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Red5d

Jupiter Broadcasting Podcast Data MCP Server

by Red5d

get_episode

Retrieve detailed information about specific podcast episodes from Jupiter Broadcasting shows by providing the show name and episode number.

Instructions

Get detailed information about a specific episode.

Args: show_name: Name of the show episode_number: Episode number

Returns: Episode data including title, description, hosts, enclosures, etc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
show_nameYes
episode_numberYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The primary MCP tool handler for 'get_episode', decorated with @mcp.tool(). It handles input validation via type hints and docstring, calls the RSS parser helper, and formats the response or error.
    @mcp.tool()
    def get_episode(show_name: str, episode_number: str) -> Dict[str, Any]:
        """Get detailed information about a specific episode.
        
        Args:
            show_name: Name of the show
            episode_number: Episode number
        
        Returns:
            Episode data including title, description, hosts, enclosures, etc.
        """
        try:
            episode = rss_parser.get_episode(show_name, episode_number)
            if episode:
                return episode
            else:
                return {"error": f"Episode '{episode_number}' not found in show '{show_name}'"}
        except Exception as e:
            return {"error": f"Failed to retrieve episode: {str(e)}"}
  • Core helper function in PodcastRSSParser that retrieves the specific episode from the cached RSS feed by matching GUID or podcast:episode number, then delegates parsing to _parse_episode.
    def get_episode(self, show_name: str, episode_number: str) -> Optional[Dict[str, Any]]:
        """Get specific episode data."""
        feed_root = self._get_feed(show_name)
        if feed_root is None:
            return None
        
        # Find all item elements (episodes)
        items = feed_root.xpath('//item')
        for item in items:
            # Check GUID
            guid_elem = item.find('guid')
            if guid_elem is not None and guid_elem.text == episode_number:
                return self._parse_episode(show_name, item)
            
            # Check podcast:episode number
            episode_elem = item.find('.//{https://podcastindex.org/namespace/1.0}episode')
            if episode_elem is not None and episode_elem.text == episode_number:
                return self._parse_episode(show_name, item)
        
        return None
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves information (implying read-only behavior) but lacks details on permissions, rate limits, error handling, or data freshness. The mention of returns like 'title, description, hosts, enclosures, etc.' hints at output structure but is vague, failing to fully compensate for the absence of annotations.

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 well-structured and appropriately sized, with a clear purpose statement followed by sections for Args and Returns. Each sentence adds value, though the 'Returns' section could be more specific (e.g., listing exact fields instead of 'etc.'). It avoids redundancy and is front-loaded with the core functionality.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (2 required parameters, no nested objects) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers purpose, parameters, and return types, though it lacks behavioral details like error cases or usage guidelines, which are minor gaps in this context.

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 0%, so the description must add meaning beyond the schema. It lists parameters ('show_name', 'episode_number') and briefly explains their roles, but doesn't provide format details (e.g., episode number as string vs. integer) or examples. This partially compensates for the schema gap but leaves ambiguity, aligning with the baseline for moderate compensation.

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 with a specific verb ('Get detailed information') and resource ('about a specific episode'). It distinguishes this from siblings like 'list_shows' (which lists shows) and 'search_episodes' (which searches across episodes), though it doesn't explicitly contrast with 'get_transcript' (which might retrieve transcript data for an episode).

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 provides no guidance on when to use this tool versus alternatives like 'search_episodes' or 'get_transcript'. It mentions retrieving 'detailed information' but doesn't specify scenarios where this is preferred over other tools, leaving the agent to infer usage from context alone.

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

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