Skip to main content
Glama
Sign In
open-code-review

open-code-review

by raye-deng
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

explain_issue

Explains code quality issues detected during review, providing detailed analysis, category context, and actionable fix guidance for developers to resolve problems.

Instructions

Explain a code quality issue detected by OCR. Returns detailed explanation, category context, and fix guidance for the AI agent to act on.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "issue": "<issue>"
    },
    "name": "explain_issue"
  }
}

Input Schema

NameRequiredDescriptionDefault
issueYesThe issue description to explain
fileNoFile path where the issue was found
lineNoLine number
severityNoSeverity: critical/high/medium/low/info
categoryNoIssue category
suggestionNoAuto-generated fix suggestion

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns explanations and guidance, but doesn't describe behavioral traits like whether it's read-only, if it has side effects, rate limits, or authentication needs. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, stating the core purpose in the first clause. It uses two sentences efficiently to cover what the tool does and what it returns. There's no wasted verbiage, though it could be slightly more structured by explicitly separating purpose from output.

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

Completeness3/5

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

Given the tool's moderate complexity (6 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return types but lacks details on behavioral traits, usage context relative to siblings, and output format specifics. With no output schema, the description should ideally explain return values more thoroughly.

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 description doesn't add any parameter-specific information beyond what's in the input schema. Since schema description coverage is 100%, the schema already documents all 6 parameters thoroughly. The baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract from the comprehensive schema documentation.

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?

The description clearly states the tool's purpose: 'Explain a code quality issue detected by OCR.' It specifies the verb (explain) and resource (code quality issue), and mentions the return content (detailed explanation, category context, fix guidance). However, it doesn't explicitly differentiate from sibling tools like 'heal_code' or 'scan_diff' which might handle similar issues.

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

Usage Guidelines2/5

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

The description provides minimal usage guidance. It implies this tool should be used when an AI agent needs to understand and act on a code quality issue, but it doesn't specify when to use this versus alternatives like 'heal_code' (which might fix issues) or 'scan_diff' (which might detect them). No explicit when/when-not instructions or prerequisites are mentioned.

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

Deploy Server

Other Tools