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smart-coding-mcp

smart-coding-mcp

by cbuntingde
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

propose_fix

Combine an issue summary with similar past lessons to generate a fix sketch.

Instructions

Suggest a fix sketch by combining the issue with k similar past lessons.

This is deterministic text-stitching — no LLM is called. The orchestrator (which has the LLM) reads the result and decides whether to apply.

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": {
      "issue_summary": "<issue_summary>"
    },
    "name": "propose_fix"
  }
}

Input Schema

NameRequiredDescriptionDefault
kNoNumber of past lessons to draw on.
issue_summaryYesPlain-English description of the current issue.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.2/5.0
Behavior4/5

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

Despite no annotations, the description explicitly states that the tool is deterministic text-stitching with no LLM call, and that the orchestrator reads the result and decides on application. This provides good behavioral context beyond a simple 'suggest fix' statement, though it omits details about failure modes or output format (but output schema exists).

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: two sentences, no redundant phrases. The first sentence states the core purpose, the second adds essential behavioral transparency. Every word earns its place.

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

Completeness4/5

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

Given the simple tool (2 parameters, output schema present), the description adequately covers purpose and behavior. It does not explain error handling or details of the output, but the output schema can fill that gap. Adequate for its complexity.

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?

With 100% schema description coverage, the description adds little beyond the schema: it reiterates that k is the number of past lessons and issue_summary is a plain-English description. No additional parameter semantics are provided.

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 tool's verb ('Suggest') and resource ('a fix sketch') by combining the issue with past lessons. This distinguishes it from siblings like recall_lessons or analyze_path, which retrieve lessons or analyze paths rather than synthesizing fixes.

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 explains when to use this tool (to get a fix sketch based on an issue and past lessons) and clarifies that it is deterministic and the orchestrator decides whether to apply. However, it does not explicitly list alternatives or when not to use it.

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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