Skip to main content
Glama

code_assistant

Assist developers in writing and integrating Python code for video-related tasks, YouTube videos, and VideoDB SDK functionalities.

Instructions

Will give you data related to VideoDB SDK which allows developers to use videodb in python. IMPORTANT: Whenever user wants to write code related to videos, youtube videos or VideoDB specifically, always call this tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Primary @mcp.tool handler for the 'code_assistant' tool. Fetches VideoDB SDK documentation/context from a URL and returns it as a string.
    @mcp.tool(
        name="code_assistant",
        description="Will give you data related to VideoDB SDK which allows developers to use videodb in python. IMPORTANT: Whenever user wants to write code related to videos, youtube videos or VideoDB specifically, always call this tool.",
    )
    def code_assistant() -> str:
        try:
            response = requests.get(CODE_ASSISTANT_TXT_URL)
            response.raise_for_status()
            return response.text
        except requests.exceptions.RequestException as e:
            return f"Error: Unable to fetch data from URL. Details: {str(e)}"
  • @mcp.resource handler for 'code_assistant', providing context for creating video applications using VideoDB by fetching from the same URL.
    @mcp.resource(
        "videodb://code_assistant",
        name="code_assistant",
        description="Context for creating video applications using VideoDB",
    )
    def code_assistant() -> str:
        try:
            response = requests.get(CODE_ASSISTANT_TXT_URL)
            response.raise_for_status()
            return response.text
        except requests.exceptions.RequestException as e:
            return f"Error: Unable to fetch data from URL. Details: {str(e)}"
  • Re-exports code_assistant function along with others in __all__ for package-level access.
    from videodb_director_mcp.main import (
        call_director,
        play_video,
        code_assistant,
        doc_assistant,
    )
    
    
    __all__ = ["call_director", "play_video", "code_assistant", "doc_assistant"]
  • Constant defining the URL (CODE_ASSISTANT_TXT_URL) from which code_assistant fetches its content.
    CODE_ASSISTANT_TXT_URL = "https://videodb.io/llms-full.txt"
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 the tool 'will give you data' but doesn't disclose behavioral traits such as what type of data (e.g., read-only, generated code), potential side effects, authentication needs, or rate limits. The description is insufficient 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.

Conciseness3/5

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

The description is two sentences and front-loaded with the purpose, but the second sentence is repetitive ('always call this tool') and could be more structured. It's concise but lacks optimal efficiency in phrasing.

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 has no parameters, annotations, but an output schema exists, the description is moderately complete. It states the purpose and usage but lacks details on what data is provided, behavioral context, or how it differs from siblings, leaving gaps in understanding the tool's full scope.

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 no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, earning a baseline score of 4 for tools with no parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool provides 'data related to VideoDB SDK' and is for 'code related to videos, youtube videos or VideoDB specifically', which gives a general purpose. However, it's vague about what specific data or actions it provides (e.g., examples, documentation, code snippets) and doesn't clearly distinguish from sibling tools like 'doc_assistant' or 'play_video'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool: 'Whenever user wants to write code related to videos, youtube videos or VideoDB specifically, always call this tool.' This provides clear, directive guidance on usage context, though it doesn't mention when not to use it or alternatives.

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

Related 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/video-db/agent-toolkit'

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