OutilsIA — Conseiller IA locale

Vérifier un PC pour l'IA locale

check_pc_for_local_ai

Read-only

Estime quels modèles IA locaux conviennent à partir de caractéristiques que l'utilisateur fournit explicitement. Ce n'est pas un scan et l'outil ne doit pas être appelé si CPU, RAM, GPU et VRAM manquent.

Input Schema

TableJSON Schema

Name	Required	Default
`usage`	No	polyvalent
`ram_gb`	Yes
`os_name`	No
`vram_gb`	Yes
`cpu_name`	Yes
`gpu_name`	Yes
`cpu_cores`	No
`gpu_vendor`	No
`unified_memory`	No
`storage_free_gb`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`decision`	Yes

TDQS

B3.4/5.0

Behavior4/5

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

Annotations already provide readOnlyHint and destructiveHint, signaling a safe read operation. The description adds behavioral context: it is not a scan and only uses provided characteristics, which clarifies the tool's limitations beyond what annotations convey. 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?

The description is two sentences, front-loaded with the primary purpose and a guardrail. It wastes no words but omits essential parameter detail; however, conciseness is achieved given the limited scope of what is described.

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

Completeness2/5

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

Despite the complexity of 10 parameters and an output schema, the description is incomplete: it lacks parameter explanations, output format (though output schema exists), and any guidance for sibling differentiation. The tool has a read-only annotation but the description doesn't leverage that to reduce burden; it leaves significant gaps for an agent.

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

Parameters1/5

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

Schema description coverage is 0% and the description offers no explanation of the 10 parameters, their meanings, or defaults. For example, the 'usage' enum values (e.g., 'polyvalent', 'code') are not defined, and fields like 'unified_memory' or 'cpu_cores' have no context. The agent has no guidance on how to fill these parameters correctly.

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: estimating which local AI models are suitable based on explicitly provided hardware characteristics. It differentiates from siblings by implying this is a compatibility check based on user-provided specs, not a scan or budget-driven recommendation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says the tool should not be called if CPU, RAM, GPU, and VRAM are missing, providing a clear precondition. However, it does not guide the agent on when to choose this tool over sibling tools like 'list_models_for_budget' or 'recommend_runtime', leaving situational use ambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All OutilsIA — Conseiller IA locale tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0

Disambiguation4/5

Each tool has a distinct purpose (analyze, check, explain, list, etc.) and the detailed descriptions make boundaries clear. However, the difference between 'reading a shared report' (analyze_shared_report) and 'reading a shared report to list installed models' (list_installed_models_from_report) or 'reading a shared report to list benchmarks' (list_benchmark_proofs_from_report) could cause an agent to pick the wrong one. There is also some overlap between these list operations that read a report versus simply displaying a pre-generated cockpit (render_machine_cockpit).

Naming Consistency4/5

The majority of tools follow a consistent verb_noun pattern (e.g., analyze_shared_report, explain_bottleneck, list_installed_models_from_report, recommend_runtime). Minor inconsistency exists with the use of 'geo_audit' vs 'geo_kit' vs 'ratings' and the verb tense in 'list_benchmark_proofs_from_report' and 'list_first_party_measurements' departs from a simple pattern.

Tool Count4/5

15 tools is at the high end of the ideal range (3-15), but each tool appears justified given the comprehensive scope of local AI assistance (hardware checking, model lookup, benchmarking, reporting, educational/reference tools). Slightly over-stuffed but still manageable for an agent.

Completeness4/5

The surface covers a complete workflow: check hardware, lookup models, benchmark, generate cockpit, explain bottlenecks, simulate upgrades, and reference documentation. The missing piece is the lack of 'update' or 'delete' operations, but this is by design, as the entire workflow is read-only. The set seems like a complete view of all possible read-only interactions with the domain.

Resources

Need Help?