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Cloto-dev

CPersona

Official
by Cloto-dev

pause_persistence

Idempotent

Pause write operations across the MCP server for benchmarking, AB testing, or exploration. All write tools return dry-run responses, preventing database writes.

Instructions

Pause write operations on this MCP server for an opt-in TTL window. While paused, every write tool — store, archive_episode, update_memory, delete_memory, delete_episode, delete_agent_data, lock_memory, unlock_memory, update_profile, import_memories, merge_memories, calibrate_threshold, set_recall_precision — returns a no-op response carrying persisted: false, dry_run: true and a reason (with the TTL remaining) instead of writing to the database. persisted: false is the authoritative signal: branch on it, not on an id. Where the success shape has an id, it reads "no-persist" (store, archive_episode); action-specific id keys (deleted_id / updated_id / locked_id / unlocked_id / episode_id) are blanked to null so a truthy echo cannot read as success. migrate_channel_axis is gated differently — it is forced to dry_run and reports repairs_skipped rather than returning a skipped-response, so it carries no persisted key. check_health and deep_check are not blocked but downgrade to fix=false (they answer with repairs_skipped: true). Read tools (recall, list_*, get_profile, etc.) still answer normally, except that recall suppresses its recall_count / last_recalled_at bump — a write that would otherwise move ranking state during a paused session. The pause is PROCESS-WIDE, not per-session (response scope: "process"): the flag is one module-global on the server process. On a streamable-HTTP deployment a single process serves every connected client, so pausing here silences writes for ALL connected sessions until resume or TTL elapse — and the other sessions get no signal. Under stdio (one process per client) it is effectively session-scoped. This affects only this MCP server (cpersona); call cscheduler's pause_persistence too if you want both paused. Use for benchmarking, AB testing, or ephemeral exploration where memory contamination must be avoided. Default TTL: 1800 seconds (30 minutes); upper bound: 86400 seconds (1 day).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ttl_secondsNoTTL until automatic resume. Min 1, max 86400 (clamped). Default 1800.
Behavior5/5

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

The description goes far beyond the annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=false) by detailing the exact no-op response format (persisted: false, dry_run: true, reason), how specific tools behave differently (migrate_channel_axis, health checks), the process-wide scope, and the implications for multi-client deployments. It provides comprehensive behavioral context.

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

Conciseness4/5

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

The description is lengthy but well-structured with clear sections and formatting (bold, code). It front-loads the main action and provides detailed breakdowns. While some redundancy exists, every sentence earns its place given the complexity of the tool's behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's high complexity (affects many tools, different behaviors for different actions, process-wide scope, deployment variations), the description is extremely comprehensive. It covers all relevant aspects, including response format, scope, affected tools, and edge cases (migrate_channel_axis, health checks), leaving no significant gaps.

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 input schema has one parameter (ttl_seconds) with full coverage (description, default, min, max). The description adds context by specifying the default TTL (1800s), upper bound (86400s), and that the TTL remaining is included in the response reason. This adds value beyond the schema definition.

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

Purpose5/5

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

The description clearly states the verb 'Pause write operations on this MCP server for an opt-in TTL window.' It explicitly names the resource (write operations on the MCP server) and provides extensive detail about which tools are affected and how they respond, distinguishing it from siblings like resume_persistence and persistence_status.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool: 'Use for benchmarking, AB testing, or ephemeral exploration where memory contamination must be avoided.' It also contrasts with other tools (e.g., cscheduler's pause_persistence) and explains the scope differences between streamable-HTTP and stdio deployments, providing clear when-to and when-not-to guidance.

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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