Lore

Official

by agentkitai

Overview Schema Source Related Servers Discussions Admin

Python

Remote

timeline

Retrieve chronologically adjacent events around a memory anchor to establish causality before fetching full content.

Instructions

Phase 6G middle drill-down: return chronologically adjacent events (±limit entries, hard cap ±max_hours) around an anchor memory ID, scoped to the same project. Each entry: id, created_at, type, title, narrative_1l, same_session. USE THIS AFTER search() identifies a promising hit, BEFORE get_memories(), to establish causality without paying for full content. ~60 tokens/entry. PARAMS: anchor_id (required); limit (1-50, default 10); direction ('before'|'after'|'both', default 'both'); max_hours (>0, ≤72, default 2.0).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "anchor_id": "<anchor_id>"
    },
    "name": "timeline"
  }
}

Input Schema

Name	Required	Default
`limit`	No
`anchor_id`	Yes
`direction`	No	both
`max_hours`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jun 24, 2026

TDQS

A5/5.0

Behavior5/5

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

Despite no annotations, the description fully discloses behavior: returns entries with specific fields, respects hard caps (±max_hours), scoped to project, non-destructive read operation. Also mentions cost (~60 tokens/entry). No contradictions.

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?

Description is a single well-structured paragraph that front-loads purpose, then adds constraints, entry format, usage guidance, cost, and parameter details. No redundant or irrelevant sentences.

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 output schema exists, the description still provides enough context: usage sequence, scope, constraints, entry structure, cost. Covers all essential aspects for an AI agent to use the tool correctly.

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 has 0% description coverage, but the tool description explains each parameter's meaning, constraints, and defaults: anchor_id (required), limit (1-50, default 10), direction (before/after/both), max_hours (>0, ≤72, default 2.0). Adds full semantics beyond the bare schema.

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?

Description clearly states verb 'return' and resource 'chronologically adjacent events' around an anchor memory ID. It distinguishes from siblings by specifying when to use (after search, before get_memories) and the scope (same project).

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?

Explicitly provides a usage sequence: 'USE THIS AFTER search() identifies a promising hit, BEFORE get_memories()'. Also explains purpose ('to establish causality without paying for full content') and gives token cost estimation, helping the agent decide when to invoke.

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