Skip to main content
Glama

topic_context

Get detailed context about a specific topic from past sessions, including files, decisions, architecture notes, and conventions. Use when deeper understanding of a subject is needed.

Instructions

Get detailed context about a specific topic from past sessions (~500-800 tokens). Deep dive.

AUTOMATIC TRIGGERS - Call this AFTER session_recap when:

  • You need deep understanding of a specific topic

  • User asks about a specific area like "what do we know about storage?"

  • Example topics: 'storage', 'privacy', 'MCP', 'compression', 'caveman', 'session_manager', or any file name/feature from the recap

Returns detailed files, decisions, architecture notes, and conventions related to the topic — not just compressed tool outputs.

PARAMETERS:

  • topic: Topic to get detailed context about (e.g., 'storage', 'privacy')

  • session_id: Optional specific session to search (default: all sessions)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes
session_idNo
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It discloses the approximate response volume (~500-800 tokens), the type of content returned (files, decisions, architecture notes, conventions), and the scope of search (past sessions, optional session filter). It still does not mention potential cost or failure behavior, but for a read-style retrieval tool this is reasonably transparent.

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 well-structured, front-loaded with the core purpose, and uses clear trigger bullets. Each section adds useful information, though a few phrases like 'Deep dive.' and the repeated 'specific topic' could be tightened without loss.

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?

For a simple two-parameter tool with no output schema and no annotations, the description covers the key needs: when to use, what it returns, and what parameters mean. It could be slightly more complete by mentioning what happens when no context is found or how session_id should be formatted, but overall it is well-rounded.

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 description coverage is 0%, so the description must compensate for the input schema. It explains both parameters with examples for topic and clarifies that session_id is optional and defaults to all sessions. It adds real semantic value beyond the raw schema fields.

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 states a specific verb and resource: getting detailed context about a specific topic from past sessions. It adds concrete examples and scopes the tool clearly, making it easy to distinguish from broader retrieval tools. The phrase 'not just compressed tool outputs' further clarifies what this tool uniquely provides.

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?

The description gives explicit automatic triggers and states this should be called AFTER session_recap, with concrete example user requests. It does not explicitly name alternatives or say when not to use it, but the trigger conditions are strong enough for an agent to make a good decision.

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/Vedant9500/OpenLMlib'

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