Loading ToolBench
MCP server for memory management, task execution, project context, and tool discovery. Provides persistent memory storage with semantic search, task lifecycle management, checkpoint tracking, and intelligent tool routing.
SloplessCode exposes 29 tools with significant quality gaps. Most tools lack complete input schemas, only 8 tools show explicit parameter definitions in the provided code. Descriptions are present but often generic (e.g., 'Store a memory entry', 'Get contextual memory information'). No output schemas are documented. Tool naming is inconsistent: memory_* tools use verb_noun correctly, but registry_*, model_*, and report_* tools are vague (e.g., 'registry_best', 'model_available'). Error handling and recovery guidance are absent. Parameter descriptions lack constraints (ranges, enums, formats). No evidence of idempotency markers, permission gates, or audit trail patterns. The server appears to be a memory/task management system but lacks the structured, LLM-optimized definitions required for reliable agent composition.
Ingest and process a directory into memory
Ingest and process a file into memory
List available tool families and their descriptions
Store multiple memory entries in a single batch operation
Clean up and optimize memory storage
Get contextual memory information
Delete a memory entry by ID
21 of 29 tools lack visible input schemas. Tools like memory_tree_slice, memory_context, registry_best, system_info have no documented parameters, making it impossible for LLMs to construct valid calls.
Generic, under-20-character descriptions on 15+ tools (e.g., 'Get contextual memory information', 'Check if a model is available'). These lack actionable context for LLM tool selection and violate the 10 - 1024 character baseline.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 47 | 2026-07-28+ | v2 |
Retrieve a specific memory by ID
Check the health status of the memory system
Retrieve recently stored memories
Search stored memories using semantic similarity
Get statistics about memory storage and usage
Store a memory entry with semantic embedding and metadata
Retrieve a slice of the memory tree structure
Check if a model is available
Find open task/improvement artifacts whose task has a strict completed/done checkpoint. Defaults to report-only mode. Set close=true only after reviewing candidates.
Record the outcome of a memory-related operation
Get the best matching registry entry
List registry components
Update a registry entry
Report that a limit has been reached
Record a compact task checkpoint across planning, execution, blockers, interruptions, handoff, and completion. Use this to keep task progress recoverable across sessions and to leave a durable trail before the agent stops.
Persist an operator review for a completed checkpoint's next_step scope. Use after reconcile_completed_checkpoints returns needs_next_step_scope.
Persist multiple completed-checkpoint next_step scope reviews in one call. This records review annotations only; it never closes artifacts.
Get system information and diagnostics
Get detailed explanation of a specific tool
Get tools in a specific family with optional depth control
Record feedback after using a testing-stage MCP tool. Use this as the standardized evaluation envelope for a tool run: what was tested, what worked, what friction you hit, what should change, and whether the tool looks ready to promote or needs redesign.
Get tool recommendations for a specific task
No output schemas documented for any tool. LLMs cannot plan downstream calls or extract required fields (e.g., what does memory_search return? Is it a list of objects with id/content/score fields?). This violates the response-shaper pattern.
Vague tool names (registry_best, model_available, report_limit_hit) do not clearly convey action or intent. 'registry_best' could mean get, search, or rank. 'model_available' is passive; should be 'check_model_availability' or 'get_available_models'.
No error handling guidance. Tools like memory_delete (destructive) and ingest_file (I/O) lack recovery hints, retry classification, or confirmation patterns. Agents cannot distinguish retryable from fatal errors.