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woodpecker-mcp

by RikiGomes
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TypeScript
Remote

List pipelines

list_pipelines
Read-only

List a repository's Woodpecker CI pipelines in reverse chronological order, paged and filtered by branch, event, or status to find runs for pull request branches. Returns summary entries, not logs.

Instructions

Lists pipelines for one repository, newest first, paged (default 10 per page, max 50). Each entry has number, status, event, branch, first line of the commit message, short commit SHA, author, ISO created time, duration_s and pipeline errors if any; get_pipeline gives the web URL. Filters are passed through to Woodpecker; branch matches exactly. Use them to find e.g. the runs for a pull request branch. Returns no workflow, step or log detail; use get_pipeline for that.

Input Schema

NameRequiredDescriptionDefault
pageNoPage number (default 1).
repoYesRepository as "owner/name" (e.g. "acme/webapp") or a numeric Woodpecker repo id.
eventNoEvent filter: push, pull_request, tag, release, deployment, cron, manual.
branchNoExact branch filter.
statusNoStatus filter: pending, running, success, failure, error, killed, canceled, blocked, declined, skipped.
instanceYesWoodpecker instance name. Configured instances: default.
per_pageNoResults per page (default 10, max 50).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.2.0
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / page / maximum
      Added value: +9007199254740991
  2. First observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations cover the safety profile (readOnlyHint, openWorldHint), and the description adds substantial non-structured context: sort order (newest first), paging defaults (10 per page, max 50), that filters are passed through to Woodpecker, that `branch` matches exactly, and the enumerated fields returned per entry including errors and duration_s.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is one dense block rather than separated sentences, but it is front-loaded with scope and paging, then return fields, then filters, then exclusions. Nearly every clause carries routing or behavioral information; the field enumeration is the only part bordering on verbose for a description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by enumerating the returned entry fields, and it closes the loop on the alternatives for deeper data. An agent has everything needed to call this correctly on the first attempt.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds genuinely useful semantics: filter pass-through behavior to the upstream service and the exact-match semantics of `branch` (as opposed to a substring match), plus the effective page-size bounds. It doesn't explain `instance` or event/status enums, presumably because the schema already does.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource with scope: 'Lists pipelines for one repository, newest first, paged'. It also distinguishes itself from get_pipeline by declaring what it does not return, letting an agent separate it from the detail-oriented sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes the agent: 'Returns no workflow, step or log detail; use get_pipeline for that' and 'get_pipeline gives the web URL'. It also names a concrete use case ('find e.g. the runs for a pull request branch') and covers the filter pass-through condition.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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