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harbor-mcp-server

Describe a Harbor table

harbor_describe_table
Read-onlyIdempotent

Return the schema for one table—columns, types, nullability, primary key, and PII masking—so you can write accurate queries.

Instructions

Return the column list, types, nullability and primary key for one table.

Args:

  • table (string): one of customers, subscriptions, plans, invoices, tickets, usage_events

Returns JSON: { "table": string, "note": string, "columns": [ { "name": string, "type": string, "nullable": boolean, "primary_key": boolean, "masked": boolean } ] }

"masked" marks columns whose values are partially redacted on the way out.

Example: use before writing a query that filters on a column you have not seen yet. Error: returns an error listing valid tables if the name is not readable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYesName of the table to describe. Must be one of the readable tables.
Behavior5/5

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

The description discloses important behavioral details not captured by annotations: the 'masked' field indicates partially redacted values, and errors return a list of valid tables. It also details the exact return JSON. There is no contradiction with annotations.

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 with a one-line summary, labeled args, returns JSON, example, and error behavior. Every sentence earns its place and there is no redundant filler.

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?

Despite having no output schema in the tool definition, the description fully documents the return JSON structure, including column attributes and masking semantics. It also covers error behavior and an example usage, making it complete for a 1-parameter tool.

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 fully describes the parameter with an enum and description. The description repeats the allowed values but adds no extra semantic meaning beyond reminding the user it is 'one table.' Schema coverage is 100%, so baseline 3 is appropriate.

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's function: 'Return the column list, types, nullability and primary key for one table.' This is a specific verb+resource pairing and distinguishes the tool from siblings like harbor_list_tables and harbor_run_query.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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

The description provides a clear use case: 'Example: use before writing a query that filters on a column you have not seen yet.' This gives context for when to use the tool, though it does not explicitly contrast with alternatives or mention exclusions.

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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