geo_kitGénère un kit GEO lecture seule à partir d'une URL publique : llms.txt, verdict robots IA, schemas JSON-LD. N'écrit rien sur le site cible et n'appelle aucun modèle.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| geo_kit | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint and destructiveHint annotations, the description explicitly states that it writes nothing to the target site and calls no model. These are meaningful behavioral guarantees that help an agent assess side effects and cost even though annotations already cover safety.
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?
Two short sentences front-load the main purpose and then add the critical behavioral caveats. Every word earns its place, with no repetition of schema details.
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?
For a one-parameter tool with an output schema, the description adequately covers what the tool generates, the input requirement, and the read-only/no-model behavior. No critical information appears missing.
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?
The schema only declares a string named 'url' with length constraints, and has 0% description coverage. The description adds the crucial semantic that the URL must be public and is the input source for the kit, which is enough for a single, self-explanatory parameter.
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 uses a specific verb ('Génère') and names the resource ('un kit GEO') plus concrete outputs (llms.txt, verdict robots IA, schemas JSON-LD). It clearly distinguishes this tool from audit/explanation siblings by framing it as kit generation.
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 implies the tool should be used for generating a read-only GEO kit from a public URL, but it does not explicitly state when to prefer it over related tools like geo_audit. The public-URL constraint gives some guidance, but exclusions and alternatives are absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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).
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.
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.
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.