Skip to main content
Glama

catalog_browse

Browse the model catalog to find candidates for any role. Filter by task, size, or installed status, and search by name or description.

Instructions

Browse the lilbee model catalog (featured entries + Hugging Face).

Lets an agent discover candidates for any model role before pulling.
The catalog is the same one the TUI browses: a curated featured list
augmented with live Hugging Face results when ``featured=false``.

Args:
    task: ``"chat"``, ``"embedding"``, ``"vision"``, ``"rerank"``, or
        ``""`` for all.
    search: Substring filter on name / repo / description.
    size: ``"small"`` (<3 GB), ``"medium"`` (3-10 GB), or ``"large"``
        (>10 GB). Empty = no size filter.
    installed: ``true`` shows only installed repos, ``false`` only
        uninstalled, ``null`` shows both.
    featured: ``true`` restricts to the curated featured list,
        ``false`` skips it (HF results only), ``null`` includes both.
    sort: ``"featured"``, ``"downloads"``, ``"name"``,
        ``"size_asc"``, or ``"size_desc"``.
    limit: Page size (default 20).
    offset: Page offset for pagination.

Returns ``{total, limit, offset, has_more, models}`` where each model
is ``{ref, display_name, task, size_gb, min_ram_gb, downloads,
featured, description}``.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNo
searchNo
sizeNo
installedNo
featuredNo
sortNofeatured
limitNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations are provided, so the description carries full burden. It thoroughly discloses behavior: pagination, source (featured list + Hugging Face), and return format. It explains all parameter effects and default values, making the tool's behavior transparent without 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 well-structured with an overview sentence followed by an parameter list. It is slightly verbose but every sentence adds value. It could be trimmed slightly, but remains clear and organized.

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

Completeness5/5

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

Given the tool has 8 parameters and an output schema, the description covers purpose, usage context, all parameters with formats, and the return structure. The output schema exists, but the description still summarizes the return fields, making it self-contained. No missing elements.

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

Parameters5/5

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

Schema coverage is 0% because the input schema only provides types and defaults. The description adds complete semantics: allowed values for task, size with size thresholds, sort options, and the role of booleans like installed and featured. This fully compensates for the lack of schema descriptions.

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 browses the lilbee model catalog to discover candidates for any model role. This distinguishes it from sibling tools like model_list (installed models) and model_pull, providing a specific verb-resource purpose.

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

Usage Guidelines4/5

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

The description explicitly advises using this tool before pulling models ('before pulling'), indicating it's for discovery. It does not explicitly state when not to use or name alternative tools, but the context is clear. A slight gap exists in not excluding scenarios.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tobocop2/lilbee'

If you have feedback or need assistance with the MCP directory API, please join our Discord server