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Local Model Suitability MCP

by io.github.OjasKord

check_local_viability

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

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "_disclaimer": {
+      "type": "string"
+    },
+    "analysis_type": {
+      "type": "string"
+    },
+    "checked_at": {
+      "format": "date-time",
+      "type": "string"
+    },
+    "cloud_justified_reason": {
+      "description": "Non-null only when verdict is CLOUD",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "confidence": {
+      "enum": [
+        "HIGH",
+        "MEDIUM",
+        "LOW"
+      ],
+      "type": "string"
+    },
+    "data_sensitivity": {
+      "enum": [
+        "PUBLIC",
+        "INTERNAL",
+        "CONFIDENTIAL"
+      ],
+      "type": "string"
+    },
+    "data_sensitivity_override": {
+      "description": "Present only when data_sensitivity=CONFIDENTIAL forced a LOCAL verdict",
+      "type": "boolean"
+    },
+    "estimated_cost_saving": {
+      "type": "string"
+    },
+    "reason": {
+      "type": "string"
+    },
+    "recommended_local_models": {
+      "description": "Present when verdict is LOCAL or EITHER",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "task_quality_threshold": {
+      "enum": [
+        "PRODUCTION",
+        "PROTOTYPE",
+        "BEST_EFFORT"
+      ],
+      "type": "string"
+    },
+    "verdict": {
+      "enum": [
+        "LOCAL",
+        "CLOUD",
+        "EITHER"
+      ],
+      "type": "string"
+    }
+  },
+  "required": [
+    "verdict",
+    "confidence",
+    "reason",
+    "checked_at",
+    "_disclaimer"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It goes beyond a simple summary by explaining the return format (LOCAL/CLOUD/EITHER verdict with recommended model), the side effect of a LOCAL verdict (skip the cloud call entirely), and the special behavior of the data_sensitivity parameter (CONFIDENTIAL forces LOCAL). This is rich, actionable context that fully informs the agent of the tool's runtime behavior.

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 appropriately sized for the tool's complexity. Every sentence adds value: it states the purpose, the optimal calling time, the return type, the cost-saving action, and the force mechanism. The only minor redundancy is the two sentences about calling 'BEFORE' routing, but this emphasis is arguably intentional to prevent misuse. It is well-structured and front-loaded.

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?

Given the tool has no sibling tools and a comprehensive output schema, the description covers all essential aspects: what it does, when to call it, what it returns, how to force LOCAL, and the general rule to always check. The output schema handles return structure, while the description supplies the surrounding context. Nothing is left ambiguous.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description does not add meaningful semantic detail beyond the schema: the data_sensitivity behavior (CONFIDENTIAL forces LOCAL) is already fully described in the schema, and the task and quality_threshold parameters are likewise already well-documented. No additional guidance is needed, but also none is provided, so it stays at baseline.

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 opens with a clear, specific action: 'Checks whether a task can be handled by a local model instead of a cloud API.' This distinguishes it from the alternative (cloud routing) and states the tool's core function unambiguously. It also names the exact output ('LOCAL/CLOUD/EITHER verdict with recommended model'), leaving no doubt about its purpose.

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?

The description gives explicit, imperative guidance: 'Call this BEFORE your agent routes any task to a cloud model' and 'Do not route to cloud without checking local viability first.' It also specifies the precise moment to invoke the tool ('at the moment a task is ready to be sent and the model has not yet been selected') and the rationale (cost savings). This leaves no ambiguity about when to use it.

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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TDQS

A4.6/5.0
Disambiguation5/5

With only one tool, there is no possibility of confusion. The tool's purpose is clearly described with specific usage instructions and output semantics, making it unambiguous for agents.

Naming Consistency5/5

The tool name follows a clean snake_case verb_noun pattern ('check_local_viability'), which is internally consistent and descriptive. While there are no other tools to compare, the naming is clear and follows standard conventions.

Tool Count3/5

The server contains only one tool, which feels thin for a typical MCP server. However, the tool addresses a specific, narrow purpose, so it is not unreasonable, but according to the rubric, 1-2 tools is borderline.

Completeness5/5

The tool fully covers the server's stated domain of checking local model viability. It returns a clear verdict and recommendation, handles data sensitivity, and provides guidance on when to use it. There are no obvious gaps or dead ends.

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