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Glama

zerodom_probe

Detect mass-assignment and tampering by replaying an endpoint with a tampered body over the active session and diffing the response against baseline.

Instructions

Replay an endpoint with a tampered body over the active session and diff vs baseline — the mass-assignment / tampering check. tamper (JSON) is merged over body; an injected field accepted (2xx) and reflected in the tampered response but absent from baseline is flagged. Returns baseline/tampered responses and any finding.

Input Schema

NameRequiredDescriptionDefault
urlYes
bodyNo
methodNoPOST
tamperYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.3

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does most of it: it discloses that the request runs over the active session, that tamper is merged over body, the exact flagging criteria, and what is returned. The notable gap is side effects — it never says whether the tampered request can write real server state or how destructive the probe is.

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?

Two tight sentences, front-loaded with the action and purpose before the mechanics. Dense but every clause carries information; only mild redundancy between the opening label and the later flagging rule.

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?

For a 4-parameter tool with no annotations, the description supplies the operating context (active session), the mechanism (merge-over-body), the success criterion, and a return summary, while an output schema covers response shape. An agent has enough to call it correctly.

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 coverage is 0%, so the description must compensate, and it does for the non-obvious parameter: 'tamper (JSON) is merged over body' explains both the string-contains-JSON typing and the merge precedence, which the schema does not. url and method are left unexplained but are self-evident with method defaulting to POST.

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?

States a specific verb chain (replay, tamper, diff vs baseline) against a specific resource (an endpoint over the active session), and names the vulnerability class it targets (mass-assignment / tampering). This distinguishes it cleanly from the plain zerodom_replay sibling.

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 phrase 'the mass-assignment / tampering check' gives a clear condition for selecting this tool over a plain replay, and the detection rule (injected field accepted with 2xx, reflected, absent from baseline) tells the agent what scenario it is for. It stops short of explicitly naming zerodom_replay as the alternative or stating when not to use it.

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