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elb-pr

using-tool-executor

by elb-pr

Slim tool definitions. Auto-compressed responses. Context efficiency on both end. Ships with 7 example servers, reduces context consumption by 98% (48k tokens down to 1.1k).

41🍴 2📅 Jan 24, 2026

SKILL.md


name: using-tool-executor description: Use when a task needs capabilities BEYOND basic tools (Read/Grep/Glob/Bash) - semantic search, AI analysis, research, image generation, etc.

Use the right tool for the job:

TaskUse ThisNOT This
Read a fileRead toolMCP
Search for literal stringGrep toolMCP
Find files by patternGlob toolMCP
Run shell commandsBash toolMCP
Semantic code searchMCP (Serena)Grep
Refactor/rename symbolsMCP (Serena)Edit
Deep researchMCP (Gemini)WebSearch
AI-powered analysisMCP (Gemini)-
Generate images/videoMCP (Gemini)-
Library documentationMCP (Context7)WebFetch
Multi-step reasoningMCP (Sequential)-

Basic tools are great for basic tasks. MCP is for capabilities that don't exist in basic tools.

You don't know the exact tool names or schemas. You must discover them.

This is not negotiable. This is not optional. You cannot guess your way through MCP.

The Workflow

EVERY tool-executor interaction follows this sequence:

1. search_tools(query)     → Find relevant tools
2. get_tool_schema(name)   → Get exact parameters
3. execute_code(code)      → Run the tool
digraph tool_flow {
    "Need MCP capability" [shape=doublecircle];
    "Know exact tool name?" [shape=diamond];
    "search_tools(query)" [shape=box];
    "get_tool_schema(name)" [shape=box];
    "execute_code with tool" [shape=box];
    "Handle result" [shape=doublecircle];

    "Need MCP capability" -> "Know exact tool name?";
    "Know exact tool name?" -> "search_tools(query)" [label="NO (99% of cases)"];
    "Know exact tool name?" -> "get_tool_schema(name)" [label="YES (rare)"];
    "search_tools(query)" -> "get_tool_schema(name)";
    "get_tool_schema(name)" -> "execute_code with tool";
    "execute_code with tool" -> "Handle result";
}

Available MCP Categories

CategoryServerCapabilities
code-navSerena (28 tools)Symbol search, refactoring, code analysis, persistent memory
knowledgeContext7, NotebookLMLibrary docs lookup, notebook Q&A, research
ai-modelsGemini (37 tools)Deep research, brainstorming, image gen, video gen, structured output
reasoningSequential-thinkingMulti-step reasoning with thought chains
uishadcnComponent search, examples, implementation
webApifyWeb scraping, RAG browser, data extraction

Red Flags

When deciding WHETHER to use MCP:

ThoughtReality
"I'll use Serena to read this file"Just use Read tool. Serena is for semantic search.
"I need MCP to search for 'TODO'"Grep is fine for literal strings.
"Let me use Gemini to check the file"Read it yourself. Gemini is for analysis/research.

When you ARE using MCP:

ThoughtReality
"I remember the tool name"Tool names change. Search first.
"I know the schema"Schemas evolve. Get fresh schema.
"Let me just try execute_code"Without search/schema = guaranteed failure.
"I'll console.log the result"Large outputs truncate. Use workspace.
"Let me Read that _savedTo path"Workspace isn't filesystem. Use workspace.readJSON().
"search_tools is overhead"search_tools prevents 10x more overhead from failures.

Workspace Pattern (CRITICAL)

Large MCP responses are auto-saved to workspace. You receive:

{ _savedTo: "mcp-results/123.json", _preview: "..." }

DO NOT try to Read("mcp-results/123.json") - that path doesn't exist on the filesystem!

DO use execute_code to access it:

const data = await workspace.readJSON("mcp-results/123.json");
console.log(JSON.stringify(data, null, 2));

Best practice - save your own outputs too:

const result = await gemini["gemini-deep-research"]({ query: "..." });
// If result has _savedTo, it's already saved
if (result._savedTo) {
  const full = await workspace.readJSON(result._savedTo);
  // Process full data...
  await workspace.writeJSON("my-analysis.json", processedData);
  console.log("Saved to my-analysis.json");  // Minimal console output
}

Quick Reference

Use basic tools for:

  • Reading files → Read
  • Literal text search → Grep
  • Finding files by name → Glob
  • Shell commands → Bash
  • Simple file edits → Edit

Use MCP for (via search_tools first!):

  • "Find where X is defined" → Serena (find_symbol)
  • "Rename this function everywhere" → Serena (rename_symbol)
  • "What's the API for library Y?" → Context7 (query-docs)
  • "Research topic Z thoroughly" → Gemini (gemini-deep-research)
  • "Help me brainstorm" → Gemini (gemini-brainstorm)
  • "Analyse this code for issues" → Gemini (gemini-analyze-code)
  • "Generate an image" → Gemini (gemini-generate-image)
  • "Help me reason through this" → Sequential-thinking
  • "Build a UI component" → shadcn (search_components)
  • "Scrape data from website" → Apify (call-actor)

Checklist

Before ANY tool-executor usage:

  • Did I search_tools first? (not guess)
  • Did I get_tool_schema? (not assume)
  • Am I saving large outputs to workspace? (not console.log)
  • Am I using workspace.readJSON for _savedTo? (not Read tool)

The Rule

search_tools → get_tool_schema → execute_code

Every. Single. Time.

No exceptions. No shortcuts. No guessing.

Score

Total Score

70/100

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