MBEstudio

Verify model

verify_model

Idempotent

Check a saved model with the native SysML compiler and SHACL shapes for requirement subjects and, when a structured specification exists, function allocations. Returns findings and explicit coverage. Paid. Call it directly: it charges what the job uses, never more than its estimated maximum or the max_spend_credits you set. request_id is optional; set one to make a retry safe. Returns job_id immediately. Poll get_job_status for later progress.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "verify_model"
  }
}

Input Schema

Name	Required	Description	Default
`rows`	No	Only for cost_mode "agent_authored" on generate_safety_analysis: {conditions:[{target,description,severity,...}],modes:[{target,mode,severity,occurrence,detection,...}]}; with analysis "stpa": {losses:[{id,description}],hazards:[{id,description,losses}],controllers:[{target,processModel}],controlActions:[{from,to,name,ucas:[{id,type,context,contextVariables,hazards,constraint}\|{type,notApplicable,rationale}]}],feedback:[{from,to,name,updates}],constraints:[{id,name,text}]}.
`model`	No
`study`	No
`events`	No
`format`	No
`prompt`	No
`stop_s`	No
`analysis`	No	For generate_safety_analysis: fha, fmea, both (default) or stpa. stpa needs no standard.
`fidelity`	No
`model_id`	No	A model from a generation job (model_ id) or a saved studio model (m_ id); a saved model is read at the head of main.
`provider`	No
`standard`	No
`cost_mode`	No		managed
`max_depth`	No	Only for run_agent_swarm: levels of division, 1 to 3 (default 2). At 2 and 3, a subsystem the orchestrator judges too large for one agent is divided again inside its frozen boundary.
`subsystem`	No	For reprompt_agent: qualified name of the subsystem, e.g. "Rover::DriveUnit". For run_agent_swarm on a saved model: the subsystem to decompose one level further (its parts get the agents); leave it out to work on the whole system's subsystems.
`request_id`	No
`scenario_id`	No
`simulations`	No
`specification`	No	Only for cost_mode "agent_authored" on generate_system_model and build_full_system; see those tools for its schema.
`simulation_type`	No
`max_spend_credits`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedNov 4, 2026

addedInput schema / properties / analysis / description

Added value: +"For generate_safety_analysis: fha, fmea, both (default) or stpa. stpa needs no standard."

changedInput schema / properties / analysis / enum

Previous value: -[
-  "fha",
-  "fmea",
-  "both"
-]New value: +[
+  "fha",
+  "fmea",
+  "both",
+  "stpa"
+]

changedInput schema / properties / rows / description

Previous value: -"Only for cost_mode \"agent_authored\" on generate_safety_analysis: {conditions:[{target,description,severity,...}],modes:[{target,mode,severity,occurrence,detection,...}]}."New value: +"Only for cost_mode \"agent_authored\" on generate_safety_analysis: {conditions:[{target,description,severity,...}],modes:[{target,mode,severity,occurrence,detection,...}]}; with analysis \"stpa\": {losses:[{id,description}],hazards:[{id,description,losses}],controllers:[{target,processModel}],controlActions:[{from,to,name,ucas:[{id,type,context,contextVariables,hazards,constraint}|{type,notApplicable,rationale}]}],feedback:[{from,to,name,updates}],constraints:[{id,name,text}]}."

First observedNov 3, 2026

TDQS

A4.1/5.0

Behavior5/5

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

Goes well beyond the annotations: it discloses that the call is paid, that charge is capped by the estimate or max_spend_credits, that it returns a job_id immediately and is asynchronous, and how request_id makes retries idempotent. These are exactly the behavioral facts an agent needs before invoking a billable job and are not derivable from idempotentHint/readOnlyHint alone.

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?

Front-loads what is checked and what is returned, then moves to billing, retry and polling concerns in a logical order. It is dense but nearly every sentence carries actionable information; only the 'returns findings and explicit coverage' line is close to filler given the surrounding detail.

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

Completeness3/5

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

For a 21-parameter, no-output-schema, asynchronous paid tool the description covers lifecycle and cost well, but it never clarifies which of the many schema properties actually apply to verify_model versus the other tools whose parameters appear to have leaked into the schema. An agent is still guessing which inputs to supply.

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?

With 21 parameters and only 29% schema description coverage, the description compensates only for request_id and max_spend_credits, and hints at the specification condition. The core inputs for verification (model_id, model, study, format, standard, analysis, cost_mode, simulations) are left entirely to an under-documented schema, so the description does not fill the coverage 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?

States a specific verb and resource ('Check a saved model') plus the exact mechanisms used (native SysML compiler, SHACL shapes for requirement subjects and function allocations). This distinguishes it clearly from generation siblings like generate_system_model and build_full_system without needing to name them.

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?

Gives operational when-to guidance ('call it directly', 'poll get_job_status for later progress', 'set request_id to make a retry safe') and the condition under which function allocation checks apply. It stops short of explicitly routing the agent away from a named alternative, so it is clear context rather than full when/when-not guidance.

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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