hubris-mcp

Search models

models_search

Read-onlyIdempotent

Search model catalog by name, capability, price ceiling, and context window; sort by price, context, or popularity.

Instructions

Find models that fit a task. Filters by name, capability, price ceiling and context window, and can sort the result. This is the tool to reach for when choosing a model — it does the filtering server-side and returns at most 50 rows.

All filters combine with AND, and every one of them is optional: calling with no arguments returns the first 50 active models.

Two things worth knowing about prices. maxInputPer1MRub keeps only models billed per token, because a ruble-per-million ceiling is meaningless for a model billed per image. The cheap_input and cheap_output sorts push non-token models to the end of the list for the same reason — their token rate reads as zero, which would otherwise put video models at the top of "cheapest".

Input Schema

TableJSON Schema

Name	Required	Description
`sort`	No	cheap_input / cheap_output — ascending token price, models not billed per token go last. big_context — widest context window first. popular — most used on Hubris first. Unsorted when omitted.
`query`	No	Case-insensitive substring matched against the model id and display name, e.g. "claude" or "gpt".
`capability`	No	Keep only models with this capability. vision — reads images. image_gen — draws them. transcription — speech to text via /v1/audio/transcriptions. audio_in — accepts an audio recording in chat. audio_out — answers with speech. audio — legacy, matches either audio direction. tools — function calling. reasoning — extended thinking. web_search — built-in web search.
`minContext`	No	Keep only models whose context window is at least this many tokens.
`maxInputPer1MRub`	No	Ceiling on the price of 1M input tokens, in rubles. Also restricts the result to models billed per token, since the ceiling means nothing for models billed per image or minute.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`models`	Yes	Matching models, at most 50.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 8, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description goes well beyond the annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false). It explains that filtering is done server-side and returns at most 50 rows, that calling with no arguments returns the first 50 active models, and it gives detailed behavioral insight into price-related filters and sorting (how non-token models are handled). This is rich, non-obvious behavior that an agent needs to know. No contradiction with annotations.

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?

The description is about 150 words, front-loaded with the core purpose, then expands on optional filters and the price quirks. Every sentence adds value; there is no filler. The structure is logical and easy to scan.

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's complexity (5 optional parameters, sorting, price behavior) and the presence of an output schema (so return format is covered), the description is complete. It explains defaults, edge cases, and the reasoning behind price-related behavior. An agent has everything needed to call this tool correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds meaningful nuance to the tricky parameters: it explains that maxInputPer1MRub restricts results to token-billed models, and that cheap_input/cheap_output sorts push non-token models to the end. This adds value beyond the schema descriptions. It does not add much for the simpler parameters (query, capability, minContext), but those are self-explanatory in the schema.

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 'Find models that fit a task' and lists the specific filters (name, capability, price ceiling, context window) and sorting. It also positions itself as the tool to use when choosing a model, distinguishing it from siblings like models_list and models_get_pricing. The verb 'search' is specific and the resource is models, making the purpose unambiguous.

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?

It explicitly says 'This is the tool to reach for when choosing a model', which gives clear usage guidance. It also explains that all filters are optional and how they combine (AND). However, it does not explicitly mention when not to use this tool or name alternative tools, so it stops short of a 5.

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

Install Server