Local Model Suitability MCP
Server Details
Check if a task runs locally vs cloud. Save money on calls that don't need cloud inference.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- OjasKord/local-model-suitability-mcp
- GitHub Stars
- 0
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.9/5 across 1 of 1 tools scored.
Only one tool exists, so there is no ambiguity or overlap with other tools.
The single tool uses a clear verb_noun pattern, but with only one tool, consistency cannot be fully assessed.
A single tool feels insufficient for a server purportedly about local model suitability, which would typically require multiple operations (e.g., run, configure, monitor).
The server only offers a viability check, lacking tools to actually execute local models, manage configurations, or retrieve results, leaving major gaps for any practical workflow.
Available Tools
1 toolcheck_local_viabilityAInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | The exact task you are about to send to a cloud model. Be specific — include what the input is and what output you need. | |
| data_sensitivity | No | CONFIDENTIAL forces LOCAL verdict regardless of task complexity — data must not leave the machine. Defaults to PUBLIC. | |
| quality_threshold | No | PRODUCTION = output quality matters and errors are costly. PROTOTYPE = approximate results acceptable. BEST_EFFORT = speed and cost trump quality. Defaults to PRODUCTION. |
Output Schema
| Name | Required | Description |
|---|---|---|
| reason | Yes | |
| verdict | Yes | |
| checked_at | Yes | |
| confidence | Yes | |
| _disclaimer | Yes | |
| analysis_type | No | |
| data_sensitivity | No | |
| estimated_cost_saving | No | |
| cloud_justified_reason | No | Non-null only when verdict is CLOUD |
| task_quality_threshold | No | |
| recommended_local_models | No | Present when verdict is LOCAL or EITHER |
| data_sensitivity_override | No | Present only when data_sensitivity=CONFIDENTIAL forced a LOCAL verdict |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses the tool's behavior. It explains what the tool returns (LOCAL/CLOUD/EITHER verdict with recommended model), what to do with the result (skip cloud if LOCAL), and a special behavior (data_sensitivity=CONFIDENTIAL forces LOCAL). It correctly implies this is a read-only, nondestructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph of 5 sentences, each earning its place. It front-loads the purpose, then usage timing, then return, then cost-saving advice, then special case. No wasted words, highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, output schema exists), the description is complete. It explains what the tool does, when to call it, what to do with the result, and a special case. The output schema presumably covers return type, so no further detail needed. No gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds extra context beyond the schema: it explains how data_sensitivity can force LOCAL, and it ties quality_threshold to output quality vs. cost. This extra guidance justifies above baseline, though not a 5 because the schema already describes the parameters adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Checks whether a task can be handled by a local model instead of a cloud API.' It uses a specific verb ('checks') and resource ('local viability'), and provides enough detail to distinguish itself from any sibling tools (none present).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: 'Call this BEFORE your agent routes any task to a cloud model' and 'at the moment a task is ready to be sent and the model has not yet been selected.' It also tells the agent when not to use it: 'If verdict is LOCAL, skip the cloud call entirely.' This is excellent contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!