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twining_promote

Promote provisional decisions to active status after validation through implementation and testing. Confirm decisions that have been successfully verified.

Instructions

Promote one or more provisional decisions to active status. Use this to confirm provisional decisions that have been validated through implementation and testing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
decision_idsYesIDs of provisional decisions to promote to active
promoted_byNoWho is promoting (default: "main")

Implementation Reference

  • The business logic implementation for the 'promote' operation, which updates provisional decisions to 'active' status.
    async promote(
      decisionIds: string[],
      promotedBy?: string,
    ): Promise<{
      promoted: string[];
      already_active: string[];
      not_found: string[];
      wrong_status: Array<{ id: string; status: string }>;
    }> {
      const result: {
        promoted: string[];
        already_active: string[];
        not_found: string[];
        wrong_status: Array<{ id: string; status: string }>;
      } = {
        promoted: [],
        already_active: [],
        not_found: [],
        wrong_status: [],
      };
    
      for (const id of decisionIds) {
        const decision = await this.decisionStore.get(id);
        if (!decision) {
          result.not_found.push(id);
          continue;
        }
    
        if (decision.status === "active") {
          result.already_active.push(id);
          continue;
        }
    
        if (decision.status !== "provisional") {
          result.wrong_status.push({ id, status: decision.status });
          continue;
        }
    
        await this.decisionStore.updateStatus(id, "active");
        result.promoted.push(id);
      }
    
      // Post a single status entry if any were promoted
      if (result.promoted.length > 0) {
        await this.blackboardEngine.post({
          entry_type: "status",
          summary:
            `Promoted ${result.promoted.length} provisional decision(s) to active`.slice(
              0,
              200,
            ),
          detail: `Decision IDs: ${result.promoted.join(", ")}`,
          scope: "project",
          agent_id: promotedBy ?? "main",
        });
      }
    
      return result;
    }
  • The MCP tool registration for 'twining_promote', which delegates the request to the DecisionEngine 'promote' method.
    // twining_promote — Promote provisional decisions to active
    server.registerTool(
      "twining_promote",
      {
        description:
          "Promote one or more provisional decisions to active status. Use this to confirm provisional decisions that have been validated through implementation and testing.",
        inputSchema: {
          decision_ids: z
            .array(z.string())
            .min(1)
            .describe("IDs of provisional decisions to promote to active"),
          promoted_by: z
            .string()
            .optional()
            .describe('Who is promoting (default: "main")'),
        },
      },
      async (args) => {
        try {
          const result = await engine.promote(
            args.decision_ids,
            args.promoted_by,
          );
          return toolResult(result);
        } catch (e) {
          if (e instanceof TwiningError) {
            return toolError(e.message, e.code);
          }
          return toolError(
            e instanceof Error ? e.message : "Unknown error",
            "INTERNAL_ERROR",
          );
        }
      },
    );
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that decisions are 'provisional' and need 'validation through implementation and testing,' hinting at prerequisites, but lacks details on permissions, side effects (e.g., if promotion is irreversible), rate limits, or error handling. This leaves significant gaps for a mutation 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?

The description is concise with two sentences that directly address the tool's purpose and usage, with no wasted words. It's front-loaded, starting with the core action, though it could be slightly more structured by separating guidelines into a distinct section.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (a mutation operation with 2 parameters), lack of annotations, and no output schema, the description is moderately complete. It covers the basic purpose and implied usage but falls short in detailing behavioral aspects like permissions or return values, making it adequate but with clear gaps for effective agent 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 documents both parameters ('decision_ids' and 'promoted_by') adequately. The description adds no additional parameter semantics beyond what's in the schema, such as format examples or constraints, resulting in a baseline score of 3.

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 verb ('promote') and resource ('provisional decisions to active status'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'twining_acknowledge' or 'twining_verify' that might involve decision status changes, leaving room for ambiguity in sibling comparison.

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 implied usage guidelines by stating 'Use this to confirm provisional decisions that have been validated through implementation and testing,' which suggests when to use it. However, it doesn't explicitly mention when not to use it or name alternatives among siblings, such as 'twining_override' or 'twining_reconsider,' which could be relevant for decision modifications.

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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