agentl
PYPI · AGENTL-ETL
Validate, plan, preview and run YAML data pipelines (ETL with LLM steps) and debug failed runs.
Available components
Last scanned (5 hours ago)
Scores higher than 78% of PyPI servers
Category Data and Analytics
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 6 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 arimayuji/agentl). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 1 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability65
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 580 tokens (~44/item across 13 items; 13 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management0
- Stability not yet verified: not enough scan history yet (needs a 30-day window).Unverified
Tool Coverage90
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 69% of tool parameters carry a description.Partial
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 13 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 13 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
Unverified: 1 category
A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.
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.
- 16 Oct 26 79
First indexed and scored.
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 19 Oct 2026 · Analysed pypi/agentl-etl@0.9.0
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 | arimayuji/agentl |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/arimayuji/agentl/.github/workflows/release.yml@refs/tags/v0.9.0 |
| Rekor log index | 3272260458 |
| Predicate type | PyPI publish attestation https://docs.pypi.org/attestations/publish/v1 |
| Subject digest | sha256:3291bd9a1514f76e4283e29a8898b2b7be43725969cf055a764ae2c1808f9746 |
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 6 packages
| Packages resolved | 6 |
|---|---|
| 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 →
diagram ~33
The pipeline as a Mermaid diagram.
| Name | Type | Req | Description |
|---|---|---|---|
| pipeline | string | yes | path to a pipeline YAML file, or the YAML text itself |
No output schema declared.
No examples provided.
doctor ~21
Explain why a run failed.
| Name | Type | Req | Description |
|---|---|---|---|
| run_id | string | yes | – |
No output schema declared.
No examples provided.
get_run ~27
A run with its events (the last 200).
| Name | Type | Req | Description |
|---|---|---|---|
| run_id | string | yes | – |
No output schema declared.
No examples provided.
lint ~58
Validate plus best-practice warnings (missing budget, delivery with no check, secrets in the YAML, unused steps). JSON findings with line, code and fix.
| Name | Type | Req | Description |
|---|---|---|---|
| pipeline | string | yes | path to a pipeline YAML file, or the YAML text itself |
No output schema declared.
No examples provided.
list_connectors ~15
Installed connectors and their parameters.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_failures ~29
Recent failed runs, each with the failing step and its error.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | – |
No output schema declared.
No examples provided.
list_runs ~18
Recent runs.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | – |
No output schema declared.
No examples provided.
new_pipeline ~58
Write a validated pipeline YAML from a description (calls the LLM set by AGENTL_PROVIDER / AGENTL_MODEL, which costs a little). Returns the YAML; nothing is saved.
| Name | Type | Req | Description |
|---|---|---|---|
| description | string | yes | what the pipeline should do |
No output schema declared.
No examples provided.
pipeline_stats ~69
Track record per pipeline: success rate, durations, cost, failures grouped by reason and a per-day trend.
| Name | Type | Req | Description |
|---|---|---|---|
| pipeline | string | – | pipeline name; all when omitted |
| since | string | – | how far back, e.g. 7d, 12h (default 30d) |
No output schema declared.
No examples provided.
plan ~71
What a run would read, call and cost: records per step, cache hits, LLM calls, token and cost ranges, and what would be written. Reads the sources; never writes, never calls an LLM, never runs fn: code.
| Name | Type | Req | Description |
|---|---|---|---|
| pipeline | string | yes | path to a pipeline YAML file |
No output schema declared.
No examples provided.
preview_step ~70
The first records going into and out of one step, computed like plan (readers and transforms only). Use it to check a transform before running the pipeline.
| Name | Type | Req | Description |
|---|---|---|---|
| n | integer | – | default 5 |
| pipeline | string | yes | path to a pipeline YAML file |
| step | string | yes | – |
No output schema declared.
No examples provided.
run_pipeline ~78
Run a pipeline file and return its status, spend and how many records each step produced. Pipelines that run Python code (fn: steps, Python tools, local MCP servers) need the server started with --allow-code.
| Name | Type | Req | Description |
|---|---|---|---|
| no_cache | boolean | – | ignore cached agent answers |
| pipeline | string | yes | path to a pipeline YAML file |
No output schema declared.
No examples provided.
validate ~33
Check a pipeline without running it.
| Name | Type | Req | Description |
|---|---|---|---|
| pipeline | string | yes | path to a pipeline YAML file, or the YAML text itself |
No output schema declared.
No examples provided.
How do I install the agentl MCP server?
agentl runs locally as a PyPI package, launched with uvx agentl-etl. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · agentl-etl
claude mcp add arimayuji-agentl -- uvx agentl-etl
What is the agentl MCP server?
agentl is an MCP server listed in the public MCP registry as io.github.arimayuji/agentl. Validate, plan, preview and run YAML data pipelines (ETL with LLM steps) and debug failed runs. This page covers its PyPI package (agentl-etl).
Is the agentl MCP server safe to use?
agentl scores 79 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 19 October 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 agentl MCP server expose?
agentl exposes 13 tools: validate, diagram, lint, plan, preview_step, and 8 more. Their descriptions and schemas cost roughly 580 tokens of context every time the server is loaded.
Is the agentl MCP server still maintained?
agentl is still listed as active in the MCP registry. We last reached this channel on 19 October 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 agentl MCP server under?
agentl declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.