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loop

Track file edits and test results to detect and prevent death spirals in AI coding sessions. Check safety before editing.

Instructions

Loop detection to prevent death spirals. Operations:

record_edit: Log file edit (file_path, description)
record_test: Log test result (passed, error_message)
check: Check if safe to edit (file_path)
status: Get edit counts and warnings
reset: Clear all loop tracking for a fresh start

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": {
      "operation": "<operation>"
    },
    "name": "loop"
  }
}

Input Schema

Name	Required	Description
`operation`	Yes	Operation
`file_path`	No	File being edited
`description`	No	For record_edit: what changed
`passed`	No	For record_test: did tests pass
`error_message`	No	For record_test: error if failed

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Apr 26, 2026

TDQS

A3.6/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It describes behaviors for each operation (log, check, clear) but lacks details on statefulness, side effects, or what 'death spirals' entails. Could be more explicit about mutability and persistence.

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 extremely concise: one sentence stating purpose followed by a clear bullet list of operations. No unnecessary words, and critical information is front-loaded.

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?

While the operation list is helpful, the description omits expected return values or outputs for each operation (e.g., what does 'check' return? 'status' returns counts?). With no output schema, this gap is significant for an AI agent.

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 input schema already describes all parameters with 100% coverage. The description adds operational context (e.g., which parameters apply to which operation) but does not significantly enhance understanding beyond the schema.

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?

The description clearly states the purpose as 'Loop detection to prevent death spirals' and enumerates specific operations (record_edit, record_test, check, status, reset) with a brief explanation for each, making it distinct from sibling tools.

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?

The description implies usage contexts via operations (e.g., record_edit when editing, check before editing), but does not explicitly state when to use this tool versus alternatives or provide any exclusions.

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