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Ankra

by ai.ankra
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Rerun pipeline run

rerun_pipeline_run

Re-run an Ankra Pipelines run of a connected repository at the same commit. failed_only re-runs only the stages that failed (refused when nothing failed); full_run runs every stage, ignoring path filters. Address the repository with exactly one of repository_id, application_id, or owner and repo. Needs pipelines.operate, and pipelines.manage when the original run carried a spec supplied at dispatch.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "pipeline_run_id": "<pipeline_run_id>"
    },
    "name": "rerun_pipeline_run"
  }
}

Input Schema

NameRequiredDescriptionDefault
repoNoRepository name, with owner.
ownerNoRepository owner (organisation or user), with repo, instead of repository_id.
full_runNoRun every stage, ignoring path filters (default false).
providerNoNarrows owner and repo when the same repository is connected on two providers.
failed_onlyNoRe-run only the failed stages (default false).
repository_idNoThe connected repository's id, from list_pipeline_repositories.
application_idNoAn application whose linked repository to address, instead of repository_id.
pipeline_run_idYesThe run to re-run (its UUID, as get_pipeline_step_logs or watch_pipeline_run report it).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior5/5

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

Goes well past the annotations: it discloses that the rerun happens at the same commit, that failed_only can be refused when nothing failed, and the permission model (pipelines.operate, plus pipelines.manage when the original run carried a dispatch-time spec). Annotations only cover the read-only/idempotency/destructive profile, so this adds real operating context.

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

Conciseness5/5

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

Three sentences, front-loaded with the core action, and each carries distinct information: mode semantics, addressing rule, permission requirements. No filler or restatement of the tool name.

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

Completeness4/5

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

Covers addressing, mode selection, error case, and permissions for an 8-parameter mutation tool. The notable gap is outcome: with no output schema, the description never says whether a new run id is returned or how to follow the rerun, leaving the agent to discover the return shape.

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 a cross-parameter constraint the schema cannot express: exactly one of repository_id, application_id, or owner+repo must be supplied. It also restates the failed_only/full_run semantics, which is redundant with the schema.

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

Purpose4/5

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

States a specific verb and resource ('Re-run an Ankra Pipelines run') plus scoping detail ('of a connected repository at the same commit'), which separates it in spirit from run_pipeline. It never names run_pipeline, rerun_application_workflow, or watch_pipeline_run, so the differentiation is implied rather than explicit.

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

Usage Guidelines4/5

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

Clearly explains the two mode flags: failed_only re-runs only failed stages and is refused when nothing failed, full_run ignores path filters. It also states the exactly-one-of addressing rule. It gives no guidance on when to rerun versus starting a fresh run with run_pipeline.

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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