Timelnapp Timeln Find -- Search Your Second Brain
Search and recall over the user's real Timeln memory. When triggered, silently pull live data via the Timeln MCP, synthesize across MECE + PARA, and return sharp, actionable insight. No hallucination -- only real nodes and edges.
Setup (one-time, user-side)
- Sign up free at https://timeln.app/signup.
- Get an API token: Settings -> API Tokens -> Create in the dashboard.
- Add the hosted MCP to your agent config.
Claude Code (~/.claude.json) or Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"timeln": {
"url": "https://timeln-mcp-production.up.railway.app/mcp",
"headers": {
"Authorization": "Bearer tln_YOUR_TOKEN_HERE"
}
}
}
}
No Python install required -- the MCP is hosted.
If tln_... is missing or invalid, MCP tools return a signup nudge -- surface that verbatim to the user.
MCP tools you will call
| Tool | Purpose |
|---|---|
whoami |
Confirm token + return email/plan. Always call first. |
get_recent_docs(window) |
Last 7 days (weekly) or 30 days (monthly) of ingested docs. |
search_documents(limit, offset) |
Paginated list of all user documents. |
get_document(doc_id) |
Fetch a single document by id (with preview). |
query_knowledge(question) |
Natural-language query over the user's KG + documents. |
get_topic_entities(topic) |
Entities/sources clustered around a topic keyword -- use for MECE gap analysis. |
ingest_text(text, title?) |
Add new text content. |
ingest_url(url, title?) |
Add a public URL. |
Do not reimplement these -- always go through the MCP.
Workflow
Step 1 -- Identify the user
Call whoami. If it errors with "no token" / "Unauthorized", return the signup message and stop.
Step 2 -- Pull recent context
Call get_recent_docs(window="monthly"). Extract topic clusters, PARA categories (project/area/resource/archive), recency.
Step 3 -- Pull knowledge-graph signal
For each dominant topic from Step 2, call get_topic_entities(topic="...") to get entity clusters and their sources. For direct NL questions, call query_knowledge(question="...").
Step 4 -- Synthesize with MECE + PARA
MECE gap analysis -- map entities into four quadrants:
| Quadrant | Test | Finding |
|---|---|---|
| Known | High-frequency entities across many sources | Core expertise |
| Emerging | Mid-frequency, recent ingestions | Growing areas |
| Isolated | Few sources, weak cross-links | Latent gaps |
| Missing | Topics implied by adjacency but absent | Blind spots |
For each gap, write: [Node A] -> SHOULD CONNECT TO -> [Node B] -- backed by real data.
PARA classification from each doc's para_category field:
- Projects -- active, time-bound -> ship today
- Areas -- ongoing responsibilities -> maintain
- Resources -- reference material -> learn from
- Archive -- noise -> stop
Step 5 -- Optional: interactive visualization
If the user asks for a graph, visual, or map (e.g. "show my knowledge graph", "visualise my brain", "plot my topics"), the skill handles everything -- no scripts to run:
- Call
get_topic_entitiesfor each relevant topic surfaced in steps 3-4. - Merge results into
{nodes, links}-- each node is an entity, each link is a relationship or shared document. - Inject the graph data into
kg_interactive_template.html(replace__GRAPH_DATA__) and write the result askg_interactive.htmlin the workspace root. - Open the file so the user sees it immediately.
The user never leaves the chat window -- just ask in natural language and the skill produces a ready-to-open HTML file.
Output format (always)
## Your Brain, Right Now
[1-2 sentence synthesis of what the data shows you're building]
## MECE Map
| | Connected | Isolated |
|---|---|---|
| **Known** | [real nodes] | [real gaps] |
| **Emerging** | [real nodes] | [missing bridges] |
## PARA -- What to Do Today
**Project (ship):** [1-3 hr action tied to a real project node]
**Area (deepen):** [real concept to go deeper on]
**Resource (learn):** [specific saved doc you haven't connected yet]
**Archive (stop):** [what's noise -- backed by data]
## The One Sentence
> [Single sharpest insight from the data]
Handling specific questions
When the user asks a concrete question (not a general "second brain" prompt):
query_knowledge(question="<their question>")-- NL over their graph + documents.search_documentsorget_recent_docsfor titles to cite.- Synthesize both into a direct answer -- cite real titles/entity names, no fabrication.
Rules
- Always go through MCP tools. Never call infrastructure directly.
- Never fabricate node names, titles, or relationships. If a tool returns nothing, say so.
- Never include the user's API token in any output or tool echo.
Common failure modes
| Rationalization | Why it's wrong |
|---|---|
| "The MCP returned thin results, I'll supplement from training data" | Say the data is thin. Never mix real memory with invented knowledge. |
| "MECE analysis is overkill for this question" | If it's a simple factual question, it should have gone to timeln-quickly. If it's here, do the full synthesis. |
| "I'll skip the PARA breakdown since the user just asked a question" | For direct questions, use the "Handling specific questions" path. PARA/MECE is for exploratory prompts. |
| "No graph was requested, but a visualization would be nice" | Only generate the D3 graph when explicitly asked. Don't pad the response. |
| "I'll fabricate node names to fill gaps in the MECE map" | Every node and edge must trace to real MCP data. Empty quadrants are valid. |
This is a flexible skill. Adapt the depth of synthesis to the question, but never fabricate data.