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aayushsinghm16

harbor-mcp-server

Find a customer

harbor_find_customer
Read-onlyIdempotent

Search customers by company name, contact name, email, or exact ID. Returns customer details (ID, status, country) to resolve account references before further queries.

Instructions

Search customers by company name, contact name, email or id.

Use this to turn a human's phrasing ("the Kestrel account") into a customer id before calling harbor_customer_360.

Args:

  • query (string): partial name, partial email, or exact id

  • limit (number): 1-50, default 10

Returns JSON: { "matches": [ { "id": string, "company_name": string, "contact_name": string, "email": string, "country": string, "status": "active" | "churned", "signed_up_at": string } ], "count": number }

Emails are partially masked. Matching still works on the unmasked value, so searching "priya" finds her even though the response shows "pr***@...".

Returns an empty match list, not an error, when nothing matches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum matches to return.
queryYesPartial company name, contact name, email, or an exact customer id like cus_0042.
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint, but the description adds valuable context: email masking behavior, matching still works on unmasked values, and returning an empty match list instead of an error. These are non-obvious traits beyond what annotations convey.

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 well-structured and front-loaded: it states purpose, usage, arguments, return format, and edge-case behavior in a logical flow. Every sentence contributes useful information without redundancy. It is appropriately sized for a lookup tool with edge cases to clarify.

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

Completeness5/5

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

Given the tool's simplicity (2 params, no output schema) and rich annotations, the description covers all necessary aspects: input semantics, return structure, example usage context, and error behavior. It is complete enough for an agent to invoke the tool correctly without further assumptions.

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 input schema already provides full parameter descriptions (100% coverage), including the meaning of 'query' and 'limit'. The description largely repeats this information without adding new semantic detail. For example, both describe partial matches and exact ids. Thus it meets baseline but adds little beyond the schema.

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 searches customers by multiple criteria (company name, contact name, email, or id) with a specific verb ('Search'). It also distinguishes itself from sibling tools by explicitly positioning it as a pre-step for harbor_customer_360, making its 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use: 'Use this to turn a human's phrasing ... into a customer id before calling harbor_customer_360.' This provides a clear use case and differentiates from customer_360. It also notes the empty-list behavior, setting expectations for no-match cases.

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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