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Mipiti MCP Server

by Mipiti
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Python
Hybrid

Query Threat Model

query_threat_model

Ask natural-language questions about an existing threat model to identify gaps, attackers, and security posture. Get grounded, prose answers without modifying the model.

Instructions

Ask a natural-language question about an existing threat model.

Read-only; no side effects (no new version, no mutation). Uses AI to answer questions grounded in the model's assets, attackers, control objectives, assumptions, and current security posture, returning {model_id, answer} where answer is prose.

Use this for interpretation or summary questions ("what are the biggest gaps?", "which attackers target the token store?"). Do NOT use it to change the model — use refine_threat_model for that — and prefer get_threat_model / assess_model when you need structured data (entity lists, coverage counts) rather than a written answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesID of the threat model to query.
questionYesThe natural-language question to ask.
server_versionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.66.0
    • changedInput schema / properties / question / description
      Previous value: -"The question to ask."New value: +"The natural-language question to ask."
  2. Addedv0.62.2
  3. Removedv0.62.2
  4. First observedv0.57.0

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden, and it succeeds: it declares 'Read-only; no side effects (no new version, no mutation)', explains that AI is used, specifies that answers are grounded in model entities, and gives the return shape {model_id, answer}. This is unusually transparent.

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?

The description is front-loaded with purpose, then behavior, then usage boundaries. Every sentence earns its place, and the three short paragraphs are easy to scan for an agent deciding whether to invoke this tool.

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?

The description is remarkably complete for a read-only query tool: it covers behavior, return shape, use cases, and sibling alternatives. The only notable omission is any explanation of the required server_version parameter, which prevents a perfect score.

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?

The description adds useful semantics for the question parameter (interpretation/summary questions, with examples) and implies model_id must reference an existing model. However, the required server_version parameter is completely undocumented in both the schema and the description, leaving a clear gap.

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 verb and resource: 'Ask a natural-language question about an existing threat model.' It also distinguishes itself from likely siblings by explicitly saying it is not for mutation (refine_threat_model) and not for structured data (get_threat_model / assess_model).

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

Usage Guidelines5/5

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

The description explicitly states when to use the tool ('interpretation or summary questions'), when not to use it ('Do NOT use it to change the model'), and which alternatives to prefer for structured data. This leaves little to inference.

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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