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list_risks

Read-onlyIdempotent

List project risks visible to the caller. Filters: projectId (optional), severity (LOW/MEDIUM/HIGH/CRITICAL), category (SCHEDULE/RESOURCE/BUDGET/SCOPE/EXTERNAL), includeDismissed (default false). Returns up to 50 risks per call, ordered by severity descending then most-recent first. Each risk carries url, its link in the Onplana web app.

Input Schema

NameRequiredDescriptionDefault
limitNoDefault 25, max 50.
categoryNo
severityNo
projectIdNoOptional - restrict to one project.
includeDismissedNoDefault false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / category / enum
      Previous value: -[
      -  "SCHEDULE",
      -  "RESOURCE",
      -  "BUDGET",
      -  "SCOPE"
      -]New value: +[
      +  "SCHEDULE",
      +  "RESOURCE",
      +  "BUDGET",
      +  "SCOPE",
      +  "EXTERNAL"
      +]
  2. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover read-only/idempotent/non-destructive safety, so the bar is lower, and the description adds real behavioral context: a 50-per-call cap, ordering (severity descending, then most-recent first), and the presence of a url field per risk. It does not say what happens when more than 50 risks exist (no pagination/cursor behavior described), which is the remaining gap.

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?

A single dense paragraph that is front-loaded with purpose, then filters, then return behavior, then output detail. Every clause carries information and there is no filler or repetition of the title.

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?

With no output schema, the description usefully documents return size, ordering, and the url field, which is more than most list tools provide. It still leaves pagination/overflow behavior and the limit parameter ambiguous, which matters for a capped list tool.

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?

With 60% schema coverage, the description compensates by naming the enum values for severity and category and the default for includeDismissed, adding meaning beyond the bare enum lists. However it omits the `limit` parameter entirely while claiming 'up to 50 risks per call', which can mislead an agent about the actual default of 25.

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 project risks') and adds a scope qualifier ('visible to the caller') that hints at permission-based filtering. It does not name the obvious siblings (list_org_risks, analyze_project_risks, compute_risk_patterns) to disambiguate project-scope from org-scope listing, so an agent must infer the distinction.

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?

Usage is implied by the filter list (projectId, severity, category, includeDismissed) rather than stated. There is no explicit 'use this when... / use X instead when...' guidance against sibling risk-listing or risk-analysis tools, leaving the agent to infer selection criteria on its own.

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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