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Diogenes Context Compression, Code Patching & Deterministic Solver MCP

Stack-Trace Root-Cause Frame Isolator & Drain Log Deduplicator

isolate_stacktrace_root_cause
Read-onlyIdempotent

Parses noisy CI/terminal logs and multi-language stack traces (Node/TS, Python, Rust, Go), strips node_modules/stdlib frames to isolate the innermost failing user-code frame, and clusters repetitive logs into Drain templates. Use when triage-debugging test failures, crash dumps, or repetitive server error logs; for compressing general source code files or prose transcripts, use prune_context_skeleton instead. Read-only edge call (32 KB, 60 RPM): accepts 'raw_log' ('runtime_hint' defaults to 'auto_detect'), supports '?demo=true' or L402 (50 sats), and returns HTTP 400 if invalid.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
raw_logYesRaw terminal output, CI failure log, or exception traceback to analyze.
runtime_hintNoRuntime stack format hint; defaults to 'auto_detect' when omitted.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
error_headlineYesExtracted primary exception class and error message.
root_cause_frameYesInnermost user-code stack frame (function_name, file, line, column).
root_cause_fingerprintYesDeterministic hash of the error class and root user frame.
deduplicated_log_templatesYesDrain-clustered log patterns with occurrence counts.
stripped_library_frames_countYesNumber of node_modules, stdlib, or internal frames filtered out.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), but the description adds operational facts beyond them: a 32 KB payload cap, 60 RPM rate limit, L402 payment (50 sats), a ?demo=true escape hatch, and an HTTP 400 failure mode. It also confirms runtime_hint defaults to auto_detect. This is meaningful operational context layered on top of the annotations.

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 core action, then usage, then operational constraints. It is dense but every clause carries information; the final sentence packs rate/payment/error details that are slightly terse but still earned.

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 and annotations are rich, the description covers supports-languages, input constraints, auth/payment modes, and error behavior. Return values are correctly left to the output schema. Only minor gaps remain around size-exceeded or rate-limited recovery guidance.

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 100% and both parameters are documented in the schema, so the description only restates the runtime_hint default (already in the schema) and adds no syntax or format guidance for raw_log. Baseline 3 is correct when the schema does the heavy lifting.

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 specific verbs and resources: parses CI/terminal logs and multi-language stack traces, strips node_modules/stdlib frames to isolate the innermost failing user-code frame, and clusters logs into Drain templates. It explicitly distinguishes itself from prune_context_skeleton, so an agent can route without opening either schema.

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?

Gives explicit when-to-use (triage-debugging test failures, crash dumps, repetitive server error logs) and names the alternative for the adjacent-but-different task (compressing source/prose → prune_context_skeleton). Both the selection condition and the exclusion are stated.

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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