Skip to main content
Glama
Cloto-dev

CPersona

Official
by Cloto-dev
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

archive_episode

Archive a conversation episode with pre-computed summary, keywords, and resolved status for later recall. Pass agent ID and summary; channel, project, and session are optional.

Instructions

Archive a conversation episode with pre-computed summary, keywords, and resolved status. All LLM processing is performed by the caller.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channelNov2.4.22 conversation-channel tag (e.g. a Discord channel id). Default '' (= unscoped). Channel-scoped recall returns episodes whose channel matches; this powers the per-channel episodic loop.
historyNoOriginal conversation messages (used for start/end timestamp extraction; the episode embedding is computed from summary)
summaryYesEpisode summary (pre-computed by caller)
agent_idYesAgent identifier
keywordsNoSpace-separated keywords (pre-computed by caller)
resolvedNoWhether the topic was completed/concluded
project_idNov2.4.17 isolation axis. Omit or pass '' for the global pool. v2.5.1: pass '@auto' to resolve this agent's default from the server's operating context (the resolution is echoed as resolved_project_id; an unmapped agent yields operating_context_warning). bug-186: resolution requires a configured operating context. With none — the default, and equally the outcome of a sidecar that fails to parse — the sentinel is NOT resolved: it is stored and filtered as the literal project_id '@auto', resolved_project_id echoes '@auto', and no warning is raised. Read resolved_project_id before relying on the resolution.
session_keyNoOpaque session identity you declare: a partition hint, not authentication and not a data filter. Selects which no-persist pause applies to this call. Omit to share one bucket with every caller that omits it. Full text on recall.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv2.5.10
    • addedInput schema / properties / session_key
      Added value: +{
+  "default": "",
+  "description": "Opaque session identity you declare: a partition hint, not authentication and not a data filter. Selects which no-persist pause applies to this call. Omit to share one bucket with every caller that omits it. Full text on recall.",
+  "type": "string"
+}
  2. Changed1 schema field changedv2.5.2
    • changedInput schema / properties / project_id / description
      Previous value: -"v2.4.17 isolation axis. Omit or pass '' for the global pool."New value: +"v2.4.17 isolation axis. Omit or pass '' for the global pool. v2.5.1: pass '@auto' to resolve this agent's default from the server's operating context (the resolution is echoed as resolved_project_id; an unmapped agent yields operating_context_warning). bug-186: resolution requires a configured operating context. With none — the default, and equally the outcome of a sidecar that fails to parse — the sentinel is NOT resolved: it is stored and filtered as the literal project_id '@auto', resolved_project_id echoes '@auto', and no warning is raised. Read resolved_project_id before relying on the resolution."
  3. Changed1 schema field changedv2.5.1
    • changedInput schema / properties / history / description
      Previous value: -"Original conversation messages (used for timestamp extraction and embedding)"New value: +"Original conversation messages (used for start/end timestamp extraction; the episode embedding is computed from summary)"
  4. Changed2 schema fields changedv2.4.34
    • addedInput schema / properties / channel
      Added value: +{
+  "description": "v2.4.22 conversation-channel tag (e.g. a Discord channel id). Default '' (= unscoped). Channel-scoped recall returns episodes whose channel matches; this powers the per-channel episodic loop.",
+  "type": "string"
+}
    • addedInput schema / properties / project_id
      Added value: +{
+  "description": "v2.4.17 isolation axis. Omit or pass '' for the global pool.",
+  "type": "string"
+}
  5. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already signal that the operation is non-read-only, non-idempotent, and non-destructive. The description adds a useful behavioral fact: the tool performs no LLM processing itself. However, it does not disclose return behavior, overwrite/conflict semantics, or side effects beyond archiving, so the added transparency is modest.

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 exactly two sentences with no fluff. The primary action is front-loaded, and the second sentence communicates the critical caller-responsibility constraint. It avoids repeating schema content.

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?

The description plus the rich input schema adequately cover the 8 parameters, including nuanced ones like project_id and session_key, and state the key prerequisite. It does not explicitly connect archival to recall/list_episodes or describe the return value, but those are not essential for correctly selecting and invoking the tool.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3 even without parameter details in the tool description. The phrase 'pre-computed summary, keywords, and resolved status' reinforces that these values must be prepared by the caller, but it does not add meaningfully to the already-detailed schema descriptions.

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 names a specific verb ('Archive') and resource ('conversation episode') and states the payload: pre-computed summary, keywords, and resolved status. It is clear about what the tool does and that it creates/stores an archived episode, but it does not explicitly contrast it with sibling tools like store, update_memory, or list_episodes, so it stops short of a 5.

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

Usage Guidelines4/5

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

'All LLM processing is performed by the caller' is an explicit prerequisite: the agent should only call this tool after summarization, keyword extraction, and resolved-status determination have already happened. It does not name alternatives or state when not to use it, so it lacks the explicit exclusion guidance needed for a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Cloto-dev/CPersona'

If you have feedback or need assistance with the MCP directory API, please join our Discord server