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kruthin-daacs

Vedha JSON MCP

Diagnose within a Vedha canvas

vedha_get_diagnosis
Read-onlyIdempotent

Get guardrails and dimension breakdowns for one canvas to answer causal 'why' questions. Scoped to a single canvas, ideal for within-canvas diagnostics; escalate cross-canvas queries elsewhere.

Instructions

Return the guardrails and a dimension breakdown for one canvas, to support a causal 'why' question. Thread-scoped: stays inside one canvas. Never use it for cross-canvas questions — escalate to vedha_get_atlas_review instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueNo
canvas_idYes
dimensionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare read-only and idempotent behavior, and the description does not contradict them. It adds useful behavioral context beyond the annotations: the tool is thread-scoped, stays inside one canvas, and returns guardrails plus a dimension breakdown.

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?

The description is short and front-loaded, with the core behavior stated first and the alternative routing stated second. Minor redundancy exists between 'for one canvas', 'Thread-scoped: stays inside one canvas', and 'Never use it for cross-canvas questions', but the structure remains efficient.

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?

The description does a good job with when-to-use and scoping, and an output schema exists to cover return values. However, with 0% schema description coverage and three parameters, the lack of detail about the 'dimension' and optional 'value' parameters leaves a meaningful 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%, so the description must compensate, but it only loosely references 'canvas' and 'dimension'. It does not explain the optional 'value' parameter, define what 'dimension' should be, or clarify how canvas_id relates to the guardrails and breakdown.

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 states a specific verb ('Return') and resource ('guardrails and a dimension breakdown for one canvas'), and ties it to a causal 'why' question. It also distinguishes itself from siblings by explicitly naming the cross-canvas tool, vedha_get_atlas_review.

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?

It clearly says when to use the tool: for a causal 'why' question within a single canvas. It also gives an explicit exclusion and alternative: never use it for cross-canvas questions, escalate to vedha_get_atlas_review instead.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.