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The Compound Intelligence Engine for AI Development — MCP server + CLI with sessions, goals, real-time watcher, and smart guardrails
LoopSpec MCP exhibits significant definition quality gaps. While all 12 tools have descriptions and input schemas are present, the schemas lack proper type definitions for most parameters. Descriptions are verbose (200-400 chars) but often lack actionable guidance on WHEN to use each tool or what happens to system state. Parameter descriptions are minimal or absent. No documented output schemas. Error handling guidance is absent. Tool names use domain-specific prefixes (loopspec_*) rather than action verbs, making intent unclear. The server conflates multiple concerns within single tools (e.g., loopspec_daemon handles init, enable, disable, add-task, remove-task, tick, complete, status, 7 distinct actions in one tool). No evidence of idempotency guarantees or recovery guidance.
Takes a raw prompt and makes it dramatically better — adds thinking structure, spec context, guardrails, anti-patterns from past sessions, and success criteria. Use this before any complex task.
Save progress: score current files, check test gate, update goals, store progress.
Long-running loop that wakes up on schedule, runs tasks (security scans, drift checks, docs updates, tests), and learns without human involvement.
Log a non-trivial architectural/design decision with rationale. Persisted for future sessions.
Full deployment pipeline: build → test → lint → security → deploy → verify → monitor. Auto-rollback on failure.
Create, check, and update goal checklists. Auto-verifies goals against code patterns.
Tool names lack action verbs. All tools prefixed with 'loopspec_' (e.g., loopspec_daemon, loopspec_orchestrate) rather than verb_noun pattern (e.g., start_daemon, create_loop). LLMs cannot infer intent from domain-specific prefixes alone.
Multiple actions bundled into single tools. loopspec_daemon accepts 8 enum actions (init, enable, disable, add-task, remove-task, tick, complete, status); loopspec_orchestrate accepts 7 (create, spawn, start, complete, next, status, cancel); loopspec_swarm accepts 7 (init, add-agent, assign, report, orchestrate, status, pause). Each action should be a separate tool for clarity and composability.
No output schemas documented. Tool descriptions state what they do but do not specify what fields are returned, their types, or structure. LLMs cannot plan downstream tool calls or extract required data without knowing response format.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 44 | 2026-07-28+ | v2 |
Get context-aware help when stuck. Searches spec, playbook, past decisions, and common mistakes.
Higher-level loop that spawns and manages sub-loops. One loop can create child loops, forming a tree of autonomous work.
Predict what code should contain BEFORE writing it, then compare after. Actions: "expect" (pre-code predictions) or "compare" (post-code gap analysis).
Track development sessions with goals, scores, and progress. Actions: start, status, end, checkpoint, history.
Call this at the start of every session. Returns project state, active goals, behavioral guidance, and suggested next action. Zero parameters needed.
Multi-agent orchestration — spawn, coordinate, and manage parallel agent loops working toward a goal.
Parameter descriptions are minimal or absent. Most optional parameters (taskId, name, description, schedule, etc.) lack descriptions explaining what they control or when to use them. Descriptions like 'Task identifier (optional)' do not guide LLM usage.
No error handling or recovery guidance. Tool descriptions do not explain what errors can occur, how to interpret them, or what the LLM should do next. Irreversible operations (loopspec_deploy with IRREVERSIBLE risk) lack confirmation or dry-run patterns.