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MCP Chaining Skill for Claude

Research-to-implement pipeline chaining 5 MCP tools with graceful degradation

1k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
521
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/vibeeval/vibecosystem --skill mcp-chaining

The instruction itself

16 sections, as written by the author

MCP Chaining Pipeline

A research-to-implement pipeline that chains 5 MCP tools for end-to-end workflows.

When to Use

  • Building multi-tool MCP pipelines
  • Understanding how to chain MCP calls with graceful degradation
  • Debugging MCP environment variable issues
  • Learning the tool naming conventions for different MCP servers

What We Built

A pipeline that chains these tools:

| Step | Server | Tool ID | Purpose |

|------|--------|---------|---------|

| 1 | nia | nia__search | Search library documentation |

| 2 | ast-grep | ast-grep__find_code | Find AST code patterns |

| 3 | morph | morph__warpgrep_codebase_search | Fast codebase search |

| 4 | qlty | qlty__qlty_check | Code quality validation |

| 5 | git | git__git_status | Git operations |

Key Files

  • scripts/research_implement_pipeline.py - Main pipeline implementation
  • scripts/test_research_pipeline.py - Test harness with isolated sandbox
  • workspace/pipeline-test/sample_code.py - Test sample code

Usage Examples

# Dry-run pipeline (preview plan without changes)
uv run python -m runtime.harness scripts/research_implement_pipeline.py \
    --topic "async error handling python" \
    --target-dir "./workspace/pipeline-test" \
    --dry-run --verbose

# Run tests
uv run python -m runtime.harness scripts/test_research_pipeline.py --test all

# View the pipeline script
cat scripts/research_implement_pipeline.py

Critical Fix: Environment Variables

The MCP SDK's get_default_environment() only includes basic vars (PATH, HOME, etc.), NOT os.environ. We fixed src/runtime/mcp_client.py to pass full environment:

# In _connect_stdio method:
full_env = {**os.environ, **(resolved_env or {})}

This ensures API keys from ~/.claude/.env reach subprocesses.

Graceful Degradation Pattern

Each tool is optional. If unavailable (disabled, no API key, etc.), the pipeline continues:

async def check_tool_available(tool_id: str) -> bool:
    """Check if an MCP tool is available."""
    server_name = tool_id.split("__")[0]
    server_config = manager._config.get_server(server_name)
    if not server_config or server_config.disabled:
        return False
    return True

# In step function:
if not await check_tool_available("nia__search"):
    return StepResult(status=StepStatus.SKIPPED, message="Nia not available")

Tool Name Reference

nia__search              - Universal documentation search
nia__nia_research        - Research with sources
nia__nia_grep            - Grep-style doc search
nia__nia_explore         - Explore package structure
ast-grep__find_code      - Find code by AST pattern
ast-grep__find_code_by_rule - Find by YAML rule
ast-grep__scan_code      - Scan with multiple patterns

morph (Fast Text Search + Edit)

morph__warpgrep_codebase_search  - 20x faster grep
morph__edit_file                 - Smart file editing

qlty (Code Quality)

qlty__qlty_check         - Run quality checks
qlty__qlty_fmt           - Auto-format code
qlty__qlty_metrics       - Get code metrics
qlty__smells             - Detect code smells

git (Version Control)

git__git_status          - Get repo status
git__git_diff            - Show differences
git__git_log             - View commit history
git__git_add             - Stage files

Pipeline Architecture

                    +----------------+
                    |   CLI Args     |
                    | (topic, dir)   |
                    +-------+--------+
                            |
                    +-------v--------+
                    | PipelineContext|
                    | (shared state) |
                    +-------+--------+
                            |
    +-------+-------+-------+-------+-------+
    |       |       |       |       |       |
+---v---+---v---+---v---+---v---+---v---+
| nia   |ast-grp| morph | qlty  | git   |
|search |pattern|search |check  |status |
+---+---+---+---+---+---+---+---+---+---+
    |       |       |       |       |
    +-------v-------v-------v-------+
                    |
            +-------v--------+
            | StepResult[]   |
            | (aggregated)   |
            +----------------+

Error Handling

The pipeline captures errors without failing the entire run:

try:
    result = await call_mcp_tool("nia__search", {"query": topic})
    return StepResult(status=StepStatus.SUCCESS, data=result)
except Exception as e:
    ctx.errors.append(f"nia: {e}")
    return StepResult(status=StepStatus.FAILED, error=str(e))

Creating Your Own Pipeline

  • Copy the pattern from scripts/research_implement_pipeline.py
  • Define your steps as async functions
  • Use check_tool_available() for graceful degradation
  • Chain results through PipelineContext
  • Aggregate with print_summary()

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How to use it

Copy the folder

Take vibeeval/mcp-chaining from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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