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quanttogo-mcp-servers

confirm_signal

Confirm or skip pending trading signals by choosing EXECUTE to trigger virtual trades or SKIP to ignore them.

Instructions

Confirm or skip a pending trading signal. EXECUTE will trigger virtual trade execution, SKIP will mark it as skipped.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signalIdYesThe signal ID to confirm
decisionYesEXECUTE to trade, SKIP to ignore

Implementation Reference

  • Main MCP tool handler for confirm_signal in signals-server. Defines the tool with zod schema (signalId: string, decision: 'EXECUTE' | 'SKIP') and implements the async handler that calls client.confirmSignal() and returns success/error messages.
    server.tool(
      'confirm_signal',
      'Confirm or skip a pending trading signal. EXECUTE will trigger virtual trade execution, SKIP will mark it as skipped.',
      {
        signalId: z.string().describe('The signal ID to confirm'),
        decision: z.enum(['EXECUTE', 'SKIP']).describe('EXECUTE to trade, SKIP to ignore'),
      },
      async ({ signalId, decision }) => {
        try {
          const result = await client.confirmSignal(signalId, decision);
    
          if (result.code !== 0) {
            return { content: [{ type: 'text', text: `Error: ${result.message || 'Failed to confirm signal'}` }] };
          }
    
          return {
            content: [{
              type: 'text',
              text: `Signal ${signalId} ${decision === 'EXECUTE' ? 'executed' : 'skipped'} successfully.\n${JSON.stringify(result.data, null, 2)}`,
            }],
          };
        } catch (error: any) {
          return { content: [{ type: 'text', text: `Error confirming signal: ${error.message}` }] };
        }
      }
    );
  • HTTP server MCP tool handler for confirm_signal. Similar implementation with zod schema validation and async handler that calls client.confirmSignal() and returns JSON results.
    server.tool(
      'confirm_signal',
      'Confirm or skip a pending trading signal',
      {
        signalId: z.string().describe('Signal ID'),
        decision: z.enum(['EXECUTE', 'SKIP']).describe('EXECUTE or SKIP'),
      },
      async ({ signalId, decision }) => {
        const result = await client.confirmSignal(signalId, decision);
        return { content: [{ type: 'text', text: JSON.stringify(result.data || result, null, 2) }] };
      }
    );
  • Core API client method confirmSignal() that makes the actual HTTP POST request to '/confirmSignal' endpoint with signalId and decision parameters.
    async confirmSignal(signalId: string, decision: 'EXECUTE' | 'SKIP') {
      return this.call('/confirmSignal', { signalId, decision });
    }
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 that EXECUTE 'will trigger virtual trade execution' and SKIP 'will mark it as skipped', which covers basic outcomes. However, it omits critical details like whether this is a mutating operation (implied but not stated), permission requirements, side effects, or error handling, leaving significant gaps for a tool that likely modifies state.

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 and front-loaded, consisting of just two sentences that directly explain the tool's function and parameter meanings without any wasted words. Every sentence earns its place by clarifying purpose and usage, making it highly efficient.

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 the tool's complexity (likely a state-modifying operation with financial implications), no annotations, and no output schema, the description is incomplete. It fails to address key aspects such as the nature of 'virtual trade execution' (e.g., simulated vs. real), confirmation consequences, error scenarios, or return values, leaving the agent with insufficient context for safe and effective use.

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%, so the schema already fully documents both parameters (signalId and decision with enum). The description adds minimal value beyond the schema by restating the enum meanings ('EXECUTE to trade, SKIP to ignore'), which is redundant. No additional context or constraints are provided, meeting the baseline for high schema coverage.

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

Purpose5/5

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 specific verbs ('confirm or skip') and resource ('pending trading signal'), distinguishing it from sibling tools like get_signal_stats and get_trading_signals which are read-only operations. It explicitly defines what happens for each decision option, making the purpose unambiguous.

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 implies usage context by mentioning 'pending trading signal' and the two decision options, suggesting it should be used when a signal requires action. However, it lacks explicit guidance on when to choose EXECUTE vs. SKIP, prerequisites for use, or comparisons to alternatives, leaving some ambiguity for the agent.

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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