AI NetCafé

Measured platform cost across models

model_costs

Read-onlyIdempotent

Measured platform cost metadata for one call on each model; your charge is $0.00 during the free beta. Vendors publish per-million-token list prices, but a call's cost depends on how many tokens the model chooses to emit — models differ by an order of magnitude on the same prompt. standard_bench sends an IDENTICAL prompt to every model, so the difference is the model, not the workload — use that to choose a model before bulk work. production_mixed is real traffic and is NOT comparable across models. Free to cite, CC BY 4.0. Example — GET https://ainetcafe.com/t/model_costs

Input Schema

TableJSON Schema

Name	Required	Description	Default
`days`	No	Measurement window in days (default 30).

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4/5.0

Behavior4/5

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

Annotations already communicate read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond this: the free $0.00 charge during beta, the distinction between vendor list prices and actual per-call cost, and the comparability caveat between benchmark and production data. 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.

Conciseness4/5

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

The description is longer than minimal but front-loads the core purpose and pricing, then adds essential comparability guidance. The licensing note and example URL are somewhat extra, but they do not distract from the main message.

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?

The tool has one optional, fully documented parameter, a rich output schema, and annotations that establish safety. The description covers pricing, comparability, intended use, licensing, and an example endpoint, making it complete enough for an agent to decide whether and how to call it.

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 coverage is 100%, and the single parameter 'days' is already described as the measurement window in days with a default of 30. The description does not add parameter-level detail, but it does not need to since the schema fully covers it.

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?

The description clearly states that the tool provides 'Measured platform cost metadata for one call on each model' with an explicit example endpoint. It distinguishes the data's purpose from workload-driven comparisons, though it does not explicitly differentiate from sibling tools like compare_models or list_models.

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 gives concrete guidance: standard_bench data is suitable for choosing a model before bulk work, while production_mixed is explicitly 'NOT comparable across models.' This is clear context, though it does not formally name alternative tools or list when not to call this tool.

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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TDQS

A3.9/5.0

Disambiguation5/5

Every tool has a clearly distinct purpose with detailed descriptions that explicitly differentiate even close pairs like diff_tables vs reconcile_ledger and list_models vs model_costs. No two tools appear to do the same thing, and the what_can_you_do tool further resolves any confusion.

Naming Consistency3/5

The majority of tools follow a verb_noun snake_case pattern (build_app, fetch_page, list_tasks), but several notable deviations exist: ai_visibility, china_reachability, model_costs, json_yaml, pdf_to_markdown, what_can_you_do, recall, remember, and jwt_decode. This mixed convention, while still readable, is not fully consistent.

Tool Count3/5

With 34 tools, the count is high and exceeds the typical comfortable range for an MCP server. However, the server is a broad AI utility platform covering web, data, LLM, conversion, and scheduling tasks, and each tool appears to serve a distinct purpose with little redundancy, making the large but organized set borderline appropriate for its scope.

Completeness3/5

The tool surface covers a wide array of common workflows (search, fetch, table operations, PDF extraction, model comparisons, task scheduling, memory). However, check_job references deep_research, translate_pdf, and make_slides which are not present in the tool list, and there is no update tool for tasks/apps or a way to delete memories, leaving some user journeys incomplete.

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