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redcon_run

Run shell commands, returning compressed, token-bounded output for LLM analysis. Preserves error details from test suites, git diffs, builds, and coverage.

Instructions

Run a shell command and return its output compressed for LLM consumption (pytest, git diff/status/log, builds, coverage and more). Use instead of a raw shell when output may exceed a screenful; token caps are hard and failures keep their essential detail. DISABLED by default; set REDCON_MCP_ENABLE_RUN=1 to enable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNoWorking directory.
commandYesFull command line, e.g. 'git diff HEAD'
quality_floorNoLowest acceptable detail levelcompact
timeout_secondsNoKill the command after this many seconds
remaining_tokensNoRemaining budget hint (drives compression aggressiveness)
max_output_tokensNoHard cap on tokens returned
semantic_fallbackNoEnable the LLMLingua-2 semantic compression fallback for commands that no schema-specific compressor recognised. Requires the optional redcon[heavy_compression] extra (torch + transformers + ~280 MB BERT-base checkpoint). Silently falls through to plain passthrough when the extra is missing.
prefer_compact_outputNoRewrite known commands to runner-native compact flags (pytest --tb=line, cargo --quiet, jest --reporter=basic) before spawning. Trades full tracebacks for ~60-80% upstream reduction on test-failure runs.
Behavior4/5

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

With no annotations provided, the description carries the full transparency burden. It discloses compression behavior, hard token caps, failure detail preservation, and the default-disabled status. However, it does not explicitly warn about potential side effects of arbitrary command execution (e.g., file modifications, network access), which is a notable omission for a command runner.

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?

The description is compact and front-loaded, starting with the core action and purpose. Each of the three sentences adds valuable information: what the tool does, when to use it, and a critical configuration note. No words are wasted.

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 the tool's complexity (8 parameters, no output schema, no annotations), the description provides a solid overview with purpose, usage criteria, and enablement. It does not fully describe the return format or handling of edge cases, but the high schema coverage compensates for missing parameter details. A brief caution about command side effects would improve completeness.

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?

The input schema provides 100% coverage for all parameters with detailed descriptions, so the baseline is 3. The tool description adds little beyond the schema; it mentions compression and token caps but does not elaborate on parameters like quality_floor or semantic_fallback. No additional semantics are provided.

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 clearly states the tool's function: 'Run a shell command and return its output compressed for LLM consumption,' naming specific use cases (pytest, git, builds, coverage). It distinguishes the tool from siblings that perform static repository analysis by emphasizing command execution and output compression.

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?

The description explicitly says 'Use instead of a raw shell when output may exceed a screenful,' giving a clear condition for when to choose this tool. It also notes the critical prerequisite 'DISABLED by default; set REDCON_MCP_ENABLE_RUN=1 to enable,' which is essential for correct invocation.

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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