local-tool-reliability
A guide to diagnosing and stabilizing local-model tool execution by adjusting gateway settings, prompt size, and tool definitions.
Install
mkdir -p .claude/skills/local-tool-reliability && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/17931" && unzip -o skill.zip -d .claude/skills/local-tool-reliability && rm skill.zipInstalls to .claude/skills/local-tool-reliability
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Use when local Copilot/Qwen tool calls fail, malformed tool JSON appears, tools are skipped, or LLM Gateway agent mode is flaky.Key capabilities
- →Separate server health from agent-tool health
- →Check local-model gateway settings
- →Check Qwen llama-server launch settings
- →Trim the tool surface to reduce complexity
- →Reduce the prompt to isolate issues
How it works
This skill provides a diagnostic procedure to debug and stabilize local-model tool calling by systematically checking server health, gateway settings, Qwen launch configurations, and tool surface complexity.
Inputs & outputs
When to use local-tool-reliability
- →Troubleshoot tool-calling failures in local LLM setups
- →Diagnose malformed tool JSON output from local models
- →Optimize tool definitions to improve model reliability
- →Resolve connection issues between gateway agents and local servers
About this skill
Local Tool Reliability
Debug and stabilize local-model tool calling before changing application code.
Procedure
- Separate server health from agent-tool health.
- Confirm
llama-serveris running and/v1/modelsresponds. - Send a tiny direct chat-completions request to confirm basic generation.
- If the server is healthy but Copilot fails, suspect gateway, prompt size, or tool-set bloat next.
- Confirm
- Check local-model gateway settings.
github.copilot.llm-gateway.agentTemperature: 0.0for tool mode.- Start with
parallelToolCalling: falsefor Qwen. - Keep
enableToolCalling: trueunless isolating a basic chat failure. - Use a long enough
requestTimeoutfor large local prompts.
- Check Qwen llama-server launch settings.
- Use Jinja chat templates (
--jinja). - Preserve Qwen thinking when needed for multi-turn tool use (
preserve_thinking: true). - Use the
qwen3_codertool parser where available. - Keep KV cache at
q8_0when reliability matters.
- Use Jinja chat templates (
- Trim the tool surface.
- Disable MCP servers and extension tools the task does not need.
- Prefer one well-named tool over overlapping tools.
- Make argument names and types unambiguous.
- Use absolute paths.
- Prefer plain-text or search-replace edit formats over giant JSON payloads.
- Reduce the prompt before blaming the parser.
- Start a fresh chat and retry with a minimal reproduction.
Output Contract
Return:
- Whether the failure is server, gateway, parser, tool-set, or context-size.
- The smallest reliability setting change to try.
- A validation step.
- Whether the change trades speed for correctness.
References
When not to use it
- →When the issue is with application code logic
- →When basic chat generation is failing due to server unresponsiveness
- →When the problem is not related to local-model tool calling
Limitations
- →Focuses only on local-model tool calling issues
- →Does not address issues with application code
- →Requires manual execution of diagnostic steps
How it compares
This skill offers a structured troubleshooting guide specifically for local LLM tool-calling issues, which is more targeted than general debugging approaches.
Compared to similar skills
local-tool-reliability side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| local-tool-reliability (this skill) | 0 | 2mo | No flags | Intermediate |
| langsmith-fetch | 6 | 6mo | Review | Intermediate |
| clawhub | 25 | 2mo | Review | Intermediate |
| agent-swarm | 3 | 5mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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