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roynertr

COBie MCP

by roynertr
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Python
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suggest_runtime_skill

Determines the appropriate runtime skill based on user intent and workflow state, returning the skill name and rationale.

Instructions

Suggest runtime skill from user intent and workflow state (agentic affordance).

Deterministic heuristics: pending_preview + 'confirm' → applying-updates;
'export' → exporting-cobie; 'validate' → validating-cobie; default → editing-cobie.
Returns name and rationale. Use get_runtime_skill(name) for full body.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNo
user_intentYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It states that the tool returns a name and rationale and implies a pure suggestion operation, but does not explicitly declare that it has no side effects.

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?

The description is concise and well-structured, with no redundant content. It packs the mapping logic, return value, and pointer to a related tool into a few lines without losing clarity.

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?

Given the tool's simplicity and output schema presence, the description is adequately complete. It specifies the output (name and rationale) and the next step for full details, though it could be slightly more explicit about the overall workflow context.

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 adds meaningful parameter semantics by listing example values for user_intent and state (e.g., 'confirm', 'export', 'validate', 'pending_preview'). It does not fully define the state structure, but enough context is given for use.

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 clearly states the tool's purpose: suggesting a runtime skill based on user intent and workflow state. It distinguishes itself from siblings like get_runtime_skill and list_runtime_skills by providing a deterministic mapping.

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?

The description provides concrete deterministic heuristics and a default fallback, giving clear guidance on when to use it. It also points to get_runtime_skill for the full skill body, but does not explicitly state when not to use this tool versus all alternatives.

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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