Decisify

review_model_code

Validates PySCIPOpt Python code against mathematical formulations and API standards, returning pass/fail status with bugs and corrections.

Instructions

Reviews PySCIPOpt Python code against the mathematical formulation and PySCIPOpt API standards. Returns 'REVIEW_STATUS: PASSED' or 'REVIEW_STATUS: FAILED' with bugs and corrections.

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": {
      "code": "<code>"
    },
    "name": "review_model_code"
  }
}

Input Schema

Name	Required	Description	Default
`code`	Yes
`formulation`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 30, 2026

TDQS

B3.1/5.0

Behavior3/5

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

No annotations provided, so the description carries the full burden. It usefully discloses the outcome contract ('REVIEW_STATUS: PASSED' or 'FAILED' with bugs and corrections), which is real behavioral context. However it says nothing about side effects, whether it is read-only, or how it handles malformed/non-PySCIPOpt input.

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 with zero padding, front-loading the action and scope before the return-value contract. Nothing extraneous.

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?

An output schema exists, so explaining return values is optional and the description does it anyway. But with no annotations and 0% parameter documentation, the definition leaves genuine gaps: usage position in the pipeline, the optional formulation input, and side-effect/permission behavior remain unstated.

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 description coverage is 0% for both parameters, so the description must compensate and largely does not. It hints at the role of 'formulation' by saying code is reviewed 'against the mathematical formulation' but never clarifies that this parameter is optional with an empty default, nor what format it takes. 'code' is also undocumented beyond the tool name.

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?

States a specific verb (reviews) and resource (PySCIPOpt Python code), and names the two axes of the review: the mathematical formulation and PySCIPOpt API standards. It distinguishes itself implicitly from review_math_model and review_requirements by scoping to code, though it never explicitly differentiates from those siblings.

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?

No when-to-use guidance, no prerequisites, and no alternatives named. The pipeline context (siblings like generate_model_code, execute_model_code, review_execution_result) implies this runs after code generation, but the description leaves the agent to infer its position in the workflow.

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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