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compare_models

PREMIUM (1 credit, $0.02). Pick between models in one call: pricing, benchmarks, status, and recent news for 2 to 5 models side by side, with cheapest-blended and per-benchmark rankings, so you choose without scraping each provider. Pay per call with an x402 wallet payment (arguments.payment, or an X-PAYMENT header; strict HTTP-402 transport at https://mcp.tensorfeed.ai/mcp?x402=strict) or use an Authorization: Bearer tf_live_... credits token. No token and no USDC? Claim free trial credits by signing a wallet message at https://tensorfeed.ai/api/payment/trial-credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsYesComma-separated list of 2-5 model ids or display names. Examples: "Claude Opus 5.5,GPT-6.1 Sol,Claude Haiku 5.5" or "opus-5-5,gpt-6-1-sol,haiku-5-5"
paymentNoOptional base64 x402 payment payload (the same string an X-PAYMENT header would carry). Sign against the accepts[0] requirement returned by an unpaid call. Alternative to the Bearer credits token.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses cost (1 credit / $0.02), that payment is per call via x402 wallet or Bearer credits token, the strict HTTP-402 transport endpoint, and a free-trial fallback path. This is exactly the auth, cost, and transport context an agent needs before invoking.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The cost caveat is correctly front-loaded and the core value proposition follows immediately, but the second half is a dense run-on string of payment alternatives (arguments.payment, X-PAYMENT header, strict 402 URL, Bearer token, trial URL) that is hard to parse and could be structured more cleanly.

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

Completeness4/5

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

With no annotations and no output schema, the description still covers the essentials: what it returns (pricing, benchmarks, status, news, cheapest-blended and per-benchmark rankings) plus cost, auth, and transport. Only the exact return shape and pagination/limits are left implicit, which is acceptable since the return content is enumerated.

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%, so both parameters are already documented, which sets the baseline at 3. The description restates the 2-5 model range and references arguments.payment but adds no syntax or format detail beyond what the schema examples already give.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: comparing 2-5 models side by side with pricing, benchmarks, status, news and rankings in one call. It clearly differentiates itself as the aggregated multi-model comparison, though it never names the narrower siblings (get_models, benchmark_series, pricing_series) it overlaps with.

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

Usage Guidelines3/5

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

Implies when to use it via 'so you choose without scraping each provider', which frames it as the convenience aggregator over per-provider lookups. However, it never states when NOT to use it or which sibling to prefer for a single model, one benchmark series, or a pricing history, leaving the routing choice to inference.

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

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