agent-loop-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| init_loopA | Creates a new .md loop state file for the given session to start a continuous, autonomous task. |
| log_stepA | Appends to the Active Context. Rejects if failed=true but no self_heal_strategy is provided. Warns if context is too large. |
| compact_memoryA | Empties the Active Context and appends the AI-provided summary to the Compacted History. |
| report_blockerA | Updates state to STATUS_BLOCKED and asks for human intervention when absolutely stuck. |
| resume_loopC | Removes the block and adds human input context back into the loop. |
| get_tool_suggestionsB | Ask this tool if you are stuck and don't know what other tools to use. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Agent Loop State | Reads the current active state and context for the autonomous agent loop. |
TDQS
Scored across 6 tools
Each tool has a distinct purpose: init starts a loop, log appends steps, compact clears memory, resume unblocks, report triggers human intervention, and get_tool_suggestions offers guidance. No overlapping functionality.
All tool names follow a consistent verb_noun pattern (init_loop, log_step, compact_memory, resume_loop, report_blocker, get_tool_suggestions), using snake_case throughout.
With 6 tools covering the essential loop lifecycle operations, the count is well-scoped for a specialized agent-loop management server.
The toolset provides comprehensive coverage for managing an autonomous loop: initialization, logging, memory compaction, resumption, blocker reporting, and suggestion retrieval. No obvious gaps in the workflow.