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Glama

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Alternatives to Where My Hug At?

  • A
    license
    A
    quality
    A
    maintenance
    Odds are, most of what you want to build is already open source. Tell your AI agent what you're making, and it finds it: which repos to use as they are, which to adapt, and what's left for you to write. Every repo it names is real, licensed for your use or flagged, and checked live.
    3
    124 npm
    1
    MIT
  • A
    license
    A
    quality
    A
    maintenance
    thicken turns photos of a real object into a GLB model at its real size and its metadata as JSON, read off the object by vision models and looked up in public catalogs. What it knows about each kind of object lives in a profile: a few come with thicken, and you can add your own.
    2
    147 npm
    1
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Related Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    Discovers LLM models in real time from cloud providers and local Ollama instances, returning compatibility profiles and live pricing so AI agents can route tasks to the cheapest viable model without breaking tool calls or context clipping.
    10
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  • F
    license
    Not graded
    quality
    A
    maintenance
    Enables users to describe an LLM fine-tuning job in natural language and receive ranked GPU recommendations from a dozen cloud providers, based on estimated VRAM and FLOPs requirements. It surfaces the cheapest, fastest, and most balanced hardware options while disclosing the source and freshness of each price.
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TDQS

A4/5.0

Scored across 3 tools

Disambiguation5/5

The three tools are explicitly numbered as steps 1, 2, and 3 of a single pipeline, each with a clearly bounded role (intake, search, verify). There is no realistic way for an agent to confuse gathering answers with searching hubs or verifying picks.

Naming Consistency4/5

All three names share the where_my_hug prefix and use predictable suffixes (base, _search, _verify) in snake_case. The entry tool lacks a verb-like suffix, a minor deviation, but the pattern remains obvious and readable.

Tool Count5/5

Three tools map exactly onto the three phases of a linear recommendation workflow, with each step earning its place. This is well-scoped and avoids fragmenting the pipeline into unnecessary tools.

Completeness4/5

The surface covers the full lifecycle of the stated purpose: clarifying questions, multi-hub search, and live verification with actionable nextAction branching. Since it is an advisory read-only workflow, no create/update/delete operations are needed, leaving only minor gaps like no explicit restart/cleanup tool.

Maintenance

ActivityNo data
ResponsivenessSyncing