spark-sense-ai

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diagnose_spark_failure

Read-onlyIdempotent

Identify the root cause of an Apache Spark job failure by analyzing logs and source code from EMR or local paths, returning structured diagnosis with evidence and fix.

Instructions

Diagnose why an Apache Spark job failed.

Args: source_type: Where to fetch the log/code from - "emr" or "local". emr_cluster_id: EMR cluster ID (required if source_type="emr"). emr_step_id: EMR step ID (required if source_type="emr"). s3_project_location: Optional S3 URI to the job's source code (used only if source_type="emr"). local_log_path: Path to a local log file or folder (required if source_type="local"). local_project_path: Optional local folder containing the job's source code (used only if source_type="local"). job_entry_point: Optional filename/relative path of the specific job file that ran (e.g. "jobs/customer_order_join.py"). If given, ONLY this file is used as code context - skips auto-extraction entirely. Best used when the caller already knows which job failed. If omitted, the tool automatically parses the error log's stack trace (Python and/or Scala/Java patterns - handles mixed PySpark traces) to find the relevant file(s) in the project, up to 10 files, filtering out framework/library internals. This keeps large, multi-job projects from having their entire codebase sent to the model - only the code actually implicated by the failure is included. provider: Which LLM does the reasoning - "bedrock", "anthropic", "openai", or "none" (default). "none" returns the fetched log/code as-is, for the CALLING AGENT to diagnose itself - no LLM call is made by this tool in that case. api_key: API key for "anthropic" or "openai" providers. If omitted, reads from the ANTHROPIC_API_KEY / OPENAI_API_KEY environment variable. Not used for "bedrock" (uses locally configured AWS credentials) or "none".

Returns: A structured diagnosis (root cause, evidence, fix, confidence) if a provider is set, or the raw fetched log/code for the calling agent to analyze if provider="none".

Input Schema

TableJSON Schema

Name	Required	Default
`api_key`	No
`provider`	No	none
`emr_step_id`	No
`source_type`	Yes
`emr_cluster_id`	No
`local_log_path`	No
`job_entry_point`	No
`local_project_path`	No
`s3_project_location`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.3Sep 7, 2026

TDQS

A4.5/5.0

Behavior4/5

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

The annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false) are consistent with the description, which implies fetch-and-analyze operations with no side effects. The description adds transparency about the optional LLM call and the 'none' provider returning raw data, which is valuable context beyond the annotations. No contradictions found.

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?

The description is long but well-structured, with clear sections for parameters and returns. Each piece of information serves a purpose (e.g., explaining conditional requirements, provider options, and the detection algorithm). While slightly verbose, the complexity of the tool justifies the length. It is not padded with irrelevant details.

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?

Given the tool's complexity (multiple providers, conditional parameters, return type variations), the description is exceptionally complete. It covers return values for both provider and 'none' cases, explains the code extraction logic, and provides examples. No missing information that an agent would need to call this tool correctly.

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

Parameters5/5

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

Despite the schema having zero description coverage, the tool description explains every parameter in detail, including conditional requirements (e.g., 'required if source_type="emr"'), defaults, environment variable fallbacks, and the behavior of job_entry_point. It even includes an example and explains the auto-extraction logic. This fully compensates for the schema's lack of per-parameter descriptions.

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?

The description opens with a specific verb and object: 'Diagnose why an Apache Spark job failed.' This clearly distinguishes the tool from the sibling 'optimize_spark_performance' by focusing on failure diagnosis rather than performance tuning. The purpose is unambiguous and immediately actionable.

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?

The description provides thorough guidance on when to use the tool and how to configure it for different scenarios (source_type, provider, conditional parameters). It explains fallback behavior (env vars, 'none' provider) and the logic for auto-extraction. However, it does not explicitly contrast with the sibling tool, relying instead on the name difference. This is a minor gap, keeping it from a perfect score.

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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