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zebbern

agloop-mcp

by zebbern

agloop_get_state

Retrieve the current AgLoop state including phase, tasks, iteration, and compaction context. Returns null when no active loop exists.

Instructions

Read the full AgLoop state including phase, tasks, iteration, and compaction context. Returns null if no active loop.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The `agloop_get_state` tool handler function, which uses `StateManager` to retrieve and return the full AgLoop state.
    @mcp.tool()
    def agloop_get_state() -> str:
        """Read the full AgLoop state including phase, tasks, iteration, and compaction context. Returns null if no active loop."""
        state = _sm().get_state()
        if not state:
            return json.dumps({"error": "No active AgLoop state found"})
        return json.dumps(asdict(state), indent=2)
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the return behavior (returns null if no active loop), which is useful context beyond basic functionality. However, it lacks details on permissions, rate limits, or error conditions, leaving gaps in behavioral transparency for a state-reading tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose and includes essential behavioral detail (returns null condition). Every word earns its place with no redundancy or fluff, making it highly concise and well-structured.

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 the tool's low complexity (0 parameters, no annotations, but has an output schema), the description is reasonably complete. It explains what the tool does and a key behavioral trait (null return). The output schema likely covers return values, so the description doesn't need to detail them. It could improve by addressing usage context or error handling, but it's adequate for this simple tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to compensate for any parameter gaps, making it straightforward. A baseline of 4 is appropriate as it doesn't add param info but doesn't need to.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Read' and the resource 'full AgLoop state', specifying what information is included (phase, tasks, iteration, compaction context). It distinguishes from siblings like agloop_get_compaction_context or agloop_get_task by indicating it returns comprehensive state. However, it doesn't explicitly contrast with all siblings, keeping it at 4 rather than 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing, or compare to siblings like agloop_get_plan or agloop_get_next_task. The only implicit context is that it reads state, but no explicit usage instructions are given.

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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