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agentlens_context

Retrieve past session summaries and lessons ranked by relevance for any topic to load history, build informed prompts, or audit past activity.

Instructions

Retrieve cross-session context for a topic — related session summaries and lessons ranked by relevance.

When to use: At the start of a session to load relevant history, when building a system prompt with past experience, when starting work on a topic the agent has handled before, or to audit what happened with a specific topic.

What it returns: Related sessions (with summaries, key events, and relevance scores) and related lessons, all ranked by relevance to the topic. Includes an overall summary.

Example: agentlens_context({ topic: "database migrations", limit: 5 }) → returns past sessions about DB migrations with key events, plus any lessons learned about migrations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesTopic to retrieve context for (natural language)
userIdNoFilter by user ID
agentIdNoFilter by agent ID
fromNoStart date filter (ISO 8601)
toNoEnd date filter (ISO 8601)
limitNoMaximum number of sessions to include (default: 5)
Behavior4/5

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

No annotations, so description carries full burden. Explains return structure (related sessions, lessons, relevance scores) and gives example. Does not mention read-only nature or potential caching, but is transparent for a retrieval tool.

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?

One paragraph and an example, no wasted words. Front-loaded with purpose. Every sentence adds value.

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?

No output schema, but description fully explains return structure with sessions, lessons, summaries, relevance scores. Covers use cases, parameters, and example. Complete for a context retrieval tool.

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?

Schema coverage is 100%, baseline 3. Description adds meaning by explaining tool's use of parameters (e.g., topic as natural language, limit for max sessions) and provides example. Adds moderate value beyond 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 it retrieves cross-session context for a topic, with specific verb and resource. It distinguishes from sibling tools like agentlens_sessions or agentlens_query_events by focusing on context retrieval.

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?

Explicitly lists when to use: at start of session, building system prompt, handling familiar topic, audit. Does not state when not to use or name alternatives, but use cases are clear.

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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