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no13productions

AI Agent History RAG MCP Server

get_session_summary

Retrieve summaries of conversation sessions to recall previous discussions and work. Useful for catching up on what was covered in recent or specific sessions.

Instructions

Get summary of conversation session(s).

Use for:
- "What did we work on in the last session?"
- "Summarize our recent conversations"

Args:
    session_id: Specific session ID, or None for recent
    project_filter: Limit to specific project
    count: Number of sessions to summarize

Returns:
    Dict with session summaries

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
session_idNo
project_filterNo
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It only states the return type ('Dict with session summaries') and parameter explanations, but omits whether the operation is read-only, destructive, or has any side effects, rate limits, or prerequisites. This is insufficient for a tool with no annotation coverage.

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?

The description is short (7 lines) and well-structured: purpose, usage examples, args, returns. Every sentence adds value, and the key information is front-loaded. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of an output schema, the description only vaguely mentions 'Dict with session summaries' without detailing the structure or keys. It also does not cover pagination, error behavior, or performance implications. For a tool with 3 parameters and no additional schema, this is adequate but has notable gaps.

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?

The input schema has no descriptions for its 3 parameters, so the description takes on the full burden. It clearly explains each parameter's meaning and default behavior (e.g., 'session_id: Specific session ID, or None for recent'), adding essential semantics beyond the raw schema types and defaults.

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 'Get summary of conversation session(s)' with a specific verb and resource. It provides concrete usage examples that distinguish it from siblings like search_conversations, which is for searching individual messages rather than summarizing entire sessions.

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 use cases ('What did we work on in the last session?', 'Summarize our recent conversations') that help an agent determine when to invoke this tool. However, it does not explicitly state when not to use it or compare it to alternatives, leaving some ambiguity.

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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