Skip to main content
Glama

save_conversation

Persist complete conversation transcripts as searchable memory, auto-extracting project context, titles, and status while routing updates to a single memory per topic.

Instructions

Save complete conversations as memory. REQUIRED: Send COMPLETE conversation in 'conversationContent' parameter (minimum 100 chars, should be thousands). Include EVERY message verbatim - NO summaries or partial content.

Intelligently tracks context, extracts project details, and routes to a single memory per conversation topic.

HOW SAVES TARGET MEMORIES:
- conversationId is auto-generated from title slug (e.g., "MCP Tools" → "mcp-tools")
- Same title (or explicit conversationId) → targets the existing memory
- The 'mode' parameter controls what happens to that existing memory:
  • mode='replace' (default): overwrites the existing content with what you send
  • mode='append': concatenates new content below existing with a timestamped separator
    (\n\n--- UPDATE <ISO8601> ---\n\n) — preserves all prior history in the live row
- The /save skill sets mode='append' automatically for living-document use
- For one-shot snapshots, ad-hoc captures, or explicit overwrite: pass mode='replace'

PRIOR CONTENT IS NEVER LOST:
- Even with mode='replace', prior content is snapshotted to memory_events audit log on every update
- Recovery from overwrites requires a one-off script (out-of-band)
- Use mode='append' if you want history to remain visible inline in the live memory

INTELLIGENT EXTRACTION (independent of mode):
- Auto-extracts project context (name, component, feature being discussed)
- Detects work iteration and status (planning/in_progress/completed/blocked)
- Generates smart titles like "Purmemo - Timeline View - Implementation"
- Tracks technologies, tools used, identifies relationships/dependencies

SERVER AUTO-CHUNKING:
- Large conversations (>15K chars) automatically split into linked chunks
- Small conversations (<15K chars) saved directly as single memory
- You always send complete content — server handles chunking
- APPEND + CHUNKING: append mode works only for content <15K chars. Saves >15K
  with mode='append' are rejected with a clear error — appending to chunked
  storage would double each chunk's content on re-save. For long-running living
  docs, send only the new delta since the last save (keep it <15K) or use
  mode='replace' for full re-saves.
- KNOWN CAVEAT: a doc that is saved small (single memory) and later grows past 15K
  transitions to chunked storage at a new conversation_id space — the original
  single memory becomes orphaned. Tracked under ADR-038 (uniform namespace).

EXAMPLES:
User: "Save progress" via /save skill
→ /save sets mode='append'; new content is appended below prior content

User: "Save this snapshot" (one-shot capture)
→ mode='replace' default; current content overwrites any existing memory at this title

User: "Save as conversation react-hooks-guide" with explicit append
→ save_conversation(conversationId="react-hooks-guide", mode="append")
→ Appends to existing memory at that ID (or creates if new)

WHAT TO INCLUDE (COMPLETE CONVERSATION REQUIRED):
- EVERY user message (verbatim, not paraphrased)
- EVERY assistant response (complete, not summarized)
- ALL code blocks with full syntax
- ALL artifacts with complete content (not just titles/descriptions)
- ALL file paths, URLs, and references mentioned
- ALL system messages and tool outputs
- EXACT conversation flow and context
- Minimum 500 characters expected - should be THOUSANDS of characters

FORMAT REQUIRED:
=== CONVERSATION START ===
[timestamp] USER: [complete user message 1]
[timestamp] ASSISTANT: [complete assistant response 1]
[timestamp] USER: [complete user message 2]
[timestamp] ASSISTANT: [complete assistant response 2]
... [continue for ALL exchanges]
=== ARTIFACTS ===
[Include ALL artifacts with full content]
=== CODE BLOCKS ===
[Include ALL code with syntax highlighting]
=== END ===

IMPORTANT: Do NOT send just "save this conversation" or summaries. If you send less than 500 chars, you're doing it wrong. Include the COMPLETE conversation with all details.

ARTIFACT PRESERVATION (ADR-025):
If this conversation produced artifacts (research reports, tables, frameworks, specs, design documents),
save them SEPARATELY using save_artifact after this call.
Flow: save_conversation first, then save_artifact for each artifact.
This ensures artifacts are preserved in full — do not try to embed large artifacts in conversationContent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoHow to handle a save that targets an existing memory (same title or conversationId). "replace" (default) overwrites the existing content with what you send. "append" concatenates new content below the existing content with a timestamped separator (\n\n--- UPDATE <ISO8601> ---\n\n). Use "append" for living documents you genuinely want to grow over time; use "replace" for one-shot snapshots and ad-hoc captures. The /save skill defaults to "append" automatically — you only need to pass this for explicit overrides.replace
tagsNoTags for categorization
titleNoTitle for this conversation memoryConversation 2026-08-03T19:20:47.417Z
priorityNoPriority level for this memorymedium
conversationIdNoOptional unique identifier for living document pattern. If provided and memory exists with this conversationId, UPDATES that memory instead of creating new one. Use for maintaining single memory per conversation that updates over time.
conversationContentYesCOMPLETE conversation transcript - minimum 500 characters expected. Include EVERYTHING discussed.
Behavior4/5

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

Annotations declare readOnlyHint=false, destructiveHint=false, openWorldHint=true. The description goes well beyond annotations by disclosing that prior content is NEVER lost (snapshotted to memory_events audit log), documenting the server-side chunking behavior (>15K auto-split), the append-chunking rejection limit, and the ADR-038 orphaned memory caveat. It also discloses the field constraints like minimum characters. The only area not covered is return format/output, though that's largely acceptable given this is a save operation.

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 long but densely informative with clear section headers (HOW SAVES TARGET MEMORIES, PRIOR CONTENT IS NEVER LOST, INTELLIGENT EXTRACTION, SERVER AUTO-CHUNKING, EXAMPLES, WHAT TO INCLUDE, FORMAT REQUIRED, ARTIFACT PRESERVATION). Front-loads the critical requirement (send complete content) first. It earns its length given the complexity of the tool's behavior, though some redundancy exists between the opening paragraphs and the later 'WHAT TO INCLUDE' section which repeats the same admonitions about complete content.

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 complexity (6 params, chunking behavior, append/replace semantics, audit logging, ADR caveats, artifact handoff), the description is remarkably complete. It covers edge cases (append+chunking rejection), warns about the orphaned memory caveat, provides the exact required format template, and provides a clear action flow for artifact preservation. There is no output schema, so return-value documentation isn't necessary, and the description adequately compensates for the tool's behavioral complexity.

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

Parameters5/5

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

Schema coverage is 100%, providing baseline 3. However, the description adds substantial meaning beyond the schema: it explains the auto-generation of conversationId from title slug, details the exact append separator format (\n\n--- UPDATE <ISO8601> ---\n\n), explains the conversationContent minimum is actually 500 chars not just the 100 in schema, and provides concrete EXAMPLES showing parameter usage for each mode. The description compensates heavily and transforms the parameters into a clear mental model.

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 purpose: saving complete conversations as memories, with a specific emphasis on requiring complete verbatim content. It distinguishes itself from siblings like save_artifact (which is explicitly referenced for artifacts) and recall_memories by describing the write/save function distinctly. The verb+resource ('Save... as memory') plus the content requirements make the purpose unmistakable.

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 provides exceptionally detailed guidance on when to use this tool, how mode affects targeting, and explicitly names save_artifact as the alternative for artifacts ('save them SEPARATELY using save_artifact after this call'). It distinguishes replace vs append use cases, explains the /save skill behavior, and gives explicit when-to-use guidance for living documents vs one-shot snapshots.

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/purmemo-ai/purmemo-mcp'

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