the basic 'set' action. However, it lacks details on permissions needed, whether the operation is reversible, potential performance impact of indexing, or error handling (e.g., invalid paths).
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 concise sentences that are front-loaded with the core purpose and outcome. Every word contributes meaning without redundancy, making it efficient and well-structured.
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 no annotations and no output schema, the description provides basic purpose and behavioral hint (indexing). However, for a tool that likely mutates state and enables other operations, it should cover more (e.g., effects on sibling tools, error cases). It's minimally adequate but has clear gaps in context.
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%, with the parameter 'project_root' well-documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides (e.g., format examples or constraints), so it meets the baseline for high schema coverage.
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's purpose with a specific verb ('Set') and resource ('project root directory'), and explains the outcome ('enable project-wide analysis', 'index all .gd files'). It distinguishes from the sibling 'get_project_root' by being the setter counterpart, though it doesn't explicitly name that sibling.
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 context ('to enable project-wide analysis'), suggesting this is a prerequisite for other analysis tools. However, it doesn't explicitly state when to use this vs. alternatives (e.g., not needed for single-file analysis) or name specific sibling tools for comparison.
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 of behavioral disclosure. It states what the tool does (extract dependencies) but lacks details on behavioral traits such as error handling (e.g., what happens if the file path is invalid), output format, or performance considerations (e.g., whether it handles large files efficiently). This leaves gaps in understanding how the tool behaves in practice.
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, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence earns its place by specifying the action, resource, and key details (e.g., types of dependencies), making it highly concise and well-structured for quick understanding.
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 lack of annotations and output schema, the description is incomplete for a tool that performs extraction. It does not explain what the output looks like (e.g., list of dependencies, structured data), error conditions, or limitations (e.g., only works with specific GDScript versions). For a tool with no structured support, more contextual details are needed to ensure proper 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?
The input schema has 100% description coverage, with the 'file_path' parameter clearly documented in the schema. The description does not add any meaning beyond what the schema provides, as it does not elaborate on parameter usage, constraints, or examples. However, with high schema coverage, the baseline score of 3 is appropriate since the schema adequately covers the parameter 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?
The description clearly states the specific action ('Extract dependencies') and resource ('from a GDScript file'), with explicit examples of what dependencies are targeted (extends, preload, import statements). It distinguishes itself from sibling tools like 'analyze_gdscript_code' or 'get_gdscript_structure' by focusing specifically on dependency extraction rather than broader analysis or structural examination.
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 when dependency extraction is needed from a GDScript file, but it does not explicitly state when to use this tool versus alternatives like 'analyze_gdscript_file' or 'find_gdscript_symbol'. There is no guidance on prerequisites, exclusions, or specific scenarios where this tool is preferred over others, leaving the context somewhat open-ended.
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 carries the full burden of behavioral disclosure. It specifies the output content (classes, functions, signals, variables with line numbers) but does not mention performance aspects, error handling, or format details. It adequately describes the core behavior but lacks depth on operational traits.
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, well-structured sentence that efficiently conveys the tool's purpose and scope without unnecessary words. It is front-loaded with the main action and resource, making it easy to parse and understand quickly.
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 moderate complexity (single input, no output schema, no annotations), the description is reasonably complete. It explains what the tool does and the output content, but could benefit from more behavioral context or usage guidance to fully compensate for the lack of annotations and 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 description coverage is 100%, with the single parameter 'file_path' clearly documented in the schema. The description does not add any parameter-specific details beyond what the schema provides, such as file format requirements or path resolution rules, so it meets the baseline for high schema coverage.
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 specific action ('Get a high-level structure view') and resource ('of a GDScript file'), with detailed scope ('showing all classes, functions, signals, and variables with their line numbers'). It distinguishes from siblings like 'analyze_gdscript_code' or 'find_gdscript_symbol' by focusing on structural overview rather than code analysis or symbol lookup.
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 for extracting structural metadata from a GDScript file, but does not explicitly state when to use this tool versus alternatives such as 'analyze_gdscript_file' or 'find_gdscript_symbol'. No exclusions or prerequisites are mentioned, leaving the context somewhat open-ended.
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 key behavioral traits: it performs analysis/extraction (implying read-only, non-destructive operation), specifies the scope of extraction (classes, functions, etc.), and mentions that it returns a comprehensive overview without full file context (hinting at efficiency or output format). However, it lacks details on error handling, rate limits, or authentication needs, which are important for a tool with no annotation coverage.
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 appropriately sized and front-loaded, consisting of two concise sentences. The first sentence directly states the purpose and scope, and the second adds valuable behavioral context about the return and efficiency. Every sentence earns its place with no wasted words 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?
Given the tool's complexity (structural analysis of code files), lack of annotations, and no output schema, the description is moderately complete. It covers the purpose, scope, and a hint about output behavior, but does not fully address what the 'comprehensive overview' includes in terms of format or structure. For a tool with no output schema, more detail on return values would improve completeness, but the description provides a basic viable level.
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 has 100% description coverage, with the single parameter 'file_path' well-documented in the schema. The description does not add any meaning beyond the schema, as it does not elaborate on parameter usage, constraints, or examples. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract from the schema's clarity.
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 specific action ('analyze'), resource ('GDScript file'), and outcome ('extract its structure (classes, functions, signals, variables, enums)'). It distinguishes from siblings like 'analyze_gdscript_code' by specifying file-based analysis rather than code snippet analysis, and from 'get_gdscript_structure' by mentioning the comprehensive extraction scope.
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 provides clear context for when to use this tool: for analyzing GDScript files to extract structural elements. It implies usage over 'analyze_gdscript_code' by specifying file-based analysis, but does not explicitly state when not to use it or name alternatives beyond the sibling context. The mention of 'without reading the entire file into context' suggests a performance consideration, but no explicit exclusions are provided.
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 carries the full burden. It discloses the tool's behavior by specifying what it returns (directory and file count), which is useful. However, it lacks details on potential errors (e.g., if no project is loaded), performance implications, or whether this is a read-only operation (implied by 'Get' but not explicit). The description adds some value but misses key behavioral traits.
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, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by specifying both the primary output (directory) and secondary output (file count), making it appropriately sized and structured.
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 (0 parameters, no annotations, no output schema), the description is adequate but has gaps. It explains what the tool does but does not cover return format details (e.g., structure of the directory path or count), error conditions, or dependencies. For a tool with no structured output schema, more completeness would be beneficial to guide the agent fully.
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?
The input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description does not add parameter-specific information, which is unnecessary here. A baseline of 4 is appropriate for zero-parameter tools, as there is no gap to compensate for.
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 specific action ('Get'), the target resource ('current project root directory'), and an additional output ('count of indexed GDScript files'). It distinguishes from siblings like 'set_project_root' (which modifies) and analysis/finding tools that operate on code content rather than project metadata.
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 implies usage for retrieving project root information and file counts, which suggests context for project setup or status checks. However, it does not explicitly state when to use this versus alternatives like 'set_project_root' or when not to use it (e.g., for code analysis). The context is clear but lacks explicit exclusions or named alternatives.
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:

[![mcp-gdscript MCP server](https://glama.ai/mcp/servers/minami110/mcp-gdscript/badges/card.svg)](https://glama.ai/mcp/servers/minami110/mcp-gdscript)

Score Badge

Copy to your README.md:

[![mcp-gdscript MCP server](https://glama.ai/mcp/servers/minami110/mcp-gdscript/badges/score.svg)](https://glama.ai/mcp/servers/minami110/mcp-gdscript)

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/minami110/mcp-gdscript'

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