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Documentation Index

Fetch the complete documentation index at: https://mintlify.com/5unnykum4r/grip-ai/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

Grip AI’s tool system provides a unified abstraction for both built-in tools and MCP (Model Context Protocol) tools. Every tool implements the Tool ABC and is registered in a central ToolRegistry. The registry handles:
  • Registration — Add/remove tools at runtime
  • Schema generation — Export OpenAI function-calling definitions
  • Execution dispatch — Route tool calls to the correct implementation
  • Result serialization — Convert Pydantic models to JSON automatically

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Tool Interface

All tools implement this abstract base class: 
class Tool(ABC):
    @property
    @abstractmethod
    def name(self) -> str:
        """Unique identifier used in tool_call function_name."""
        ...

    @property
    @abstractmethod
    def description(self) -> str:
        """One-line description shown to the LLM."""
        ...

    @property
    @abstractmethod
    def parameters(self) -> dict[str, Any]:
        """JSON Schema (type: object) describing accepted parameters."""
        ...

    @property
    def category(self) -> str:
        """Tool category for grouped display. Defaults to 'general'."""
        return "general"

    @abstractmethod
    async def execute(self, params: dict[str, Any], ctx: ToolContext) -> ToolResult:
        """Run the tool with validated parameters and return a result.

        Can return a plain string or a Pydantic BaseModel instance.
        BaseModel instances are automatically serialized to JSON.
        """
        ...

    def to_definition(self) -> dict[str, Any]:
        """Serialize this tool to OpenAI function-calling schema format."""
        return {
            "type": "function",
            "function": {
                "name": self.name,
                "description": self.description,
                "parameters": self.parameters,
            },
        }

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Tool Context

Every tool execution receives a ToolContext with runtime information: 
@dataclass
class ToolContext:
    workspace_path: Path
    restrict_to_workspace: bool = False
    shell_timeout: int = 60
    session_key: str = ""
    extra: dict[str, Any] = field(default_factory=dict)
The extra dict can contain:
  • brave_api_key — For web search tools
  • dry_run — Skip actual execution (testing mode)
  • trust_manager — For sandbox file access validation

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Tool Registry

The ToolRegistry manages all registered tools: 
class ToolRegistry:
    def __init__(self) -> None:
        self._tools: dict[str, Tool] = {}
        self._category_cache: dict[str, list[Tool]] | None = None
        self.mcp_manager: Any = None

    def register(self, tool: Tool) -> None:
        if tool.name in self._tools:
            logger.warning("Overwriting existing tool registration: {}", tool.name)
        self._tools[tool.name] = tool
        self._category_cache = None
        logger.debug("Registered tool: {}", tool.name)

    def register_many(self, tools: list[Tool]) -> None:
        for tool in tools:
            self.register(tool)

    def get_definitions(self) -> list[dict[str, Any]]:
        """Return OpenAI function-calling definitions for all registered tools."""
        return [tool.to_definition() for tool in self._tools.values()]

    async def execute(self, name: str, params: dict[str, Any], ctx: ToolContext) -> str:
        """Look up a tool by name and execute it.

        Returns error string (not exception) if tool is not found or fails.
        Pydantic BaseModel results are serialized to indented JSON automatically.
        """
        tool = self._tools.get(name)
        if tool is None:
            return f"Error: Unknown tool '{name}'. Available: {', '.join(self._tools.keys())}"

        try:
            result = await tool.execute(params, ctx)
            return _serialize_result(result)
        except Exception as exc:
            logger.error("Unhandled error in tool {}: {}", name, exc, exc_info=True)
            return f"Error executing {name}: {type(exc).__name__}: {exc}"

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Built-in Tools

Grip includes tools across multiple categories:

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Filesystem Tools

  • read_file — Read file contents with offset/limit
  • write_file — Write or overwrite file
  • append_file — Append to existing file
  • list_directory — List files and directories
  • create_directory — Create directory tree
  • delete_file — Remove file
  • move_file — Move or rename file
  • search_files — Glob pattern search
  • grep_files — Content search with regex

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Shell Tools

  • execute_command — Run shell commands with timeout
  • get_environment — Read environment variables

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Web Tools

  • fetch_url — HTTP GET with headers
  • brave_search — Web search via Brave API
  • scrape_page — Extract clean text from HTML

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Messaging Tools

  • send_message — Send text to user via channel
  • send_file — Send file attachment

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Orchestration Tools

  • spawn_subagent — Launch parallel agent tasks
  • schedule_task — Schedule cron jobs
  • workflow_execute — Run multi-step workflows

​
Finance Tools

  • stock_quote — Get stock price (requires yfinance)
  • crypto_price — Get cryptocurrency price

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Research Tools

  • research_topic — Multi-source research with citations
  • fact_check — Verify claims against sources

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Code Analysis Tools

  • analyze_code — Static analysis and metrics
  • find_definition — Locate class/function definitions
  • trace_calls — Build call graphs

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Data Transform Tools

  • convert_format — CSV ↔ JSON ↔ YAML conversions
  • filter_data — Query JSON with JSONPath
  • aggregate_data — Sum, average, group-by operations

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Document Generation Tools

  • generate_markdown — Create formatted markdown
  • generate_pdf — Convert markdown to PDF
  • generate_diagram — Create Mermaid diagrams

​
Email Tools

  • compose_email — Draft email with template
  • send_email — Send via SMTP (requires config)

​
Task Management Tools

  • todo_write — Create and update tasks
  • todo_read — List active tasks

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Tool Execution Flow

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1. LLM Returns Tool Calls

When the LLM decides to use tools, it returns: 
{
  "content": "I'll search the codebase for the config file.",
  "tool_calls": [
    {
      "id": "call_abc123",
      "function_name": "search_files",
      "arguments": {
        "pattern": "**/config*.py",
        "directory": "/workspace"
      }
    }
  ]
}

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2. Parallel Tool Execution

The agent loop executes all tool calls in parallel: 
# Execute all tool calls in parallel via asyncio.gather
exec_results = await asyncio.gather(
    *(self._execute_tool(tc, tool_ctx) for tc in response.tool_calls)
)

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3. Tool Invocation

async def _execute_tool(self, tool_call: ToolCall, ctx: ToolContext) -> ToolExecutionResult:
    args = tool_call.arguments if isinstance(tool_call.arguments, dict) else {}
    logger.info(
        "Executing tool: {}({})",
        tool_call.function_name,
        ", ".join(f"{k}={v!r}" for k, v in list(args.items())[:3]),
    )

    start = time.perf_counter()

    # Prefer ToolRegistry
    if self._registry:
        output = await self._registry.execute(tool_call.function_name, args, ctx)
        elapsed = (time.perf_counter() - start) * 1000
        success = not output.startswith("Error:")
        return ToolExecutionResult(
            tool_call_id=tool_call.id,
            tool_name=tool_call.function_name,
            output=output,
            success=success,
            duration_ms=elapsed,
        )

​
4. Result Scrubbing

Secrets are redacted before appending to message history: 
for exec_result in exec_results:
    all_tool_calls.append(exec_result.tool_name)
    all_tool_details.append(
        ToolCallDetail(
            name=exec_result.tool_name,
            success=exec_result.success,
            duration_ms=exec_result.duration_ms,
            output_preview=exec_result.output[:120],
        )
    )
    # Scrub secrets before storing tool output in message history
    scrubbed_output = _scrub_secrets(exec_result.output)
    messages.append(
        LLMMessage(
            role="tool",
            content=scrubbed_output,
            tool_call_id=exec_result.tool_call_id,
            name=exec_result.tool_name,
        )
    )

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5. Loop Continues

The agent loop sends tool results back to the LLM, which can:
  • Return final text response (loop ends)
  • Make more tool calls (loop continues)
  • Hit max_tool_iterations limit (forced completion)

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Creating Custom Tools

Example custom tool: 
from grip.tools.base import Tool, ToolContext, ToolResult
from typing import Any

class MyCustomTool(Tool):
    @property
    def name(self) -> str:
        return "my_custom_tool"

    @property
    def description(self) -> str:
        return "Does something custom and useful"

    @property
    def parameters(self) -> dict[str, Any]:
        return {
            "type": "object",
            "properties": {
                "input_text": {
                    "type": "string",
                    "description": "The text to process",
                },
                "format": {
                    "type": "string",
                    "enum": ["json", "yaml", "xml"],
                    "description": "Output format",
                },
            },
            "required": ["input_text"],
        }

    @property
    def category(self) -> str:
        return "custom"

    async def execute(self, params: dict[str, Any], ctx: ToolContext) -> ToolResult:
        input_text = params["input_text"]
        format_type = params.get("format", "json")

        # Your custom logic here
        result = f"Processed {len(input_text)} chars in {format_type} format"

        return result

# Register the tool
registry = create_default_registry()
registry.register(MyCustomTool())

​
MCP Tool Integration

MCP servers provide additional tools via the MCP protocol: 
class MCPManager:
    async def connect_all(
        self, mcp_servers: dict[str, MCPServerConfig], registry: ToolRegistry
    ) -> None:
        for name, config in mcp_servers.items():
            if not config.enabled:
                continue

            try:
                # Connect to MCP server (stdio or SSE)
                client = await self._connect_server(name, config)

                # List available tools
                tools_result = await client.list_tools()

                # Register each tool as a dynamic tool in the registry
                for tool_info in tools_result.tools:
                    mcp_tool = MCPTool(
                        server_name=name,
                        tool_name=tool_info.name,
                        description=tool_info.description,
                        parameters=tool_info.inputSchema,
                        client=client,
                    )
                    registry.register(mcp_tool)

                logger.info(
                    "Connected to MCP server '{}': {} tools",
                    name,
                    len(tools_result.tools),
                )
            except Exception as exc:
                logger.error("Failed to connect to MCP server '{}': {}", name, exc)
MCP tools are registered with prefixed names: mcp__server__tool

​
Tool Categories

Tools are grouped by category for system prompt generation: 
def get_tools_by_category(self) -> dict[str, list[Tool]]:
    groups: dict[str, list[Tool]] = {}
    for tool in self._tools.values():
        groups.setdefault(tool.category, []).append(tool)
    return groups
Valid categories:
  • filesystem — File I/O operations
  • shell — Command execution
  • web — HTTP requests and scraping
  • messaging — User communication
  • orchestration — Subagents, workflows, scheduling
  • finance — Stock/crypto data
  • research — Multi-source research
  • code_analysis — Static analysis
  • data_transform — Data format conversions
  • document_gen — Document creation
  • general — Uncategorized

​
Configuration

tools:
  # Built-in tool settings
  restrict_to_workspace: true  # Sandbox file access
  shell_timeout: 60  # Command timeout in seconds

  # Web tools
  web:
    brave:
      enabled: true
      api_key: "your-brave-api-key"

  # MCP servers
  mcp_servers:
    filesystem:
      enabled: true
      command: "uvx"
      args: ["mcp-server-filesystem", "/workspace"]

    fetch:
      enabled: true
      command: "uvx"
      args: ["mcp-server-fetch"]
      allowed_tools:
        - "mcp__fetch__fetch"

    memory:
      enabled: true
      url: "https://memory.example.com/sse"
      type: "sse"
      headers:
        Authorization: "Bearer token"

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Tool Result Serialization

Tools can return strings or Pydantic models: 
def _serialize_result(result: ToolResult) -> str:
    if isinstance(result, str):
        return result
    if PydanticBaseModel is not None and isinstance(result, PydanticBaseModel):
        return result.model_dump_json(indent=2)
    if isinstance(result, (dict, list)):
        return json.dumps(result, indent=2, default=str)
    return str(result)
Example Pydantic result: 
from pydantic import BaseModel

class SearchResult(BaseModel):
    total_files: int
    matches: list[str]
    time_ms: float

class SearchTool(Tool):
    async def execute(self, params: dict[str, Any], ctx: ToolContext) -> ToolResult:
        # ... search logic ...
        return SearchResult(
            total_files=len(files),
            matches=matching_files,
            time_ms=elapsed,
        )
The registry automatically serializes to JSON: 
{
  "total_files": 142,
  "matches": [
    "/workspace/config.py",
    "/workspace/settings/config.yaml"
  ],
  "time_ms": 234.5
}

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