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getsimba-ai

Simba MCP Server

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by getsimba-ai

Get Pipeline Run

get_pipeline_run
Read-onlyIdempotent

Retrieve one pipeline run's status, timestamps, version ID, and error details to confirm success or diagnose why a run failed, timed out, or never started.

Instructions

Get one run of an owned pipeline: {run_id, status (queued | running | succeeded | failed), started_at, finished_at (UTC), version_id, error_code, error}. On success version_id is the new saved version — pass it as pipeline_version_id to get_recipe_draft_template to build on the refreshed data. On failure error_code says why: execution_failed (a source or transform failed; error names it), no_output, timeout, interrupted or not_started (start it again), not_queued, owner_blocked, unexpected. Scheduled runs are polled the same way. Requires the create:models scope.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idYes
pipeline_refYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.12.0

TDQS

A4.1/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), yet the description adds genuinely new behavioral context: the create:models scope requirement, the meaning of each status value, and actionable error_code semantics (re-start on interrupted/not_started, error names the failing source/transform). This is far beyond what structured fields convey.

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?

Front-loaded with the returned shape, then next-step routing, error semantics, and scope. Dense but each clause earns its place; slightly packed with parenthetical enumerations.

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?

Given an output schema exists, the description needn't explain return values but does so helpfully, and it adds scope and error handling. The remaining gap is the undocumented parameter formats, which leaves the 0%-coverage schema slightly under-supported.

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

Parameters3/5

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

Schema description coverage is 0%, so the description carries the burden for pipeline_ref and run_id, but it never defines pipeline_ref's format (id vs slug) or where to obtain it. It only obliquely references run_id via the returned-object notation, so it partially but not fully compensates.

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 ('Get one run of an owned pipeline') and enumerates the returned fields, so the agent knows exactly what it retrieves. It does not explicitly differentiate itself from nearby siblings such as list_pipelines or list_pipeline_versions, leaving that gap to the reader.

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?

Gives clear workflow context: poll scheduled runs the same way, and on success route version_id into get_recipe_draft_template. It stops short of stating when to prefer this over alternatives like list_runs or list_pipeline_versions, so there are no explicit exclusions.

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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