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Detection Workbench

Detection Workbench

by andrewbrooks
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Python
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suggest_tuning_targets

Rank noisy SIDs in a Snort run by hit count, distinct sources/destinations, and source concentration to identify detection tuning targets.

Instructions

Rank the noisiest SIDs in a run by count, with distinct sources/destinations and how concentrated the hits are in one source. Example: suggest_tuning_targets(run_id="r-0002")

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "run_id": "<run_id>"
    },
    "name": "suggest_tuning_targets"
  }
}

Input Schema

NameRequiredDescriptionDefault
topNo
run_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations and no output schema, the description does useful work by disclosing what the ranking exposes (hit counts, distinct sources/destinations, source concentration). It says nothing about safety profile, cost of scanning a run, or limits on the ranking beyond the default top value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences, front-loaded with the core verb and resource, followed by a concrete example that clarifies the run_id format. Nothing is redundant.

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?

Without an output schema, the description's summary of returned metrics is the main source of return-value information and it covers it adequately. The remaining gaps are the undocumented top parameter and the lack of sibling routing guidance.

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

Parameters2/5

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

Schema coverage is 0%, so the description must carry parameter meaning. It illustrates run_id indirectly via the example ("r-0002"), but the top parameter is never mentioned, and its role in bounding the ranking must be guessed from the name and default.

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 action and resource: ranking the noisiest SIDs within a run, plus the metrics returned (count, distinct sources/destinations, concentration). This is materially different from siblings like profile_rules or test_tuning, though it never explicitly contrasts itself with them.

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 statement of when to call this versus alternatives such as profile_rules, get_alerts, or test_tuning, and no prerequisites or exclusions. The sample invocation shows the call shape but not the decision context, so usage is only inferable.

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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