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OjasKord

local-model-suitability-mcp

by OjasKord

check_local_viability

Determine if a local model can handle your task to avoid unnecessary cloud costs. Specify task, quality, and data sensitivity to get a verdict on whether to route locally or to the cloud.

Instructions

Checks whether a task can be handled by a local model instead of a cloud API. Call this BEFORE your agent routes any task to a cloud model — at the moment a task is ready to be sent and the model has not yet been selected. Use this when your agent is about to make a cloud inference call and wants to determine whether a local model could handle it at lower cost. Returns LOCAL/CLOUD/EITHER verdict with recommended model. If verdict is LOCAL, skip the cloud call entirely — saves cost on every routable inference call. Pass data_sensitivity=CONFIDENTIAL to force LOCAL verdict for tasks that must not leave the machine. Do not route to cloud without checking local viability first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesThe exact task you are about to send to a cloud model. Be specific — include what the input is and what output you need.
data_sensitivityNoCONFIDENTIAL forces LOCAL verdict regardless of task complexity — data must not leave the machine. Defaults to PUBLIC.
quality_thresholdNoPRODUCTION = output quality matters and errors are costly. PROTOTYPE = approximate results acceptable. BEST_EFFORT = speed and cost trump quality. Defaults to PRODUCTION.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonYes
verdictYes
checked_atYes
confidenceYes
_disclaimerYes
analysis_typeNo
data_sensitivityNo
estimated_cost_savingNo
cloud_justified_reasonNoNon-null only when verdict is CLOUD
task_quality_thresholdNo
recommended_local_modelsNoPresent when verdict is LOCAL or EITHER
data_sensitivity_overrideNoPresent only when data_sensitivity=CONFIDENTIAL forced a LOCAL verdict
Behavior5/5

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

No annotations provided, so description carries full burden. It fully discloses return value (LOCAL/CLOUD/EITHER with recommended model), behavior with CONFIDENTIAL, and cost-saving implications. No contradictions.

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?

Description is well-structured and front-loaded with purpose. Slightly verbose in middle sentences but no wasted words. Could be tightened, but still effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

All aspects covered: purpose, when to use, parameter details, output nature, and behavioral notes (cost savings). No gaps given the complexity and presence of output schema.

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

Parameters5/5

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

Schema description coverage is 100%, but description adds significant context: task should be 'exact task', data_sensitivity forces LOCAL, quality_threshold defines PRODUCTION/PROTOTYPE/BEST_EFFORT implications. Enhances schema meaning.

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 checks if a task can be handled locally vs cloud API, with specific verb 'Checks' and resource 'local model'. No sibling tools exist, but it distinguishes itself by its specific cost-saving role.

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?

Explicit when to call: 'Call this BEFORE your agent routes any task to a cloud model'. Provides strong guidance: 'Do not route to cloud without checking local viability first'. Also explains scenarios for using data_sensitivity and quality_threshold.

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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