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Claude Error Collector

Claude Error Collector

by henningziech
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

record_error

Capture user corrections to create persistent guidelines in CLAUDE.md, preventing repeated errors in future interactions.

Instructions

Record a correction from the user and save it as a learned rule in CLAUDE.md. Call this whenever the user corrects you.

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": {
      "error_description": "<error_description>",
      "correction": "<correction>",
      "rule": "<rule>"
    },
    "name": "record_error"
  }
}

Input Schema

NameRequiredDescriptionDefault
error_descriptionYesWhat was wrong
correctionYesWhat is correct
ruleYesDerived guideline, e.g. "ALWAYS use X instead of Y"
project_dirNoCurrent working directory (to find project CLAUDE.md)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It states the tool saves corrections as learned rules, implying persistence and mutation, but lacks details on permissions, file overwrite behavior, error handling, or response format. It adequately describes the core behavior but misses operational specifics.

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 two concise sentences that are front-loaded with the core purpose and usage trigger. Every word serves a clear function with zero waste, making it highly efficient and well-structured.

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 no annotations and no output schema, the description adequately covers the tool's purpose and usage but lacks details on behavioral traits like file I/O specifics, error responses, or system impacts. For a mutation tool with 4 parameters, it provides a functional overview but could be more complete.

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 all parameters. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or interdependencies. This meets the baseline for high schema coverage.

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 specific action ('Record a correction'), the resource ('save it as a learned rule in CLAUDE.md'), and the precise trigger ('whenever the user corrects you'). It distinguishes from the sibling 'list_errors' by focusing on recording rather than listing.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance with 'Call this whenever the user corrects you,' establishing a clear context for when to invoke this tool. It implicitly distinguishes from 'list_errors' by focusing on correction recording rather than error listing.

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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