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dalboki

sonsuchup-mcp

by dalboki

add_alibi

Record an alibi for a person in a mystery case. Optionally include time range and set status to claimed, confirmed, or false.

Instructions

특정 인물에 알리바이를 추가합니다. status: claimed(주장)/confirmed(확인됨)/false(허위). 기본 claimed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
caseIdYes
personLocalIdYes
startNo시작 시각 (datetime-local)
endNo종료 시각 (datetime-local)
contentYes알리바이 내용
statusNo

Implementation Reference

  • Handler function for add_alibi tool. Fetches the case, finds the person by local ID, pushes a new alibi entry (with start, end, content, status) to that person's alibis array, saves the case, and returns success.
    handler: async (input: {
      caseId: string;
      personLocalId: number;
      start?: string;
      end?: string;
      content: string;
      status?: 'claimed' | 'confirmed' | 'false';
    }) => {
      const c = await fetchCase(input.caseId);
      const person = c.people.find((p) => p.id === input.personLocalId);
      if (!person) throw new Error(`인물 local id ${input.personLocalId}을(를) 찾을 수 없습니다.`);
      person.alibis = person.alibis ?? [];
      person.alibis.push({
        start: input.start ?? '',
        end: input.end ?? '',
        content: input.content,
        status: input.status ?? 'claimed',
      });
      await saveCase(c);
      return { ok: true, url: caseUrl(input.caseId) };
    },
  • Input schema for add_alibi tool using Zod validation. Requires caseId (UUID), personLocalId (int), content (non-empty string). Optional: start, end (datetime-local strings), status (claimed/confirmed/false).
    inputSchema: z.object({
      caseId: z.string().uuid(),
      personLocalId: z.number().int(),
      start: z.string().optional().describe('시작 시각 (datetime-local)'),
      end: z.string().optional().describe('종료 시각 (datetime-local)'),
      content: z.string().min(1).describe('알리바이 내용'),
      status: z.enum(['claimed', 'confirmed', 'false']).optional(),
    }),
  • src/tools.ts:219-252 (registration)
    Registration of the add_alibi tool in the tools array. Defines name, description, inputSchema, and handler for the MCP tool.
    {
      name: 'add_alibi',
      description:
        '특정 인물에 알리바이를 추가합니다. status: claimed(주장)/confirmed(확인됨)/false(허위). 기본 claimed.',
      inputSchema: z.object({
        caseId: z.string().uuid(),
        personLocalId: z.number().int(),
        start: z.string().optional().describe('시작 시각 (datetime-local)'),
        end: z.string().optional().describe('종료 시각 (datetime-local)'),
        content: z.string().min(1).describe('알리바이 내용'),
        status: z.enum(['claimed', 'confirmed', 'false']).optional(),
      }),
      handler: async (input: {
        caseId: string;
        personLocalId: number;
        start?: string;
        end?: string;
        content: string;
        status?: 'claimed' | 'confirmed' | 'false';
      }) => {
        const c = await fetchCase(input.caseId);
        const person = c.people.find((p) => p.id === input.personLocalId);
        if (!person) throw new Error(`인물 local id ${input.personLocalId}을(를) 찾을 수 없습니다.`);
        person.alibis = person.alibis ?? [];
        person.alibis.push({
          start: input.start ?? '',
          end: input.end ?? '',
          content: input.content,
          status: input.status ?? 'claimed',
        });
        await saveCase(c);
        return { ok: true, url: caseUrl(input.caseId) };
      },
    },
  • AlibiData type used to represent an alibi entry with start, end, content, and status fields.
    type AlibiData = { start: string; end: string; content: string; status: string };
Behavior2/5

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

No annotations are provided, so the description must disclose all behavioral traits. It only states it adds an alibi and lists status options, but does not mention consequences like overwriting existing alibis, validation rules, or permissions.

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 two sentences, with the main action in the first sentence and clarifying detail in the second. No wasted words.

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?

For a simple creation tool without output schema or annotations, the description covers purpose and status but lacks information on return values or post-conditions, making it moderately complete.

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?

The description adds value by explaining the status enum values and indicating the default. However, with 50% schema coverage, the description does not fully compensate for the lack of detail on other parameters like start and end formats.

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 'Add an alibi to a specific person' with verb and resource, and specifies the status enum, distinguishing it from siblings like add_person or add_record.

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 the default status but no explicit guidance on when to use this tool versus alternatives or prerequisites. The purpose is clear, but usage context is implied rather than stated.

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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