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list_tables

List tables and views in a loaded Perfetto trace to explore its schema before running SQL queries. Optionally filter by GLOB pattern.

Instructions

List tables and views in the loaded trace. Read-only.

Use when: exploring an unfamiliar trace or verifying a table exists before writing SQL. Underlying SQL engine is SQLite, so the catalog tables common in other SQL engines aren't present — this MCP tool is the schema introspection path.

Don't use for: queries against known stdlib modules — go straight to execute_sql with INCLUDE PERFETTO MODULE. Don't reference this tool name inside SQL; it's a separate MCP tool, not a SQL function — call it via the tool API.

Parameters: optional pattern — SQLite GLOB filter (e.g. chrome_* for chrome stdlib views, slice* for the slice table family). Without it, internal stdlib tables (_*) are hidden.

Empty result: no tables matched the pattern. If a doc-listed table is missing, retry with an explicit pattern in case it's marked internal.

Errors when: no trace is loaded — call load_trace first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
patternNoOptional GLOB pattern to filter table names (e.g. "chrome_*").
Behavior5/5

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

With no annotations, the description fully discloses behavior: read-only, SQLite engine, pattern filtering hides internal tables, empty result meaning, and error condition (no loaded trace). Provides complete transparency.

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?

Description is well-structured with purpose at the start, followed by usage guidance and parameter details. Each sentence adds value, though a bit lengthy; could be slightly more concise.

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 no output schema, the description covers return behavior (empty result, errors) and dependencies (requires loaded trace). Complete for a simple listing tool with one optional parameter.

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?

Although schema coverage is 100%, the description adds significant meaning: pattern is a GLOB filter, provides examples ('chrome_*', 'slice*'), and explains behavior with and without pattern. Adds 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?

The description clearly states it 'list tables and views in the loaded trace' and notes it is read-only. It distinguishes from sibling tools like 'execute_sql' and 'list_table_structure' by specifying its purpose as schema introspection.

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 states when to use (exploring unfamiliar trace, verifying table existence) and when not to use (known stdlib modules should use 'execute_sql'). Also warns against referencing tool name in SQL.

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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