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probe_fuzz

Destructive

Fuzz MCP tool endpoints with generated valid and malformed inputs, recording outcomes and coverage to identify protocol crashes and silent error acceptance.

Instructions

Generate one valid and several malformed inputs per target tool, call each, and record the outcome (ok, toolError, protocolCrash), whether malformed inputs were silently accepted, and call latency. Tools annotated destructiveHint:true are skipped by default (set fuzzDestructive to include them). Returns a coverage summary of which tools were fuzzed vs skipped.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxToolsNoCap on the number of tools to fuzz. Defaults to 10.
connectionIdNoIdentifier returned by probe_connect. Defaults to the most recent connection.
fuzzDestructiveNoAlso fuzz tools annotated destructiveHint:true. Default false (the dry-run safety guard) so fuzzing an untrusted target can't trigger a destructive action.
Behavior4/5

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

Annotations already mark the tool as destructive and open-world, and the description adds concrete behavioral details: it records ok/toolError/protocolCrash, detects silently accepted malformed inputs, measures latency, and returns a coverage summary. The safety default of skipping destructive target tools unless fuzzDestructive is set is also disclosed, going beyond the annotations.

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 two sentences and front-loads the core action, making the tool's function immediately clear. The first sentence is somewhat long, but every detail earns its place and the safety sentence is essential.

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

Completeness4/5

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

Even without an output schema, the description states it returns a coverage summary of fuzzed vs skipped tools, which covers the primary return value. Combined with full schema coverage and annotations, this is complete for the tool's complexity; the probe_connect prerequisite is handled by the schema's connectionId description.

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 coverage is 100% and all three parameters (maxTools, connectionId, fuzzDestructive) are already documented in the input schema. The description only restates fuzzDestructive's behavior and does not add new parameter-level meaning beyond the 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 opens with a specific action—generate one valid and several malformed inputs per target tool, call each, and record outcomes—which clearly defines the tool's purpose. It distinguishes probe_fuzz from siblings like probe_connect and probe_lint by emphasizing malformed-input generation and the resulting coverage summary.

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 this tool is for fuzzing target tools and explicitly explains the fuzzDestructive flag behavior. However, it does not name alternative tools or explicitly state when to choose this over a sibling, so usage guidance remains implicit rather than explicit.

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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