Find language models by price and context window, from a catalogue read daily across sixty providers. Use this before choosing a model: prices move without announcement and training data is out of date on the day it ships.
| Name | Required | Description | Default |
|---|---|---|---|
| maxPrice | No | Maximum USD per million prompt tokens. | |
| provider | No | ||
| minContext | No | Minimum context window in tokens. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the catalogue is read daily across sixty providers and warns that prices can change without notice and training data becomes outdated, which is valuable behavioral context. It does not cover return format or error behavior, but for a read-only search, this is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences; the first states purpose and data source, the second gives usage advice. No redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, key filters, data freshness, and usage timing. It lacks an explicit return-type description, but given the tool's simplicity and absence of an output schema, the essential context is provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes maxPrice and minContext, but the provider parameter lacks a description. The tool description mentions 'price and context window' but does not clarify provider semantics or allowed values. With 67% schema coverage, the description adds some context but not enough to fully compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds language models filtered by price and context window, and it emphasizes the catalogue's daily refresh and provider scope, distinguishing it from sibling tools like model_price_history. The verb 'Find' plus resource and criteria is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance: 'Use this before choosing a model' and explains the rationale (prices move without announcement, training data stale). It does not name alternative tools explicitly, so it lacks when-not guidance, earning a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct query type: package status, stack review, provider incidents, model pricing, watchlist changes, etc. Even similar-sounding tools like check_package and check_stack are clearly differentiated by granularity (single package vs. whole manifest). The descriptions further remove ambiguity.
All names use lowercase snake_case, but the pattern is mixed: some are imperative verb_noun (check_package, find_model, watch_add) while many are noun phrases (advisory_severity, provider_incidents, runtime_deadlines). This is readable but not a consistent verb_noun style, so there is noticeable inconsistency.
At 31 tools, the count exceeds the 'too many' threshold (25+). While the domain is broad, the agent must navigate a large surface with many similarly scoped utilities, making selection harder. A more consolidated set (e.g., grouping related readings) would improve appropriateness.
The tool surface covers a wide range of supply-chain intelligence: package advisories, provider status, model pricing, runtime EOL, and watchlist changes. The only notable gap is lifecycle management for the private watchlist (e.g., no watch_remove or watch_list), but the overall coverage is strong.