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list_recent

Retrieve recent episodes from the MCP Standards server to access current content and updates.

Instructions

List recent episodes

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results

Implementation Reference

  • Registration of the 'list_recent' tool in the list_tools handler, including its description and input schema (optional limit parameter).
    Tool(
        name="list_recent",
        description="List recent episodes",
        inputSchema={
            "type": "object",
            "properties": {
                "limit": {"type": "integer", "description": "Max results", "default": 10},
            },
        },
    ),
  • Core handler implementation for 'list_recent' tool. Queries SQLite episodes table for most recent entries ordered by timestamp DESC, limited by input limit (default 10), returns success, results list, and count.
    async def _list_recent(self, limit: int = 10) -> Dict[str, Any]:
        """List recent episodes"""
        try:
            with sqlite3.connect(self.db_path) as conn:
                conn.row_factory = sqlite3.Row
                cursor = conn.execute("""
                    SELECT id, name, content, source, timestamp
                    FROM episodes 
                    ORDER BY timestamp DESC
                    LIMIT ?
                """, (limit,))
                
                episodes = [dict(row) for row in cursor.fetchall()]
                
                return {
                    "success": True,
                    "results": episodes,
                    "count": len(episodes)
                }
        except Exception as e:
            return {"success": False, "error": str(e)}
  • Registration of the 'list_recent' tool in enhanced_server list_tools, with note about cost-efficient model routing.
    Tool(
        name="list_recent",
        description="List recent episodes (uses cost-efficient model)",
        inputSchema={
            "type": "object",
            "properties": {
                "limit": {"type": "integer", "description": "Max results", "default": 10},
            },
        },
    ),
  • Identical core handler implementation in enhanced_server.
    async def _list_recent(self, limit: int = 10) -> Dict[str, Any]:
        """List recent episodes"""
        try:
            with sqlite3.connect(self.db_path) as conn:
                conn.row_factory = sqlite3.Row
                cursor = conn.execute("""
                    SELECT id, name, content, source, timestamp
                    FROM episodes
                    ORDER BY timestamp DESC
                    LIMIT ?
                """, (limit,))
    
                episodes = [dict(row) for row in cursor.fetchall()]
    
                return {
                    "success": True,
                    "results": episodes,
                    "count": len(episodes)
                }
        except Exception as e:
            return {"success": False, "error": str(e)}
  • Classification of 'list_recent' as SIMPLE task complexity for model routing to cost-efficient models.
    "list_recent": TaskComplexity.SIMPLE,
Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. 'List recent episodes' implies a read-only operation but doesn't specify whether it's paginated, sorted, or has rate limits. It mentions 'recent' but doesn't define the timeframe or ordering. For a tool with no annotations, this leaves significant behavioral gaps.

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

Conciseness5/5

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

The description is extremely concise at three words, with zero wasted language. It's front-loaded with the core action and resource. Every word earns its place, making it efficient for quick scanning by an agent.

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?

Given no annotations, no output schema, and a simple parameter, the description is incomplete. It doesn't explain what 'recent' means, how results are returned, or how this differs from sibling tools. For a tool in a server with multiple episode-related tools, more context is needed to guide proper usage.

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%, with the 'limit' parameter fully documented in the schema. The description doesn't add any parameter semantics beyond what's in the schema—it doesn't explain how 'limit' interacts with 'recent' or provide usage examples. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.

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 'List recent episodes' clearly states the verb ('List') and resource ('recent episodes'), providing basic purpose. However, it doesn't differentiate from sibling tools like 'search_episodes' or specify what 'recent' means (timeframe, recency criteria). The purpose is clear but lacks specificity that would help distinguish it from alternatives.

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?

No guidance is provided about when to use this tool versus alternatives like 'search_episodes' or 'get_learned_preferences'. The description implies usage for listing recent episodes but doesn't specify scenarios, prerequisites, or exclusions. Without any contextual direction, the agent must infer usage from the tool name 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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