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smart-coding-mcp

smart-coding-mcp

by cbuntingde
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

health_check

Return a structured JSON snapshot for monitoring server health and readiness, including database status, schema version, and lessons count.

Instructions

Return a structured JSON snapshot for monitoring.

Fields: ok boolean — always true if the store could be opened schema_version stored schema_version current_schema version this code expects wal_mode WAL / journal-mode status db_path absolute path to the SQLite file db_size_bytes file size on disk lessons_total row count in the lessons table conventions_path where AGENTS.md lives conventions_writable bool — whether AGENTS.md can be appended to

Use this for liveness/readiness checks, not for hot-path validation.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "health_check"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.4/5.0
Behavior4/5

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 ok field is 'always true if the store could be opened' and describes all output fields. It implies no side effects, which is appropriate for a health check.

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 well-structured with a concise overview and a bullet list of fields. Every sentence adds value, but the field list could be slightly more compact.

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

Completeness5/5

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

Given zero parameters and an existing output schema (as per context), the description fully explains the output and the condition for the ok field. It is complete for a health check tool.

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

Parameters4/5

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

The input schema has no parameters, so baseline is 4. No parameter description needed.

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 states it returns a structured JSON snapshot for monitoring, lists fields, and explicitly distinguishes from siblings by stating 'Use this for liveness/readiness checks, not for hot-path validation.'

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

Usage Guidelines4/5

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

The description explicitly states 'Use this for liveness/readiness checks, not for hot-path validation,' providing clear context on when to use. However, it does not mention specific alternative tools for other scenarios.

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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