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claude-engram

claude-engram

by 20alexl
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

output

Detect fake or silent failures by validating code and output. Verify code for errors, and ensure output matches expected format, contains required content, and excludes prohibited items.

Instructions

Output validation. Operations:

  • validate_code: Check for fake/silent failures (code, context)

  • validate_result: Check output for fakes (output, expected_format, should_contain, should_not_contain)

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": "output"
  }
}

Input Schema

NameRequiredDescriptionDefault
operationYesOperation
codeNo
contextNo
outputNo
expected_formatNo
should_containNo
should_not_containNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations exist, and the description fails to disclose behavioral traits such as side effects, authorization needs, or return values. It is unclear whether validation failures raise errors or return results.

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 short and uses a clear bullet-point structure. It front-loads the purpose and efficiently lists operations. Minor improvement would be to add a return value note.

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 7 parameters, no output schema, and no annotations, the description is insufficient. It does not explain return values, error handling, or full parameter details, leaving significant gaps.

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 adds meaning for parameters used in each operation (e.g., validate_code uses code and context), but many parameters remain unexplained (e.g., context, expected_format syntax). With only 14% schema description coverage, the description partially compensates.

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 it performs output validation and lists specific operations with brief explanations. However, it does not differentiate from sibling tools like code_quality_check or code_pattern_check, which may overlap.

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?

No guidance is provided on when to use this tool versus alternatives, nor on choosing between validate_code and validate_result. The agent must infer usage.

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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