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claude-engram

claude-engram

by 20alexl
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

scout_analyze

Analyze code by providing code and a specific question to a local LLM. Receive targeted answers about code behavior, errors, or improvements.

Instructions

Analyze code with local LLM. Provide code and question.

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": {
      "code": "<code>",
      "question": "<question>"
    },
    "name": "scout_analyze"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYes
questionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

C2.9/5.0
Behavior3/5

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

The description discloses that analysis uses a local LLM, which is a key behavioral trait (privacy, dependency). However, it does not mention potential side effects, limitations, or authorization requirements. With no annotations, this is minimally adequate.

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?

Two sentences with no wasted words. However, it could be more structured (e.g., bullet points) to improve readability.

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?

No output schema or annotations provided. The description does not mention return values, error handling, or usage context. For a simple tool, it leaves significant gaps in understanding what the tool produces.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description restates parameter names ('code and question') without adding meaning beyond the schema. With schema description coverage at 0%, the description fails to compensate by explaining constraints, formats, or examples.

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 verb 'analyze' and resource 'code with local LLM'. It is specific but does not differentiate from siblings like code_pattern_check or code_quality_check, which also analyze code.

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 on when to use this tool versus alternatives. The description only instructs to provide code and question, lacking context for selection.

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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