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Glama

search

Find Arabic AI models, datasets, benchmarks, tools, and organizations by name, type, country, modality, or tags, ranked by Hugging Face downloads for relevant results.

Instructions

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)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNo
limitNo
queryYes
countryNo
modalityNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses match semantics (case-insensitive substring on name/org/notes/tasks/tags), that type/country/modality are exact filters, and the result ordering (downloads, most first). It omits any note on auth, rate limits, or what 'limit' can/cannot do, which keeps it below 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the purpose, then match/ordering semantics, then the enum reference block, then a concrete example. Every sentence adds information an agent needs; nothing is boilerplate.

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

Completeness4/5

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

An output schema exists, so return-value explanation is correctly omitted, and the description covers filtering, matching and ordering. The only gap is pagination/limit behavior, which is minor for a catalogue search but not nothing.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate and largely does: it explains that 'query' is a substring match over specific fields, names the three filter params as exact matches, and enumerates every valid value for type, country and modality. Only 'limit' is left without explicit semantics (default 10 is visible in the schema but no max/behavior note).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (search) and resource (Arabic AI Atlas catalogue), and enumerates the entity kinds indexed (models, datasets, benchmarks, tools, organizations). Clear what it does, but it never explicitly distinguishes itself from the sibling tools 'recommend' and 'get'.

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?

Usage is implied by the match semantics and the worked example, but there is no explicit when-to-use / when-not-to-use guidance and no mention of when to prefer 'recommend' or 'get' instead. An agent can infer the use case but must do the routing itself.

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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