grepai-mcp-tools▌
yoanbernabeu/grepai-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
This skill provides a complete reference for all tools available through GrepAI's MCP server.
GrepAI MCP Tools Reference
This skill provides a complete reference for all tools available through GrepAI's MCP server.
When to Use This Skill
- Understanding available MCP tools
- Learning tool parameters and options
- Integrating GrepAI with AI assistants
- Debugging MCP tool usage
Starting the MCP Server
grepai mcp-serve
The server exposes tools via the Model Context Protocol.
Available Tools
1. grepai_search
Semantic code search using embeddings.
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
query |
string | Yes | - | Search query describing what to find |
limit |
number | No | 10 | Maximum results to return |
compact |
boolean | No | false | Return compact output (no content) |
format |
string | No | "json" | Output format: "json" or "toon" (v0.26.0+) |
Example Request
{
"tool": "grepai_search",
"parameters": {
"query": "user authentication middleware",
"limit": 5,
"compact": true,
"format": "toon"
}
}
Response (Compact)
{
"q": "user authentication middleware",
"r": [
{"s": 0.92, "f": "src/auth/middleware.go", "l": "15-45"},
{"s": 0.85, "f": "src/auth/jwt.go", "l": "23-55"},
{"s": 0.78, "f": "src/handlers/auth.go", "l": "10-40"}
],
"t": 3
}
Response (Full)
{
"query": "user authentication middleware",
"results": [
{
"score": 0.92,
"file": "src/auth/middleware.go",
"start_line": 15,
"end_line": 45,
"content": "func AuthMiddleware() gin.HandlerFunc {\n ..."
}
],
"total": 3
}
2. grepai_trace_callers
Find all functions that call a specified symbol.
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
symbol |
string | Yes | - | Function/method name to trace |
compact |
boolean | No | false | Return compact output (no context) |
format |
string | No | "json" | Output format: "json" or "toon" (v0.26.0+) |
Example Request
{
"tool": "grepai_trace_callers",
"parameters": {
"symbol": "Login",
"compact": true
}
}
Response (Compact)
{
"q": "Login",
"m": "callers",
"c": 3,
"r": [
{"f": "handlers/auth.go", "l": 42, "fn": "HandleAuth"},
{"f": "handlers/auth_test.go", "l": 15, "fn": "TestLoginSuccess"},
{"f": "cmd/main.go", "l": 88, "fn": "RunCLI"}
]
}
Response (Full)
{
"query": "Login",
"mode": "callers",
"count": 3,
"results": [
{
"file": "handlers/auth.go",
"line": 42,
"caller": "HandleAuth",
"context": "user.Login(ctx, credentials)"
}
]
}
3. grepai_trace_callees
Find all functions called by a specified symbol.
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
symbol |
string | Yes | - | Function/method name to trace |
compact |
boolean | No | false | Return compact output (no context) |
format |
string | No | "json" | Output format: "json" or "toon" (v0.26.0+) |
Example Request
{
"tool": "grepai_trace_callees",
"parameters": {
"symbol": "ProcessOrder",
"compact": true
}
}
Response (Compact)
{
"q": "ProcessOrder",
"m": "callees",
"c": 4,
"r": [
{"f": "services/order.go", "l": 45, "fn": "validateOrder"},
{"f": "services/order.go", "l": 48, "fn": "calculateTotal"},
{"f": "services/order.go", "l": 51, "fn": "applyDiscount"},
{"f": "services/order.go", "l": 55, "fn": "sendConfirmation"}
]
}
4. grepai_trace_graph
Build a complete call graph starting from a symbol.
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
symbol |
string | Yes | - | Root function for the graph |
depth |
number | No | 2 | Maximum recursion depth |
compact |
boolean | No | false | Return compact JSON format |
format |
string | No | "json" | Output format: "json" or "toon" (v0.26.0+) |
Example Request
{
"tool": "grepai_trace_graph",
"parameters": {
"symbol": "main",
"depth": 3,
"compact": true
}
}
Response (Compact)
{
"q": "main",
"d": 3,
"r": {
"n": "main",
"c": [
{
"n": "initialize",
"c": [
{"n": "loadConfig"},
{"n": "connectDB"}
]
},
{
"n": "startServer",
"c": [
{"n"How to use grepai-mcp-tools on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add grepai-mcp-tools
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches grepai-mcp-tools from GitHub repository yoanbernabeu/grepai-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate grepai-mcp-tools. Access the skill through slash commands (e.g., /grepai-mcp-tools) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★60 reviews- ★★★★★Anaya Desai· Dec 28, 2024
grepai-mcp-tools has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Nikhil Khan· Dec 24, 2024
grepai-mcp-tools has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Xiao Liu· Dec 20, 2024
grepai-mcp-tools fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Chaitanya Patil· Dec 8, 2024
grepai-mcp-tools reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Piyush G· Nov 27, 2024
I recommend grepai-mcp-tools for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Arya Verma· Nov 11, 2024
grepai-mcp-tools has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Shikha Mishra· Oct 18, 2024
Useful defaults in grepai-mcp-tools — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Advait Kapoor· Sep 21, 2024
Keeps context tight: grepai-mcp-tools is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Yash Thakker· Sep 9, 2024
grepai-mcp-tools has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Carlos Haddad· Sep 9, 2024
Useful defaults in grepai-mcp-tools — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
showing 1-10 of 60