Etuki FinOps & LLM Pricing Oracle

discover_and_probe_account_models

Read-onlyIdempotent

Retrieves verified production GA models and eligible zero-cost grant tiers (Google Cloud Tier 1, AWS Activate Bedrock, OpenRouter :free) from the embedded UMCE registry. Deterministic local snapshot lookup that issues zero network probes, requires no API keys, and consumes zero user quota. Returns JSON listing zero-cost workhorse models, flagship reasoning models, and recommended role assignments benchmarked against current_model (formatted as 'vendor/model-slug', defaulting to 'openai/gpt-6.1-sol'). Use this tool at session startup to inspect available model tiers before calling auto_route_task_by_quality_tier; use list_supported_models instead when filtering the full 563-entity Merkle registry by vendor.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "discover_and_probe_account_models"
  }
}

Input Schema

Name	Required	Description	Default
`current_model`	No	Active primary model ID in 'vendor/model-slug' format used as the baseline cost reference.	openai/gpt-6.1-sol

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 9, 2026

changedInput schema / properties / current_model / description

Previous value: -"Active primary model ID in the IDE used as the baseline reference."New value: +"Active primary model ID in 'vendor/model-slug' format used as the baseline cost reference."

Changed1 schema field changedNov 9, 2026

changedInput schema / properties / current_model / description

Previous value: -"Current primary model ID"New value: +"Active primary model ID in the IDE used as the baseline reference."

Changed1 schema field changedNov 8, 2026
- addedInput schema / properties / current_model / description
```
Added value: +"Current primary model ID"
```
AddedNov 8, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnly, idempotent, closed-world and non-destructive, but the description adds behavior the annotations cannot express: it is a deterministic local snapshot, issues zero network probes, needs no API keys and consumes zero user quota. It also discloses the shape of the return value (workhorse vs flagship reasoning models plus role assignments), which matters because there is no output schema.

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?

Front-loaded with what is retrieved, followed by behavioral guarantees, then return shape, then routing guidance — a sound order. It is slightly heavy, since "zero network probes / no API keys / zero quota" is a three-way restatement of the same cost-free property, but none of it is filler.

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?

There is no output schema, and the description compensates by summarizing the JSON payload (zero-cost workhorses, flagship reasoning models, role assignments). Combined with the startup-timing guidance and the sibling alternative, an agent has everything needed to call it correctly at the right moment.

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

Parameters3/5

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

Schema description coverage is 100% and the single parameter's format ('vendor/model-slug'), default ('openai/gpt-6.1-sol') and role as a baseline are already documented in the schema. The description restates the format and default and only gestures at the benchmarking use ("recommended role assignments benchmarked against current_model"), which is marginal added meaning rather than new semantics.

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?

Uses a specific verb and resource ("Retrieves verified production GA models and eligible zero-cost grant tiers") plus concrete tier examples (Google Cloud Tier 1, AWS Activate Bedrock, OpenRouter :free) and the registry it reads from. It also names the sibling it is not ("use list_supported_models instead when filtering the full 563-entity Merkle registry by vendor"), so an agent can separate the two without opening either schema.

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

Usage Guidelines5/5

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

Gives an explicit trigger ("Use this tool at session startup") and an explicit ordering constraint relative to auto_route_task_by_quality_tier ("before calling"). It also names the concrete alternative (list_supported_models) and the condition that selects it (filtering the full registry by vendor).

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

All Etuki FinOps & LLM Pricing Oracle tools Inspect

Try in Browser

Claude Code, inside the Godot 4 editorFlockbay puts an AI agent in the real Godot 4 editor: it writes GDScript, runs your game and fixes what breaks. Free, or bring your own Claude Code or Codex.flockbay.comAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?