RepoMind-MCP
Server Quality Checklist
Latest release: v2.0.1
- Disambiguation5/5
Each tool targets a distinct phase of repository analysis: scanning structure, computing dependencies, generating diagrams, and calculating impact. No overlap or ambiguity exists between them.
Naming Consistency5/5All tools follow the consistent pattern 'repomind_<verb>_<object>' with snake_case throughout. The verb-noun construction is uniform and predictable across the set.
Tool Count5/5Four tools is an ideal size for a focused repository analysis server. Each tool earns its place and there is no bloat or redundancy.
Completeness5/5The tool set forms a complete workflow: scan a workspace, extract dependencies, visualize them, and analyze impact. No obvious gaps exist for the stated purpose of repository analysis.
Average 3.8/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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.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?
With no annotations, the description carries full burden but only states the core action of generating a diagram and optionally grouping by directories. It does not disclose whether the tool is read-only, any required permissions, or how output is returned.
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 two sentences, front-loaded with the main purpose, and avoids redundancy. The word 'beautiful' is non-essential but does not significantly detract.
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?
The description covers the basic function and one key feature, but lacks clarity on return value format and the effect of optional parameters, though these are documented in the schema. Without annotations or output schema, it is minimally adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents all three parameters with descriptions, so the description adds no additional meaning. The mention of grouping by directories is a feature detail, not tied to specific parameter usage.
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 generates a Mermaid diagram of the dependency graph, which is distinct from sibling tools that scan, get dependencies, or analyze impact. The verb 'Generates' and resource 'Mermaid diagram' are specific and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives; it does not mention that get_dependencies should be used for raw data or impact_analysis for impact assessment. Usage is only implied by the description's purpose.
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, the description carries the full burden. It discloses that the scan is recursive, ignores binary/build folders (node_modules, .git, dist), and provides statistics. However, it does not explicitly state that the operation is read-only or mention any potential side effects, performance implications, or error conditions.
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, well-structured sentence that front-loads the main action ('Recursively scans'), then adds specific details about ignored folders and outputs. Every clause earns its place, with no superfluous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description adequately covers the tool's behavior and output by mentioning file structure mapping, source file listing, and statistics. It could be more specific about the return format (e.g., JSON object), but the current level is sufficient for a straightforward scan.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter, basePath, is fully described in the schema ('The absolute path to the workspace root directory to scan'). The description adds no extra meaning beyond repeating that it is the workspace root. Baseline 3 is appropriate given 100% schema coverage.
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 verb ('Recursively scans'), resource ('workspace directory'), and scope ('mapping its file structure, listing source files, and providing statistics'). It also distinguishes itself from siblings by specifying it ignores binary/build folders, which is unique among the listed tools.
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 for scanning a workspace to understand its structure, but does not explicitly state when to use this tool versus alternatives like repomind_generate_diagram or repomind_impact_analysis. No exclusions or explicit 'use this when...' guidance is provided.
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 the burden of behavioral disclosure. It clearly indicates a read-only analysis operation (no side effects) and specifies supported languages and output type (directed dependency graph). It lacks details on scalability or edge cases, but for a simple analysis tool it is sufficiently transparent.
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 well-structured sentence that front-loads the core function and includes essential qualifiers (languages, file/directory, output type). Every word earns its place with no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description is largely complete: it specifies the input path, languages analyzed, and the output graph. It could mention return format or limitations, but these are less critical for a read-only analysis tool in the context of sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter targetPath, which already defines it as an absolute path to a file or directory. The description adds no new information about parameter semantics beyond what the schema states, so a baseline of 3 is appropriate.
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 analyzes imports in JavaScript, TypeScript, and Python files and builds a directed dependency graph. It uses specific verbs and resources, and it distinguishes itself from siblings by focusing on dependency graph construction rather than scanning, diagramming, or impact analysis.
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 the tool should be used to analyze dependencies in a given path, but it does not explicitly state when to prefer this over sibling tools like repomind_scan_workspace or repomind_impact_analysis. There is no mention of exclusions or alternative usage 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?
No annotations are provided, so the description carries the burden of behavioral disclosure. It adds meaningful context by explaining the tool shows 'directly or indirectly' dependent files, hinting at recursive traversal. However, it does not detail the output format, performance implications, or how depth is handled beyond what the schema already states. This is minimal but non-contradictory.
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, focused sentence that clearly conveys the tool's purpose without any filler or repetition. Every part contributes meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple two-parameter schema and clear purpose, the description is complete enough for an agent to select and invoke the tool. It lacks an output schema, but the description implies a list of dependent files. It could be slightly more detailed about output format, but overall it is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with both targetPath and depth having descriptions. The description adds 'directly or indirectly' which maps to depth traversal but does not provide substantial additional semantics. Baseline 3 applies because the schema already explains parameters.
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: 'Calculates the downstream blast radius (reverse dependents) of modifying a target file or module, showing exactly which files depend on it directly or indirectly.' This uses a specific verb and resource, and distinguishes it from siblings like repomind_get_dependencies (forward dependencies) by emphasizing reverse dependents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a clear use case: assessing the impact of modifying a file or module. It states the purpose ('if modifying a target file') but does not explicitly mention alternatives or exclusions. This is clear context without explicit when-not-to-use guidance, matching the 'clear context, no exclusions' level.
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/deep095/RepoMind-MCP'
If you have feedback or need assistance with the MCP directory API, please join our Discord server