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MikeyBeez

MCP Contemplation

by MikeyBeez

start_contemplation

Initiate continuous background cognitive processing to maintain thoughts, recognize patterns, and develop insights between conversations.

Instructions

Start the background contemplation loop

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Core implementation in ContemplationManager.start(): spawns the Python contemplation subprocess, sets up stdout listener to capture insights, and handles errors.
    async start(): Promise<string> {
      if (this.subprocess) {
        return 'Contemplation loop already running';
      }
    
      try {
        this.subprocess = spawn('python3', [this.contemplationPath], {
          cwd: path.dirname(this.contemplationPath),
          stdio: ['pipe', 'pipe', 'pipe'],
          detached: false
        });
    
        this.subprocess.stdout?.on('data', (data) => {
          try {
            const lines = data.toString().split('\n').filter((line: string) => line.trim());
            for (const line of lines) {
              const response = JSON.parse(line);
              if (response.has_insight) {
                this.insights.push({
                  id: response.thought_id,
                  thought_type: response.thought_type,
                  content: response.insight,
                  significance: response.significance || 5,
                  timestamp: new Date().toISOString(),
                  used: false
                });
              }
            }
          } catch (e) {
            // Not JSON, ignore
          }
        });
    
        this.subprocess.on('error', (err) => {
          console.error('Contemplation process error:', err);
        });
    
        // Give it a moment to start
        await new Promise(resolve => setTimeout(resolve, 1000));
        
        return 'Contemplation loop started successfully';
      } catch (error) {
        throw new Error(`Failed to start contemplation: ${error}`);
      }
    }
  • MCP CallToolRequestSchema handler case: delegates to ContemplationManager.start() and returns the result as text content.
    case 'start_contemplation': {
      const result = await contemplation.start();
      return {
        content: [{ type: 'text', text: result }],
      };
    }
  • src/index.ts:376-383 (registration)
    Registers the tool in ListToolsRequestSchema response: defines name, description, and input schema.
    {
      name: 'start_contemplation',
      description: 'Start the background contemplation loop',
      inputSchema: {
        type: 'object',
        properties: {},
      },
    },
  • Input schema definition: empty object since no parameters are required.
    inputSchema: {
      type: 'object',
      properties: {},
    },
Behavior2/5

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

With no annotations, the description carries the full burden but only states the action without disclosing behavioral traits. It doesn't mention side effects, permissions, rate limits, or what 'background contemplation loop' does, leaving significant gaps for a tool that likely initiates a process.

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 a single, efficient sentence with no wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 tool name suggesting a process initiation, the description is incomplete. It lacks details on what the 'contemplation loop' entails, expected outcomes, or how it interacts with siblings, making it inadequate for informed use.

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 tool has 0 parameters with 100% schema coverage, so no parameter information is needed. The description doesn't add parameter details, but this is acceptable given the lack of parameters, aligning with the baseline for 0 params.

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's purpose with a specific verb ('Start') and resource ('background contemplation loop'), but it doesn't differentiate from its sibling 'stop_contemplation' or explain what 'contemplation loop' entails. It's clear but lacks sibling differentiation.

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 on when to use this tool versus alternatives like 'stop_contemplation' or other siblings. The description implies usage for starting a process but doesn't specify context, prerequisites, or exclusions.

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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