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by Sebtiago
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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save_validation

Store validation reports generated from user profile validation, enabling review and tracking of service design assessment results.

Instructions

Save a validation report.

Args: project_path: Absolute path to the project directory. profile_id: The profile that was validated against. report: JSON string with the validation report.

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": {
      "project_path": "<project_path>",
      "profile_id": "<profile_id>",
      "report": "<report>"
    },
    "name": "save_validation"
  }
}

Input Schema

NameRequiredDescriptionDefault
project_pathYes
profile_idYes
reportYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

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

With no annotations, the description must disclose behavior. Only states 'save', which is a mutation, but does not mention overwriting behavior, required permissions, side effects, or response details. Minimal disclosure.

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?

Description is very concise: one sentence for purpose then a clean list of args. No unnecessary words, front-loaded with purpose.

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 simple save operation with 3 params and an output schema, the description covers purpose and parameter meanings. Could mention that profile_id should match an existing profile, but overall adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description explains each parameter: project_path as absolute path, profile_id as the profile validated against, report as JSON string. This adds meaning beyond the schema's type-only definitions.

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 states 'Save a validation report' with a specific verb and resource. It distinguishes from siblings like list_validations (list) and get_* tools (retrieve), though it doesn't explicitly differentiate.

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 guidance on when to use this tool vs alternatives. Does not mention prerequisites (e.g., must have run a validation first) or that list_validations can list saved reports.

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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