arabic-ai-atlas
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| searchA | Search the Arabic AI Atlas, a curated catalogue of Arabic models, datasets, benchmarks, tools and organizations with Hugging Face download metrics. Case-insensitive substring match over name, org, notes, tasks and tags; the optional type/country/modality filters are exact. Results are ordered by downloads, most first. Valid values: type: llm, asr, tts, ocr, embedding, dataset, benchmark, tool, agent-skill, org country: SA, AE, EG, QA, MA, JO, TN, LB, KW, OM, BH, INTL modality: text, speech, vision, multimodal, none Example: search(query="speech", type="asr", limit=5) |
| recommendA | Recommend entries from the Arabic AI Atlas for a task, ranked with an explanation. Score = 3 if the task is in the entry's tasks, +2 if the dialect matches, +1 if the task
word appears in its notes; zero-score entries are dropped. Each result carries task: e.g. chat, tts, asr, ocr, embedding, translation. dialect: msa, egy, gulf, lev, magh, iraqi, sudanese, yemeni, classical, mixed (optional; entries lacking dialect data still match on task). on_device: true keeps only entries marked on-device (phone or laptop CPU); false drops those; omit for no filter. license_filter: "open" excludes proprietary/unknown licenses; any other string must equal the license exactly (optional). type: exact entry type, e.g. "tts" to get models only, "dataset" for training data (optional). Example: recommend(task="tts", type="tts", on_device=true, license_filter="open") |
| getA | Fetch one full Arabic AI Atlas entry by its id (e.g. "jais-30b"), including links and metrics. Returns {"error": "unknown id", "id": ...} when no entry has that id. Use Example: get(id="jais-30b") |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
The three tools have clearly distinct purposes: `search` does substring matching with exact filters, `recommend` does task/dialect ranked scoring with explanations, and `get` fetches a single entry by id. The descriptions even cross-reference each other (get says 'use search to find ids'), leaving no realistic selection ambiguity.
All three names are single lowercase verbs (search, recommend, get) in a uniform style with no mixing of conventions. The pattern is predictable and readable.
Three tools is on the thin side, but for a read-only catalogue the discovery/ranking/retrieval split earns each tool's place. Nothing redundant or missing at the surface level.
The read-only domain is well covered: broad search with filters, semantic-ish task recommendations, and full-detail retrieval. Minor gaps exist (no way to list all entries, browse by organization, or enumerate valid filter values beyond the examples), but agents can work around these via search.