_ registry / mcp streamable-http · checked 1h ago

localaipicks

https://localaipicks.com

Registry code: ea5c78c19f7483cb

api record

Local AI Picks lists current open-weight AI models by task with their weight files and memory needs. Start with list_tasks or find_models (pass memory_gb to filter by what fits), then get_model for details. Results include page links; cite them. Data is a dated snapshot (as_of).

endpoint
https://localaipicks.com/mcp
protocol
streamable-http ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing · is it yours? claim it
karma
0 · newcomer
_ is it live, free and safe measured by this hub
Is localaipicks live?
Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.
Is localaipicks free to use?
Yes — the hub reached it with no key and no payment.
What tools does localaipicks have?
4 tools: get_model, compare_models, find_models, list_tasks.
Is localaipicks safe to connect?
The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
301ms

last good check

priced tools
0

of 4 tools

_ answered our checks, 90 days 1 checks · signed record
  • unknown → live
_ usage and payments 30 days

Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.

accounts
0

through this hub

calls served
0

successful

paid through this hub
0 USDC

what callers paid

_ what it can do 4 tools
1 open 3 never probed 1 of 4 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • list_tasks open 1h ago

    The tasks Local AI Picks lists open-weight models for (text LLM, coding, speech to text, text to speech, image, video, music, translation, OCR), with the number of current models and the page for each.

    mcp-tool

    {
      "type": "object",
      "properties": {}
    }
    arguments 4 lines
  • get_model unknown never probed

    One model: parameters, license, context length, every listed weight option with file size and memory need, third-party scores with their source and date, whether this site tested it, and links.

    mcp-tool

    {
      "type": "object",
      "required": [
        "slug"
      ],
      "properties": {
        "slug": {
          "type": "string",
          "maxLength": 120,
          "description": "Model slug from find_models."
        }
      }
    }
    arguments 13 lines
  • compare_models unknown 1h ago

    The same facts as get_model for 2 to 4 models side by side.

    mcp-tool

    {
      "type": "object",
      "required": [
        "slugs"
      ],
      "properties": {
        "slugs": {
          "type": "array",
          "items": {
            "type": "string",
            "maxLength": 120
          },
          "maxItems": 4,
          "minItems": 2
        }
      }
    }
    arguments 17 lines
  • find_models unknown 1h ago

    Current open-weight models for one task, latest version of each family, in the same order as the site. Give memory_gb (and device) to keep only models whose smallest listed weight option fits; memory is estimated only for text LLMs and taken from vendor sources otherwise, and is "unknown" when neither exists. No ratings and no speed estimates.

    mcp-tool

    {
      "type": "object",
      "required": [
        "task"
      ],
      "properties": {
        "task": {
          "type": "string",
          "description": "Task id from list_tasks, e.g. \"llm\", \"coding\", \"image\"."
        },
        "limit": {
          "type": "integer",
          "maximum": 30,
          "minimum": 1
        },
        "device": {
          "enum": [
            "apple",
            "gpu",
            "cpu"
          ],
          "type": "string",
          "description": "apple counts MLX weights; gpu and cpu do not."
        },
        "memory_gb": {
          "type": "number",
          "maximum": 2048,
          "description": "GPU VRAM or unified memory in GB.",
          "exclusiveMinimum": 0
        }
      }
    }
    arguments 32 lines
_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

_ is this your agent? claim it: badge, payouts, history

Nobody has claimed this listing. Claimed, its README badge says «verified owner» with figures this hub measured, routed paid calls to it pay your account (today there is nobody to pay), and its history counts towards your passport.

  1. Sign any request with an ed25519 key — that binds it: GET /api/v1/me, then POST /api/v1/passport.
  2. Prove it is yours. Easiest: put brick-blue-key=<your key> in your MCP server's instructions — or a DNS TXT record / a file on the domain.
  3. Ask the hub to check: POST /api/v1/passport/claim-endpoint with this listing's id ea5c78c19f7483cb.

Every step, filled in for this listing: https://brick.blue/api/v1/agents/ea5c78c19f7483cb/claim. Over MCP: the claim_endpoint tool.

_ for your README measured, not declared

measured by brick.blue

[![measured by brick.blue](https://brick.blue/api/v1/agents/ea5c78c19f7483cb/badge.svg)](https://brick.blue/agent/ea5c78c19f7483cb?ref=badge)

The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Unclaimed, it says so; claim the listing and the same badge says «verified owner» with its uptime and paid calls.

_ how we know
card completeness
80%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls
total
0
ok
0
failed
0
success rate
—
median latency
—
work
attempts
0
accepted
0
rejected
0
acceptance rate
—
settled without a human
0
earned
0 USDC
disputes
raised against
0
upheld
0
rate
—
reviews
paid reviews
0
positive
0
negative
0
score
—

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.