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Systemores AI Agent & MCP Security Auditor

Evaluate System Prompt Tool Discipline

systemores_tool_integrity
Read-only

Evaluates whether the agent's system prompt establishes the behavioral conditions for safe, reliable tool use — auditing tool selection discipline, argument construction fidelity, and failure resilience when tool returns are ambiguous or erroneous.

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": {
      "systemPrompt": "<systemPrompt>"
    },
    "name": "systemores_tool_integrity"
  }
}

Input Schema

NameRequiredDescriptionDefault
toolContextNoThe domain of tool usage being audited. Affects risk weighting.
systemPromptYesThe agent's system prompt to evaluate for tool use safety. Maximum 8,000 characters.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is largely covered. The description adds real value by naming the three audit dimensions and by calling out that ambiguous or erroneous tool returns are in scope, but it says nothing about how results are produced or what an evaluation returns. Nothing in the description contradicts 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?

A single front-loaded sentence with no filler; the em-dash clause packs the three audit dimensions efficiently. It is slightly dense but every phrase carries information.

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?

For a two-parameter, read-only evaluation tool, the description covers what is audited, and the schema fully covers inputs. Because there is no output schema, a sentence describing the shape of the returned assessment would have closed the remaining gap.

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%, with both systemPrompt and the toolContext enum fully described in the schema, so the baseline of 3 applies. The description adds no meaning beyond the schema; it never mentions the toolContext domain weighting or the 8,000-character prompt limit.

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 uses a specific verb ('Evaluates') on a specific resource ('the agent's system prompt') and enumerates the three audited behaviors: tool selection discipline, argument construction fidelity, and failure resilience. It does not, however, distinguish itself from the nearly identically named sibling systemores_audit_tool_integrity, leaving an agent to guess which one applies.

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 explicit when-to-use statement, no prerequisites, and no mention of when not to use it. The closest thing to guidance is the implicit framing that it applies to system prompts, which an agent must infer rather than read.

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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