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open-code-review

open-code-review

by raye-deng
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

heal_code

Apply AI-suggested fixes to code issues by loading source files and generating repair prompts for automated correction.

Instructions

Load a file's source code and prepare a repair prompt for the AI agent. The agent (you) should then apply the fix based on the issue description and suggestion. Returns the file content along with the repair context.

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": {
      "path": "<path>",
      "issue": "<issue>"
    },
    "name": "heal_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesFile path to heal
issueYesIssue description to fix
suggestionNoSuggested fix from OCR scan

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 full burden. It discloses that the tool loads code and prepares a repair prompt, but lacks details on behavioral traits such as permissions needed, whether it modifies the file (implied by 'heal' but not confirmed), error handling, rate limits, or what 'repair context' entails. This leaves significant gaps for a tool with potential mutation implications.

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 action in the first sentence. Both sentences earn their place by explaining the tool's function and the agent's role. However, it could be slightly more structured by explicitly separating tool behavior from agent instructions.

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

Completeness2/5

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

Given no annotations and no output schema, the description is incomplete. It mentions returns 'file content along with the repair context' but doesn't detail the output format or behavioral aspects like side effects. For a tool named 'heal_code' with potential mutations, more context on safety, response structure, and error cases is needed.

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 schema fully documents parameters (path, issue, suggestion). The description adds marginal value by implying parameters are used to 'prepare a repair prompt' and 'apply the fix,' but doesn't provide additional syntax, format, or usage details beyond what the schema already specifies. Baseline 3 is appropriate here.

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: 'Load a file's source code and prepare a repair prompt for the AI agent.' It specifies the verb ('load' and 'prepare'), resource ('file's source code'), and outcome ('repair prompt'). However, it doesn't explicitly differentiate from sibling tools like 'explain_issue' or 'scan_diff', which might also involve code analysis or repair contexts.

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 guidance on when to use this tool. It mentions the agent should 'apply the fix based on the issue description and suggestion,' implying usage for code repair scenarios, but offers no explicit when-to-use vs. alternatives, prerequisites, or exclusions compared to siblings like 'scan_directory' or 'explain_issue'.

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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