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Glama

zerodom_race

Fires interleaved concurrent requests over an active browser session to probe TOCTOU race conditions, with setup and post-burst checks that reveal status spread and over-applied single-use actions.

Instructions

Fire a concurrent burst over the active session — the TOCTOU probe, run as the browser's own multiplexed fetch (the tightest client-side engine). bodies is one or more request bodies, each fired n times and interleaved (a mix of operations on one record widens a read-modify-write window); setup is a list of prep requests [{url|path, method?, body?}]; check is a URL GET-ed after the burst. Returns the status spread, per-body breakdown, and a race-condition finding when a single-use action over-applies.

Input Schema

NameRequiredDescriptionDefault
nNo
urlYes
checkNo
setupNo
bodiesNo
methodNoPOST

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.3

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses that it fires multiplexed fetch requests and returns a status spread, per-body breakdown, and race-condition finding, which is useful. However, it does not state whether the tool requires authentication, whether the requests are destructive, or what happens to the target session during the burst, leaving significant safety context unaddressed.

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 dense but front-loaded, with the core action stated first and parameter explanations following. It is appropriately sized for a complex tool, though the technical jargon and em-dash phrasing slightly reduce immediate readability.

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 values need not be described, but the description still covers them. For a mutation-like tool with no annotations and six parameters, it omits critical behavioral context such as safety, side effects, and authentication, leaving an agent with some uncertainty about calling it responsibly.

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%, so the description must compensate. It explains `bodies` (one or more request bodies, each fired n times and interleaved), `n` (repetition count), `setup` (prep requests), and `check` (URL GET after burst), but it does not explain `method` (default POST) or clarify `url` as the target. This covers most but not all parameters.

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 uses a specific verb and resource ('Fire a concurrent burst over the active session') and explicitly names the technique ('TOCTOU probe'), which distinguishes it from sequential or single-request siblings like zerodom_repeat and zerodom_probe. An agent can identify the tool's unique purpose immediately.

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

Usage Guidelines3/5

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

The description implies the tool is for race-condition testing and explains the roles of `bodies`, `setup`, and `check`, but it never states when to use this tool versus alternatives (e.g., zerodom_repeat for sequential bursts) or any conditions under which it should not be used. Usage is implied rather than explicitly guided.

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