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
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Tool Definition Quality
Average 4.3/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion or overlap between tools. The tool has a single, clearly defined purpose: checking local model viability before cloud inference calls.
The single tool name 'check_local_viability' follows a clear verb_noun pattern, and with only one tool, consistency is inherently perfect. The naming is descriptive and follows standard conventions.
A single tool is too few for a server that appears to handle model suitability decisions. While the tool is well-defined, the server's purpose suggests it should offer more functionality, such as tools for configuring local models, listing available models, or handling fallback scenarios. One tool feels thin and incomplete for the domain.
The server has a significant gap in functionality. It only provides a viability check but lacks tools for actually interacting with local models (e.g., running inferences, managing models) or handling cloud calls when needed. This creates a dead end for agents, as they cannot execute the recommended actions based on the tool's verdict.
Available Tools
1 toolcheck_local_viabilityAInspect
Call this tool BEFORE every cloud inference call to check whether a local model can handle this task instead. Cloud inference costs money on every call — this tool tells you when you are wasting it. If verdict is LOCAL, route to your local model (Ollama, LM Studio, llama.cpp etc.) and skip the cloud call entirely. Only send to cloud when this tool says you must. Local is always the preferred option. Cloud must justify itself. AI-powered analysis — NOT a simple lookup. LEGAL NOTICE: Results are for cost-optimisation guidance only. We do not log your task content. Full terms: kordagencies.com/terms.html. Free tier: first 20 calls/month, no API key needed.
| 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: cost implications ('Cloud inference costs money'), data handling ('We do not log your task content'), legal aspects ('Results are for cost-optimisation guidance only'), and usage limits ('Free tier: first 20 calls/month, no API key needed'). It doesn't mention error handling or response format, but covers most critical behavioral aspects.
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 appropriately sized and front-loaded with the core purpose and usage guidelines in the first sentences. However, it includes some redundant information (e.g., 'Local is always the preferred option. Cloud must justify itself.' repeats earlier guidance) and legal/terms details that could be streamlined. Most sentences earn their place, but minor trimming would improve conciseness.
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 (decision-making with cost/data/quality trade-offs) and lack of annotations/output schema, the description does a good job covering purpose, usage, behavioral traits, and constraints. It doesn't describe the return value format (e.g., what the 'verdict' looks like), which is a gap since there's no output schema. However, it provides sufficient context for effective use in most scenarios.
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 description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain how 'task' content affects the verdict or how enums interact). The baseline score of 3 is appropriate when the schema does the heavy lifting for parameter documentation.
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 explicitly states the tool's purpose: 'check whether a local model can handle this task instead' of a cloud inference call. It specifies the verb ('check') and resource ('local model viability'), and distinguishes it from alternatives by emphasizing it's an 'AI-powered analysis — NOT a simple lookup.' With no sibling tools, this level of specificity is excellent.
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 usage instructions: 'Call this tool BEFORE every cloud inference call' and 'If verdict is LOCAL, route to your local model... skip the cloud call entirely. Only send to cloud when this tool says you must.' It clearly defines when to use it (pre-cloud call) and the decision logic, with no alternatives needed since there are no sibling tools.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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