autotask-dwh-mcp-server

List warehouse views

list_views

Read-onlyIdempotent

List Autotask Data Warehouse reporting views (wh_*) with column counts to find the right table. Filters by name substring; falls back to schema snapshot if database unavailable.

Instructions

List the Autotask Data Warehouse views (read-only reporting views, all named wh_*) with their column counts. Use this FIRST to discover which view holds the data you need, then describe_view for its columns and query to read rows. Source is the live INFORMATION_SCHEMA; if the DB is unreachable it falls back to the bundled schema snapshot (the 'source' field says which). Trap: there is NO wh_ticket view - tickets live in wh_task with project_id IS NULL. Optional case-insensitive substring filter on the view name. See warehouse://guide for the data model and how to resolve *_id columns to names.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`filter`	No	Case-insensitive substring to match view names, e.g. "time" matches wh_time_item and wh_time_subitem. Omit to list all views.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`count`	Yes
`views`	Yes
`source`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Aug 31, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already cover read-only, idempotent, non-destructive behavior, so the description earns credit for added context: live INFORMATION_SCHEMA with a snapshot fallback, the 'source' field indicating which, and the missing wh_ticket trap. This is useful behavioral detail beyond what structured fields provide.

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 dense but every sentence earns its place: purpose, first-use workflow, fallback behavior, trap, filter, and a pointer to the guide. It is front-loaded with the most important discovery intent before diving into caveats.

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?

Completeness is high for a read-only discovery tool with an output schema: it explains the discovery workflow, the fallback source, the filtering option, the critical missing-view trap, and how to resolve IDs. Nothing needed to call it correctly is left out.

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 says the filter is optional and case-insensitive and matches view names, but the schema already documents this plus an example, so the description adds no substantial new parameter meaning.

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?

States a specific verb 'List' plus the exact resource ('Autotask Data Warehouse views') and scope ('all named wh_*') and the key output ('column counts'). It clearly differentiates from siblings by mapping the subsequent steps to describe_view and query.

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 instructs 'Use this FIRST to discover which view holds the data you need, then describe_view for its columns and query to read rows', giving the agent a clear workflow over sibling tools. The trap about wh_ticket living in wh_task also steers the agent away from a common wrong lookup.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server