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resolve_person

Resolve a person to a canonical AnchorID using email, name, company domain, or external identifiers. Returns match status, confidence score, and match reasons.

Instructions

Resolve a person to an AnchorID using email, name, company domain, or external identifiers (Slack/Google user IDs). Returns status (resolved | needs_review | not_found), confidence score, the canonical AnchorID, match reasons, and any ambiguous candidates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailNoPerson's email address
nameNoPerson's full name
company_entity_idNoResolved company AnchorID (UUID) for name+company matching
company_domainNoCompany domain for name+company matching
identifiersNoExternal system identifiers
min_confidenceNoMinimum confidence threshold (0-1)

Implementation Reference

  • src/tools.ts:126-169 (registration)
    Registration of the 'resolve_person' tool on the MCP server via server.tool(), including Zod schema definition for inputs (email, name, company_entity_id, company_domain, identifiers, min_confidence) and the handler callback.
    // ─── 3. resolve_person ───────────────────────────────────────────
    server.tool(
      "resolve_person",
      "Resolve a person to an AnchorID using email, name, company domain, " +
        "or external identifiers (Slack/Google user IDs). " +
        "Returns status (resolved | needs_review | not_found), " +
        "confidence score, the canonical AnchorID, match reasons, and any ambiguous candidates.",
      {
        email: z.string().optional().describe("Person's email address"),
        name: z.string().optional().describe("Person's full name"),
        company_entity_id: z
          .string()
          .optional()
          .describe("Resolved company AnchorID (UUID) for name+company matching"),
        company_domain: z
          .string()
          .optional()
          .describe("Company domain for name+company matching"),
        identifiers: z
          .object({
            slack_user_id: z.string().optional(),
            google_user_id: z.string().optional(),
            salesforce_contact_id: z.string().optional(),
            hubspot_contact_id: z.string().optional(),
            phone: z.string().optional(),
          })
          .optional()
          .describe("External system identifiers"),
        min_confidence: z
          .number()
          .min(0)
          .max(1)
          .optional()
          .describe("Minimum confidence threshold (0-1)"),
      },
      async (input) => {
        try {
          const data = await api.post("/resolve/person", input as Record<string, unknown>);
          return jsonContent(data);
        } catch (e) {
          return errorContent(e);
        }
      },
    );
  • Handler function for resolve_person: calls api.post('/resolve/person', input) and returns the JSON response, with error handling via try/catch.
    async (input) => {
      try {
        const data = await api.post("/resolve/person", input as Record<string, unknown>);
        return jsonContent(data);
      } catch (e) {
        return errorContent(e);
      }
    },
  • Zod input schema for resolve_person defining optional fields: email, name, company_entity_id, company_domain, identifiers (slack_user_id, google_user_id, salesforce_contact_id, hubspot_contact_id, phone), and min_confidence (0-1).
    {
      email: z.string().optional().describe("Person's email address"),
      name: z.string().optional().describe("Person's full name"),
      company_entity_id: z
        .string()
        .optional()
        .describe("Resolved company AnchorID (UUID) for name+company matching"),
      company_domain: z
        .string()
        .optional()
        .describe("Company domain for name+company matching"),
      identifiers: z
        .object({
          slack_user_id: z.string().optional(),
          google_user_id: z.string().optional(),
          salesforce_contact_id: z.string().optional(),
          hubspot_contact_id: z.string().optional(),
          phone: z.string().optional(),
        })
        .optional()
        .describe("External system identifiers"),
      min_confidence: z
        .number()
        .min(0)
        .max(1)
        .optional()
        .describe("Minimum confidence threshold (0-1)"),
    },
  • Helper functions jsonContent and errorContent used by the handler to format API responses as MCP tool content.
    /** Format the API response as MCP tool content. */
    function jsonContent(data: unknown) {
      return {
        content: [{ type: "text" as const, text: JSON.stringify(data, null, 2) }],
      };
    }
    
    /** Format an error as MCP tool content (isError flag). */
    function errorContent(err: unknown) {
      if (err instanceof ApiError) {
        const payload = {
          error: err.message,
          status_code: err.status_code,
          request_id: err.request_id,
          details: err.details,
        };
        return {
          content: [{ type: "text" as const, text: JSON.stringify(payload, null, 2) }],
          isError: true,
        };
      }
      return {
        content: [{ type: "text" as const, text: (err as Error).message ?? String(err) }],
        isError: true,
      };
    }
  • The ApiClient.post method that the handler delegates to, sending a POST request to the AnchorID REST API at /api/v1/resolve/person.
    async post<T = unknown>(path: string, body: Record<string, unknown>): Promise<T> {
      const requestId = this.generateRequestId();
      const res = await fetch(`${this.baseUrl}/api/v1${path}`, {
        method: "POST",
        headers: this.headers(requestId),
        body: JSON.stringify(body),
      });
      return this.parse<T>(res, requestId);
    }
Behavior4/5

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

With no annotations, the description carries the burden. It explains the resolution process, possible statuses (resolved, needs_review, not_found), confidence score, and output fields. It does not mention side effects or idempotency, but for a query-like tool this is sufficient.

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 that front-loads the action and lists key inputs and outputs. No wasted words.

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?

The description covers all input fields except min_confidence, but includes all important output fields (status, confidence, AnchorID, match reasons, ambiguous candidates). Given the lack of output schema, it provides sufficient context for agent understanding.

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 baseline is 3. The description adds general context about how parameters are used (e.g., email, name, company info) but does not provide additional per-parameter semantics beyond what the schema already contains.

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 specifies the tool resolves a person to an AnchorID using multiple identifiers (email, name, company domain, external IDs). It distinguishes from sibling tools like resolve_person_batch (batch variant) and resolve_company (different entity type).

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 states what the tool does but does not explicitly guide when to use it vs. alternatives like batch or other resolution tools. The context from sibling names provides some differentiation, but no direct when-not-to-use or exclusion criteria.

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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