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Product Manager MCP

Product Manager MCP

by r28ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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linear_customer_needs_list

Read-onlyIdempotent

List customer requests from Linear, filter by issue to see what work is wanted or by customer to see what a company asked for.

Instructions

List customer requests. Filter by issue to see what a piece of work is wanted for, or by customer to see everything one company has asked for.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "linear_customer_needs_list"
  }
}

Input Schema

NameRequiredDescriptionDefault
variablesNoPaging and filtering.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true, so the safety profile is covered. The description adds no behavioral context beyond that — nothing about pagination behavior, default page size, or archived-record handling, all of which the schema silently carries.

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?

Two tight sentences, front-loaded with the action and then the two filtering modes. Every clause carries information; nothing is wasted.

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?

For a read-only list tool with a fully documented nested filter schema and no output schema, the description covers purpose and filter intent adequately. It could still mention pagination (the `after`/`first` variables) to be fully self-sufficient, but the schema handles that.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description goes slightly beyond the schema by explaining the intent behind the issue and customer filters ('what a piece of work is wanted for', 'everything one company has asked for'), which helps an agent choose the right filter dimension.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('List customer requests') and immediately clarifies the two dimensions along which results can be sliced. It distinguishes the tool from sibling list tools like linear_customers_list and linear_customer_need_create, though it doesn't explicitly name an alternative.

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?

Gives concrete usage context for both filter paths: filter by issue to learn what a work item is wanted for, or by customer to see everything one company requested. This is clear implied when-to-use guidance, though it doesn't state when NOT to use the tool or name a competing sibling.

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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