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set_signal_preferences

Configure which financial data signals feed into your analysis by enabling or disabling specific sources like weather, political cycles, or DeFi metrics.

Instructions

Customize which signals feed into your reality check. Turn individual signal sources on or off. Regime and sentiment are always included. Preferences persist between sessions. Example: {weather: false, political_cycle: false} to disable those signals.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cycleNoInclude Bitcoin halving cycle data (default: true)
defiNoInclude DeFi TVL and health data (default: true)
macroNoInclude Fed rates, DXY, yield curve (default: true)
onchainNoInclude Bitcoin on-chain data (default: true)
narrativesNoInclude sector rotation and narrative data (default: true)
weatherNoInclude weather in financial centers (default: true)
political_cycleNoInclude presidential cycle data (default: true)
seasonalityNoInclude monthly/seasonal patterns (default: true)
macro_calendarNoInclude FOMC, CPI, options expiry dates (default: true)

Implementation Reference

  • The main logic for the set_signal_preferences tool, which calls the preference storage mechanism and generates user guidance.
    export function setSignalPreferencesTool(input: SetPreferencesInput): SetPreferencesResult {
      const stored = setPreferences(AGENT_ID, input);
    
      const enabled = Object.entries(stored.preferences)
        .filter(([, v]) => v === true)
        .map(([k]) => k);
      const disabled = Object.entries(stored.preferences)
        .filter(([, v]) => v === false)
        .map(([k]) => k);
    
      let guidance = `Signal preferences saved. ${enabled.length} signals enabled. `;
      if (disabled.length > 0) {
        guidance += `Disabled: ${disabled.join(', ')}. `;
      }
      guidance += 'These preferences apply to all future get_reality_check calls. Regime and sentiment are always included. Call set_signal_preferences again to change.';
    
      return {
        success: true,
        agent_id: AGENT_ID,
        preferences: stored.preferences,
        agent_guidance: guidance,
      };
    }
  • Type definitions for the input parameters of the set_signal_preferences tool.
    export interface SetPreferencesInput {
      cycle?: boolean;
      defi?: boolean;
      macro?: boolean;
      onchain?: boolean;
      narratives?: boolean;
      weather?: boolean;
      political_cycle?: boolean;
      seasonality?: boolean;
      macro_calendar?: boolean;
    }
  • src/index.ts:409-430 (registration)
    Registration of the set_signal_preferences tool with the MCP server, including its schema description and invocation logic.
    // ─── Tool: set_signal_preferences ───
    server.tool(
      'set_signal_preferences',
      'Customize which signals feed into your reality check. Turn individual signal sources on or off. Regime and sentiment are always included. Preferences persist between sessions. Example: {weather: false, political_cycle: false} to disable those signals.',
      {
        cycle: z.boolean().optional().describe('Include Bitcoin halving cycle data (default: true)'),
        defi: z.boolean().optional().describe('Include DeFi TVL and health data (default: true)'),
        macro: z.boolean().optional().describe('Include Fed rates, DXY, yield curve (default: true)'),
        onchain: z.boolean().optional().describe('Include Bitcoin on-chain data (default: true)'),
        narratives: z.boolean().optional().describe('Include sector rotation and narrative data (default: true)'),
        weather: z.boolean().optional().describe('Include weather in financial centers (default: true)'),
        political_cycle: z.boolean().optional().describe('Include presidential cycle data (default: true)'),
        seasonality: z.boolean().optional().describe('Include monthly/seasonal patterns (default: true)'),
        macro_calendar: z.boolean().optional().describe('Include FOMC, CPI, options expiry dates (default: true)'),
      },
      async (params) => {
        const gateError = gateTool('set_signal_preferences');
        if (gateError) return { content: [{ type: 'text' as const, text: gateError }] };
    
        const result = setSignalPreferencesTool(params);
        return { content: [{ type: 'text' as const, text: JSON.stringify(result, null, 2) }] };
      },
Behavior4/5

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

With no annotations provided, the description carries the full burden and successfully discloses key behavioral traits: preferences persist between sessions (statefulness), regime/sentiment cannot be disabled (immutable constraints), and signals can be individually toggled (mutation scope). It does not mention rate limits or return values, but covers the essential behavioral contract for a preferences tool.

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?

Four efficient sentences front-load the purpose (reality check customization), explain mechanism (on/off), state constraints (regime/sentiment), and provide persistence info with a concrete example. Every sentence earns its place, though the example sentence combines persistence declaration with example data which slightly impacts scannability.

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 nine well-documented boolean parameters (100% schema coverage) and no output schema, the description appropriately focuses on domain context (reality check integration) and behavioral constraints rather than repeating parameter documentation. It adequately explains what the tool affects and how settings behave over time, which is sufficient completeness for this complexity level.

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?

With 100% schema coverage, the baseline is 3. The description adds valuable context by explaining that regime and sentiment are omitted from parameters because they are always included—justifying the schema structure. It also maps parameters to the 'reality check' domain and provides a concrete JSON example that clarifies the boolean toggle semantics beyond the schema's 'Include...' descriptions.

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 customizes signal sources feeding into the user's 'reality check' (specific verb+resource combination). It scopes the tool to the reality-check feature, implicitly distinguishing it from siblings like set_alert or set_custom_strategy. However, it assumes familiarity with what 'reality check' means without referencing the sibling get_reality_check tool explicitly.

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

Usage Guidelines3/5

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

The description provides concrete constraints ('Regime and sentiment are always included') and an example usage pattern, which implies when certain parameters should be toggled. However, it lacks explicit guidance on when to use this versus set_alert or set_custom_strategy, and does not state prerequisites or when not to use the tool.

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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