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google-ai-history-mcp

google-ai-history-mcp

by travispflanz

Search Google Ai Chats

search_google_ai_chats

Search all synced Google AI prompts and responses by keyword to locate past Gemini, Search AI Mode, AI Studio, or NotebookLM conversations.

Instructions

Full-text search (SQLite FTS5) across all synced Google AI prompts and responses.

Input Schema

NameRequiredDescriptionDefault
limitNo
queryYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It usefully discloses that matching is SQLite FTS5 (so query text is interpreted as full-text syntax, not substring matching) and that both prompts and responses are indexed, but it says nothing about read-only safety, ranking/ordering, or whether the default limit truncates results.

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?

A single front-loaded sentence with no filler; the FTS5 parenthetical earns its place by signaling query syntax. It is very short, though, so the efficiency comes at the cost of the missing usage and parameter detail noted above.

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?

An output schema exists, so return-value documentation is not required, and the tool is simple (two parameters, one required). Still, for a search tool with no annotations and zero schema coverage, an agent gets no guidance on query syntax, result ordering, or limit behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for both parameters. The description compensates partly by implying 'query' is a full-text expression searched over both prompts and responses, but it never explains 'limit', its default of 20, or any maximum, leaving half the parameters undocumented everywhere.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Full-text search') and a specific resource ('all synced Google AI prompts and responses'), which is enough to distinguish it from the list/get siblings by intent. It stops short of naming those siblings explicitly, so the differentiation is inferred rather than stated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance: nothing says to search by keyword rather than enumerate with list_google_ai_chats, or to use get_google_ai_chat once a chat is identified. The phrase 'across all synced' weakly implies global scope, but no alternative or exclusion is stated.

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