Skip to main content
Glama
Sign In
chimeraforge

chimeraforge

by Sahil170595
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

chimeraforge_suggest

Find models that fit your GPU and meet latency, quality, and budget targets. Compare catalog, local Ollama, and Hugging Face options by specifying your hardware.

Instructions

Rank the models that actually fit and hit the SLO on a given GPU -- the inverse of planning. Use for 'what can I run on a 4090', 'best model for 12GB'. Sources: catalog (offline curated set), ollama (locally installed), hf (top Hub repos).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "hardware": "<hardware>"
    },
    "name": "chimeraforge_suggest"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
sourceNocatalog
hardwareYes
hf_limitNo
ollama_urlNo
request_rateNo
context_lengthNo
latency_slo_msNo
quality_targetNo
budget_usd_monthNo
avg_output_tokensNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.30.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations exist, so the description carries the behavioral disclosure burden. It communicates core behavior: ranking models against a fit/SLO objective and drawing from three sources: catalog, ollama, and hf. However, it does not disclose external interactions (e.g., reaching out to Ollama or Hugging Face), side effects, failure modes, or whether the operation is read-only.

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?

Two sentences carry the core behavior, user-facing examples, and source list without redundancy. The most important action is front-loaded: 'Rank the models...' instead of burying the intent in filler.

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

Completeness3/5

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

The description covers core purpose and high-level sources, which is enough to make a reasonable first call with the one required parameter. However, with 11 parameters, no output schema, no annotation safety clues, and 0% parameter documentation coverage, the description omits too much detail to fully steer an agent through meaningful variations of source, SLO, latency, and quality trade-offs.

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

Parameters2/5

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, but it only adds meaning to the source concept ('catalog', 'ollama', 'hf') and general context around SLO. The other 10 parameters, including latency_slo_ms, request_rate, quality_target, hf_limit, and budget_usd_month, are left to the agent to infer from their names and defaults, which is risky for a tool with this much configuration surface.

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 uses a specific verb and object: 'Rank the models that actually fit and hit the SLO on a given GPU.' It clearly identifies itself as the inverse of planning, which separates it from its most likely sibling confusion. The example use cases ('what can I run on a 4090', 'best model for 12GB') make the intent immediately understandable.

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 provides concrete usage framing with quoted examples and explicitly contrasts it with planning. It would be stronger if it also stated when not to use it or pointed to the sibling tools, but the 'inverse of planning' hint gives the agent enough context to route appropriately.

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

Deploy Server

Other Tools