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Decisify

Decisify

by sharabhshukla
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

review_requirements

Validate a clarified problem specification for completeness, consistency, and feasibility, returning PASSED or FAILED with specific critique.

Instructions

Reviews a clarified problem specification for completeness, consistency, and feasibility. Returns 'REVIEW_STATUS: PASSED' or 'REVIEW_STATUS: FAILED' with specific critique.

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": {
      "specification": "<specification>"
    },
    "name": "review_requirements"
  }
}

Input Schema

NameRequiredDescriptionDefault
specificationYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the output format ('REVIEW_STATUS: PASSED' or 'FAILED' with critique), which is helpful, but does not state whether the operation modifies any state, what permissions are needed, or that it requires a pre-clarified input. The read-only nature is implied by 'Reviews' but not explicit.

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 sentences, front-loaded with the action and criteria, followed by the return format. No redundant or filler text; every sentence earns its place.

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 return values need not be detailed, and the description does cover the return format. However, for a tool with a completely undocumented input parameter (0% schema coverage), the description does little to clarify what the specification should contain or how it should be formatted, leaving a gap.

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% and the only parameter is undocumented. The description adds the notion that the input must be a 'clarified problem specification', which is minimal semantic guidance, but it does not describe the expected format, structure, or content, leaving the agent to guess.

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 'Reviews' and resource 'a clarified problem specification', and lists the exact criteria: completeness, consistency, feasibility. This distinguishes it from siblings like review_math_model and review_model_code, which target different artifacts.

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

Usage Guidelines3/5

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

The reference to a 'clarified problem specification' implies it should be used after clarification, but the description does not explicitly state when to use it versus siblings like review_math_model, nor does it name alternatives or give conditions. Usage is only inferable from the input artifact.

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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