Skip to main content
Glama

ask-rovodev

Query AI for code analysis, file examination, and repository exploration using @file syntax, specialized modes, and large context windows.

Instructions

Query Rovodev AI with support for file analysis (@file or #file syntax), codebase exploration, and large context windows. Supports various models and execution modes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesThe query or instruction for Rovodev. Use @filename, #filename, or directory references to include file contents. Example: '@src/ Explain this codebase structure'
modelNoOptional model to use (e.g., 'default'). If not specified, uses the default model (default).
sandboxNoUse sandbox mode to safely test code changes, execute scripts, or run potentially risky operations in an isolated environment
approvalModeNoControl tool execution approval: 'plan' (analyze only), 'default' (prompt for approval), 'auto-edit' (auto-approve edits), 'yolo' (auto-approve all)
yoloNoEnable YOLO mode to automatically approve all tool calls without prompting (equivalent to approvalMode='yolo')
allFilesNoInclude all files in the current directory as context (use with caution for large directories)
debugNoEnable debug mode for more verbose output
codeModeNoEnable code-specific analysis mode for better code understanding
reviewModeNoEnable code review mode for detailed feedback
optimizeNoRequest optimization suggestions for the code
explainNoRequest detailed explanations of code functionality
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions capabilities like file analysis and large context windows, it lacks critical behavioral details: whether this is a read-only or mutating operation, authentication requirements, rate limits, response format, or error handling. The description covers what the tool can do but not how it behaves operationally.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise - a single sentence that efficiently lists key capabilities. It's front-loaded with the core purpose and doesn't waste words. However, it could be slightly more structured by separating distinct capability categories more clearly.

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?

For a complex tool with 11 parameters and no annotations or output schema, the description is insufficient. It doesn't explain what kind of response to expect, error conditions, operational constraints, or how the various parameters interact. The description covers 'what' but not 'how' or 'what happens next,' leaving significant gaps for a tool of this complexity.

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%, so the schema already documents all 11 parameters thoroughly. The description adds minimal parameter semantics beyond what's in the schema - it mentions '@file or #file syntax' which relates to the 'prompt' parameter, but doesn't provide additional context about parameter interactions or advanced usage patterns. Baseline 3 is appropriate when 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 tool's purpose: 'Query Rovodev AI with support for file analysis, codebase exploration, and large context windows.' It specifies the action (query), target (Rovodev AI), and key capabilities. However, it doesn't explicitly differentiate from sibling tools like 'Help' or 'ping' beyond the AI query focus.

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 mentions general capabilities but provides no guidance on when to use this tool versus alternatives. There's no mention of when to choose this over sibling tools like 'Help' or 'ping', nor any context about appropriate use cases versus other query methods. Usage is implied through feature listing rather than explicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

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/Jaggerxtrm/rovodev-mcp-tool'

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