MCP transport for loom — exposes the pipeline tools over stdio and assembles the runnable registry for multi-agent LLM workflows with finite-state-machine orchestration.
Five tools with basic naming and descriptions, but significant gaps in schema completeness and parameter documentation. Tool names follow verb_noun convention (submit_task, get_task_status, answer_gate, list_projects, get_project_config), which is good. However, input schemas lack critical details: no enum constraints for project identifiers, no format specifications, no validation ranges. Parameter descriptions are minimal (10-20 chars each). Output schemas are not documented anywhere in the provided source. Error handling and recovery guidance are absent. The server exposes task orchestration capabilities but lacks the rigor expected of production tools.
Provide user input or approval to answer a human gate in a paused task execution
Retrieve the configuration for a specific project
Retrieve the current status and progress of a submitted task
List all available projects configured in loom
Submit a task to the loom orchestration pipeline for execution by an agent
Output schemas not documented. No tool returns a documented structure, LLMs cannot plan downstream calls or extract required fields. Critical for composition.
Parameter descriptions are too brief (10-20 chars). 'The project identifier' and 'The task identifier' lack context on format, constraints, or how to obtain them. LLMs cannot infer whether to pass a name, UUID, or slug.
No enum constraints on project parameter. Free-form strings invite hallucinated project names. Should declare valid projects as an enum or reference list_projects discovery tool.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 49 | 2026-07-28+ | v2 |
No error handling guidance. Tools do not document what errors are retryable, what requires user intervention, or how to recover. Agents cannot self-correct on failure.
submit_task and answer_gate are write operations but lack confirmation/dry-run patterns. Agents could accidentally submit malformed tasks or provide wrong answers without a safety check.