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bmorphism

Manifold Markets MCP Server

award_bounty

Reward insightful comments on prediction markets by allocating bounty funds to specific contributions using market and comment identifiers.

Instructions

Award bounty to a comment

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contractIdYesMarket ID
commentIdYesComment ID to award bounty to
amountYesAmount of bounty to award

Implementation Reference

  • Handler for 'award_bounty' tool: validates input with AwardBountySchema, requires MANIFOLD_API_KEY, POSTs to Manifold API /market/{contractId}/award-bounty with commentId and amount, returns success message on OK response.
    case 'award_bounty': {
      const params = AwardBountySchema.parse(args);
      const apiKey = process.env.MANIFOLD_API_KEY;
      if (!apiKey) {
        throw new McpError(
          ErrorCode.InternalError,
          'MANIFOLD_API_KEY environment variable is required'
        );
      }
    
      const response = await fetch(`${API_BASE}/v0/market/${params.contractId}/award-bounty`, {
        method: 'POST',
        headers: {
          'Content-Type': 'application/json',
          Authorization: `Key ${apiKey}`,
        },
        body: JSON.stringify({
          commentId: params.commentId,
          amount: params.amount,
        }),
      });
    
      if (!response.ok) {
        throw new McpError(
          ErrorCode.InternalError,
          `Manifold API error: ${response.statusText}`
        );
      }
    
      return {
        content: [
          {
            type: 'text',
            text: 'Bounty awarded successfully',
          },
        ],
      };
    }
  • Zod schema defining input parameters for award_bounty: contractId (string), commentId (string), amount (positive finite number).
    const AwardBountySchema = z.object({
      contractId: z.string(),
      commentId: z.string(),
      amount: z.number().positive().finite(),
    });
  • src/index.ts:373-385 (registration)
    Tool registration in listTools handler: defines name 'award_bounty', description, and inputSchema matching the Zod schema.
    {
      name: 'award_bounty',
      description: 'Award bounty to a comment',
      inputSchema: {
        type: 'object',
        properties: {
          contractId: { type: 'string', description: 'Market ID' },
          commentId: { type: 'string', description: 'Comment ID to award bounty to' },
          amount: { type: 'number', description: 'Amount of bounty to award' }
        },
        required: ['contractId', 'commentId', 'amount']
      }
    },
Behavior2/5

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

With no annotations provided, the description carries full burden but lacks behavioral details. It doesn't disclose whether this is a write operation (implied by 'award'), permission requirements, rate limits, or what happens after awarding (e.g., bounty transfer effects). This is inadequate for a mutation tool with zero annotation coverage.

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 zero wasted words. It's front-loaded with the core purpose, making it highly concise and well-structured for quick understanding.

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?

For a mutation tool (implied by 'award') with no annotations and no output schema, the description is incomplete. It doesn't explain return values, error conditions, or behavioral nuances, leaving significant gaps for an agent to operate safely and effectively.

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 parameters are documented in the schema. The description adds no additional meaning beyond the schema's parameter descriptions (e.g., clarifying what 'amount' represents or units). Baseline 3 is appropriate when the schema handles parameter documentation.

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 'Award bounty to a comment' clearly states the action (award) and target (bounty to a comment), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'add_bounty' or 'send_mana', which might involve similar bounty/currency operations.

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. The description doesn't mention prerequisites (e.g., needing a bounty to exist), exclusions, or relationships to sibling tools like 'add_bounty' or 'send_mana', leaving the agent without contextual usage cues.

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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