AI regulation, data protection and compliance notes from CSIDIA, plus its capability catalogue.
Scored across 4 tools
Each tool has a distinct role: profile/catalogue (capabilities), keyword search (search), full-text retrieval by id (fetch), and needs-based capability matching (match_use_case). The only potential overlap is between search and match_use_case, since both retrieve relevant content from the same corpus, but descriptions clarify that one returns article ids while the other returns matched capabilities with rationale and next steps.
Three tools follow a verb-style convention (search, fetch, match_use_case) but match_use_case uses snake_case verb_noun while search/fetch are bare verbs, and capabilities is a noun describing a resource. Readable and mostly predictable, but the mix of single-word verbs, a snake_case verb_noun, and a plain noun is a genuine convention inconsistency.
Four tools is lean but each earns its place in a read-only content/retrieval server: catalogue, search, fetch, and use-case matching form a coherent minimal pipeline. It is slightly under the typical range and leaves little room for browsing or filtering, but nothing is redundant.
The read-only lifecycle is covered: discover via search or match_use_case, retrieve full text via fetch, and get the overall catalogue and constraints via capabilities. Minor gaps remain—no way to list all articles or browse by site (csidia vs splidia) or by sector, and fetch depends entirely on ids from search.
Profilo del gruppo CSIDIA e catalogo dei servizi: chi siamo, posizionamento, capacità (cosa facciamo, risultati, settori, modelli di erogazione, pagina, articoli correlati), settori, vincoli e come iniziare. csidia = AI privata con modelli a pesi aperti addestrati o rifiniti sul compito del cliente, sull'infrastruttura di CSIDIA o sulla sua; splidia = fibra ottica/reti/data center. / Profile of the CSIDIA group and its service catalogue: positioning, capabilities, sectors, delivery models, constraints and how to start. csidia = private AI with open-weight models trained or fine-tuned for the client's task; splidia = optical fibre, networks, data centres.
| Name | Required | Description | Default |
|---|---|---|---|
| site | No | Marchio di cui restituire il profilo (default all) / brand to profile | |
| detail | No | false (default) = una riga per capacità, risposta compatta; true = descrizione completa, risultati, domande tipiche e articoli correlati / false (default) = one line per capability; true = full detail | |
| language | No | Lingua dei testi (default it) / text language | |
| capability_id | No | Dettaglio di una sola capacità, per id (es. 'conformita-documentale-assistita') / detail for a single capability id |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full behavioral burden. It makes clear this is a passive read of profile/catalogue content with no evident side effects, and the brand definitions help interpret results, but it never states the read-only nature explicitly, never describes the response shape or size (that detail lives only in the schema descriptions), and says nothing about permissions or limits.
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 content is front-loaded with what is returned, but it is one long run-on enumeration and the full Italian/English duplication doubles the length while conveying the same facts. The brand glossary is the only part that clearly earns its tokens twice; the list of capability sub-fields could be tightened.
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?
With four optional parameters, no annotations and no output schema, the description does a fair job of saying what domains of content come back and what the two brands mean. It is weaker on return-value expectations (compact vs full responses are only explained in the parameter schema) and on how this tool relates to the sibling tools.
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 a baseline of 3 would apply, but the description earns more: it defines 'csidia' and 'splidia', which are the enum values of the `site` parameter and would otherwise be opaque brand strings. It also frames the capability catalogue that `capability_id` drills into. Defaults and the detail toggle are documented in the schema, not the description, which keeps it from a 5.
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 enumerates the specific content the tool returns: group profile, positioning, capabilities (activities, results, sectors, delivery models, page, related articles), sectors, constraints and how to start. That is a concrete resource with scope, and the brand glossary (csidia = private AI, splidia = fibre/networks/data centres) further pins down what 'capabilities' means here. It does not distinguish itself from the siblings fetch, match_use_case or search, so it stops short of a 5.
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?
Usage is only implied: the phrase 'how to start' plus the profile/catalogue framing signals this is the orientation entry point for questions like 'who is CSIDIA' or 'what services exist'. There is no explicit when-to-use, when-not, or routing to fetch/search/match_use_case, so an agent must infer the boundary itself.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Ritorna il testo integrale di un articolo dato il suo id (l'URL restituito da 'search'). / Returns the full text of an article given its id (the URL returned by 'search').
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Id/URL dell'articolo |
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 implies a read-only operation by stating 'returns the full text', but does not explicitly confirm idempotency or mention any side effects, authentication, or rate limits.
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 extremely concise with no wasted words. Bilingual text is appropriate and 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?
While the tool is simple, the description omits details about the return format (e.g., plain text, HTML, JSON) which would be helpful for an agent to parse the response correctly.
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%, and the description adds value by specifying that the id is the URL returned by 'search', which clarifies the expected input format beyond the schema's brief description.
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 that the tool returns the full text of an article given its id, and explicitly references the sibling tool 'search' as the source of the id, distinguishing it well.
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 indicates that the id should be a URL returned by 'search', providing clear context for use. However, it does not explicitly state when not to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Data la descrizione libera di un'esigenza operativa, restituisce le capacità del gruppo CSIDIA più pertinenti, con il perché (termini che hanno fatto match), il sito di riferimento, il modello di erogazione, gli articoli correlati e il passo successivo. Se non c'è corrispondenza lo dice e restituisce il profilo sintetico. / Given a free-text operational need, returns the most relevant CSIDIA capabilities with the reason for the match, reference site, delivery model, related articles and next step.
| Name | Required | Description | Default |
|---|---|---|---|
| need | Yes | Descrizione libera dell'esigenza o del problema / free-text description of the need | |
| limit | No | Numero massimo di capacità (default 3) | |
| sector | No | Settore di riferimento, id o nome (es. manufacturing, energy, logistics, public-sector, healthcare, defence, telecomunicazioni, data-center, industria) / sector id or name | |
| language | No | Lingua dei testi (default it) / text language |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses return format (matched capabilities with reason, site, etc.) and behavior on no match (says so and returns synthetic profile). Does not mention auth, rate limits, or side effects, but adequately covers core behavior.
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 bilingual sentences, front-loaded with Italian then English. Compact but slightly redundant due to bilingual nature. No waste; every sentence adds value.
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?
Despite no output schema, the description explains return values (reason, site, etc.) and failure case. However, it does not reference optional parameters like limit, sector, or language in the description text. Still sufficient for a matching tool with good schema coverage.
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%, achieving baseline. Description adds no extra meaning beyond schema definitions (e.g., 'free-text need' is already in schema). No parameter-specific elaboration in description.
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 verb ('restituisce/returns') and resource ('capacità del gruppo CSIDIA / CSIDIA capabilities'). It distinguishes itself from siblings: 'capabilities' likely lists all, while this tool matches a specific need. The bilingual text reinforces the specific action.
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 when to use (when you have a free-text need) but provides no explicit guidance on alternatives or when not to use. No comparison to sibling tools like 'capabilities' or 'search', so context-aware selection is left to the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Cerca negli articoli (Note operative) di csidia.com (AI privata: modelli a pesi aperti addestrati sul compito del cliente, conformità, dati riservati) e splidia.com (fibra ottica, reti, data center). Ritorna una lista di risultati con id da passare a 'fetch'. / Searches the operational notes of csidia.com (private AI: open-weight models trained for the client's task) and splidia.com (optical fibre, networks, data centres). Returns results whose id can be passed to 'fetch'.
| Name | Required | Description | Default |
|---|---|---|---|
| site | No | Filtro sito (default all) | |
| limit | No | Numero massimo di risultati (default 8) | |
| query | Yes | Termini di ricerca / search terms | |
| language | No | Filtro lingua (default all) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations supplied, the description does meaningful work: it names the corpus and its subject matter, discloses the return shape (a list of results with ids), and points to the follow-up call. It does not mention ranking behaviour, pagination, or whether the search is read-only, which leaves some burden unmet for a zero-annotation tool.
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 corpus definition and the fetch hand-off are front-loaded, so the essential facts come first. The full bilingual duplication doubles the length, which is defensible for localisation but is still redundant text for a single reader.
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 four-parameter read tool with a fully documented schema and no output schema, the description covers the corpus, the result shape, and the next step, which is enough to call it correctly. The remaining gap is operational detail such as ordering or paging, which matters only at larger result sizes.
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 baseline is 3. The description does add real meaning by explaining what each 'site' value covers topically (private AI vs. fibre/networks/data centres), which helps the agent choose a site filter, but it says nothing about 'limit' or 'language' beyond what the schema already documents.
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?
States a specific verb (searches) plus the exact corpora searched: csidia.com operational notes (private AI, open-weight models, compliance) and splidia.com (optical fibre, networks, data centres). It also distinguishes itself from the sibling 'fetch' by saying results carry ids to be passed there, so an agent can tell the two apart without opening a schema.
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?
Clear context for use: keyword search across the two sites, followed by 'fetch' on returned ids, which establishes the intended two-step workflow. It gives no explicit when-not-to-use guidance, though — nothing tells the agent when 'match_use_case' or 'capabilities' would be the better choice.
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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