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myco_immune

Run the substrate's lint dimensions to enforce contract invariants and report findings with severity, category, location, and message. Optionally auto-repair fixable issues.

Instructions

Run the substrate's 50 lint dimensions and report findings (severity + category + location + message per finding). Dimensions fall in four categories: mechanical (canon invariants, write-surface coverage, LLM-boundary), shipped (package version ↔ canon version parity), metabolic (raw-note backlog, stale integrated), semantic (graph connectedness, orphan detection). Per L2 homeostasis.md, this is the substrate's autonomic health check.

Use this: routinely during development (cheap, ~1-2s), at release time (R7 top-down layering checks); with fix=true to auto-repair mechanically-fixable findings (M2 entry-point, MB1 raw_backlog). Do NOT use this to validate external code hygiene — immune only checks substrate contract invariants, not application correctness (that's what pytest is for).

Side effects: none by default. With fix=true, applies safe repairs per each dimension's fixable contract: M2 creates missing entry-point stubs, MB1 can auto-assimilate raw notes over threshold. All fixes respect R6 write_surface. List-mode

  • explain-mode are pure read.

Returns: { exit_code, findings: [...], dimensions_run, skeleton_downgrade_applied, exit_on, fix, fixes: [...] }. exit_code 0 when no critical findings; non-zero otherwise.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dimensionsNoOptional subset of dimension IDs to run (e.g. ['M1','SE1','MB2']). When empty, runs all 46. Use to re-check specific dimensions after fixing their findings without the full suite's cost. IDs are case-sensitive; see myco_immune --list for the full enumeration.
fixNoWhen true, apply safe auto-repairs for fixable dimensions (M2 entry-point stub creation, MB1 raw-note promotion). Only dimensions with fixable=True in their class definition act; others remain informational. Respects R6 write_surface — a fix attempt that would write outside allowed paths is refused. Default false (report-only).
listNoWhen true, enumerate every registered dimension (id, category, default_severity, fixable, doc) without actually running them. Useful for discovery. Mutually incompatible with fix=true and explain= (mode-level exclusivity; CLI enforces).
explainNoWhen non-null, print the prose documentation (class docstring) for the named dimension and exit. Passing an unknown dimension id raises UsageError. Mutually incompatible with fix=true and list=true.
project_dirNoAbsolute path of the workspace / project whose Myco substrate this call targets. Overrides auto-discovery. When omitted, Myco resolves via MCP roots/list, then MYCO_PROJECT_DIR, then cwd — the substrate_pulse field in every response echoes which source answered.
Behavior5/5

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

With no annotations, the description fully describes side effects: none by default, safe repairs with fix=true, respecting R6 write_surface. It details the return value schema and explains the four categories of dimensions. No contradictions with 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?

The description is well-structured with clear front-loading of purpose. Each sentence adds value, though it is somewhat lengthy. Minor inconsistency in dimension count (50 vs 46) but otherwise efficient.

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 no output schema, the description covers return values, side effects, categories, and use cases thoroughly. It is complete for a complex tool, though the dimension count inconsistency and lack of explicit examples for some parameters are minor gaps.

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%, so baseline is 3. The description does not add significant meaning beyond the schema's already detailed parameter descriptions. It provides context on dimension categories but not direct parameter semantics improvement.

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 it runs the substrate's 50 lint dimensions and reports findings, distinguishing it as an autonomic health check for substrate contract invariants, not external code. The verb 'run' and resource are specific, and the description differentiates from siblings by focusing on internal vs. external code.

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?

Explicitly tells when to use: routinely during development and at release time, with fix=true for auto-repair. Also says what NOT to use it for (validating external code hygiene) and suggests pytest as an alternative.

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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