YAML Workflow
PYPI · YAML-WORKFLOW
Expose declarative YAML-defined workflows as MCP tools over stdio
Available components
Last scanned (21 hours ago)
Scores higher than 99% of PyPI servers
Category Workflow Automation
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 0 of 3 dependencies flagged as unhealthy. View diagnostics → Pass
Provenance & Transparency100
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to orieg/yaml-workflow). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 78 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability81
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 587 tokens (~146/item across 4 items; 4 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- All 1 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation.Pass
- An AI judge read all 4 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 2 Nov 26 +1
- Stability: 0.97 → pass security
- 1 Nov 26 −1
- Stability: pass → 0.97 functional
- 30 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 26 Oct 26 +1
- Stability: 0.97 → pass security
- 24 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.
- 22 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.
- 20 Oct 26 −14
- Malware scan: pass → unverified ▼ security
- 19 Oct 26 +15
- Malware scan: unverified → pass ▲ security
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 4 Nov 2026 · Analysed pypi/yaml-workflow@0.9.6
Provenance Verified
A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.
| Result | Verified |
|---|---|
| Ecosystem | pypi |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | orieg/yaml-workflow |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/orieg/yaml-workflow/.github/workflows/publish.yml@refs/tags/v0.9.6 |
| Rekor log index | 2498547845 |
| Predicate type | PyPI publish attestation https://docs.pypi.org/attestations/publish/v1 |
| Subject digest | sha256:4b1a7ba29cf8915a538ca58473a5e9c87b18847e5b336d17b7cab3f0c97afa26 |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 3 packages
| Packages resolved | 3 |
|---|---|
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
dry_run_workflow ~196
Preview what a workflow would do without executing any task. Give a `workflow` (a name from list_workflows or a file path) and optional `params`; returns `{status, outputs, preview}` where `preview` is a human-readable list of the steps that would run with their resolved inputs (the same information as the CLI's --dry-run). Use this to inspect side effects (shell commands, file writes, HTTP calls) before running for real. It does not execute any task — no shell or Python runs and none of the workflow's own side effects occur; only ephemeral logs are written to a temporary workspace.
| Name | Type | Req | Description |
|---|---|---|---|
| params | object | – | Optional values for the workflow's declared inputs, as an object of name -> value. Omit to use each parameter's default. |
| workflow | string | yes | Workflow to target: either a name returned by list_workflows, or a path to a workflow YAML file. |
No output schema declared.
No examples provided.
list_workflows ~113
List the workflows available in this server's workflow directory. Returns an object with `count` and `workflows` (one entry per workflow, each containing `name` (its declared name), `description`, `path` (the YAML file), and `parameters` (declared inputs with types and defaults)). Call this first to discover which workflows exist and what inputs each accepts before calling dry_run_workflow or run_workflow. Read-only: it only reads YAML files and never executes anything. Takes no arguments.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
run_workflow ~167
Execute a workflow and return its results. Give a `workflow` (a name from list_workflows or a file path) and optional `params`; runs it to completion and returns `{status, workflow, outputs}` where `outputs` maps each step name to its result. DESTRUCTIVE: a workflow may run arbitrary shell commands and Python, write files, and make HTTP requests — call dry_run_workflow first if you need to preview side effects, and only run workflows you trust.
| Name | Type | Req | Description |
|---|---|---|---|
| params | object | – | Optional values for the workflow's declared inputs, as an object of name -> value. Omit to use each parameter's default. |
| workflow | string | yes | Workflow to target: either a name returned by list_workflows, or a path to a workflow YAML file. |
No output schema declared.
No examples provided.
validate_workflow ~111
Validate a single workflow YAML file without running it. Give the file `path`; returns `{valid, error_count, warning_count, issues[]}`, where each issue has a level (error/warning/info), message, and optional line, step, and hint. Use this to check a workflow the agent authored or edited before running it, or to explain why a workflow is malformed. Read-only: no tasks run and nothing is written.
| Name | Type | Req | Description |
|---|---|---|---|
| path | string | yes | Path to the workflow YAML file to validate. |
No output schema declared.
No examples provided.
How do I install the YAML Workflow MCP server?
YAML Workflow runs locally as a PyPI package, launched with uvx yaml-workflow. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · yaml-workflow
claude mcp add orieg-yaml-workflow -- uvx yaml-workflow
What is the YAML Workflow MCP server?
YAML Workflow is an MCP server listed in the public MCP registry as io.github.orieg/yaml-workflow. Expose declarative YAML-defined workflows as MCP tools over stdio. This page covers its PyPI package (yaml-workflow).
Is the YAML Workflow MCP server safe to use?
YAML Workflow scores 97 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 4 November 2026. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the YAML Workflow MCP server expose?
YAML Workflow exposes 4 tools: list_workflows, validate_workflow, dry_run_workflow, run_workflow. Their descriptions and schemas cost roughly 587 tokens of context every time the server is loaded.
Is the YAML Workflow MCP server still maintained?
YAML Workflow is still listed as active in the MCP registry. We last reached this channel on 4 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the YAML Workflow MCP server under?
YAML Workflow declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.