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LLM Abacus

by com.llmabacus
OverviewDiscussionsAdmin

Server Details

Compare up-to-date pricing for 40+ LLMs (incl. Chinese) & estimate cost from tokens. EN/zh.

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Status
Unhealthy
Uptime
95.8% over 64 days
Last Tested
Transport
Streamable HTTP
URL

TDQS

A3.9/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one estimates input-side costs for text/tokens (a calculation service), the other queries model price listings (a reference/data service). There is no overlap or ambiguity between them; the shared context of model pricing is handled without duplication.

Naming Consistency4/5

Both tools use a verb_noun pattern: 'estimate_cost' and 'query_model_price'. While the pattern is consistent (verb + object), the objects differ in abstraction—'cost' vs. 'model_price'—but this is acceptable. A minor deviation is that 'query_model_price' could be seen as a query action, while 'estimate_cost' is a computation, but the naming style remains coherent.

Tool Count3/5

With only 2 tools, the server is minimal. For a focused utility like LLM cost estimation, 2 tools might be sufficient, but it feels slightly thin because an agent might expect additional related capabilities, such as output-cost estimation or a combined estimate. However, the purpose is clearly narrowed to input-side cost and price queries, so it is borderline.

Completeness2/5

The domain is LLM cost estimation, but the tool surface is incomplete: 'estimate_cost' explicitly warns that it only handles input cost and does not include output tokens, but there is no tool to estimate full round-trip costs or to combine input/output. It lacks a tool to estimate output cost directly, and while 'query_model_price' provides output prices, the agent must manually integrate them. This is a significant gap because the core use case of estimating a complete API call cost is not fully supported.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool update
    • Changedestimate_cost1 field changed
      • addedInput schema / properties / time_band
        Added value: +{
+  "description": "分时模型的计费时段。缺省为 peak（峰时/最坏价，避免低估）；只有确认调用发生在官方空闲时段时才传 off_peak。",
+  "enum": [
+    "peak",
+    "off_peak"
+  ],
+  "type": "string"
+}
  2. 2 tool updates
    • Changedestimate_cost2 fields changed
      • changedInput schema / properties / model / description
        Previous value: -"模型 ID 或模糊名称（例如 'deepseek-v4-pro'、'gpt-5-5'）"New value: +"模型 ID 或模糊名称，大小写不敏感（例如 'deepseek-v4-pro'、'gpt-5'、'claude-sonnet'）。匹配规则与 query_model_price 一致；不确定名称时可先调 query_model_price 确认。"
      • changedInput schema / properties / text_or_tokens / description
        Previous value: -"待评估的文本内容，或者是纯 tokens 数量的数字字符串（如 '200000'）"New value: +"二选一：① 待估算的自然语言文本（如一段提示词或整篇文档），工具按中文字符 + 英文词估算 token 数；② 纯数字字符串表示已知 token 数，如 '200000'。注意：整串都是数字时一律按模式 ② 处理——若确实要估算一串数字本身的 token 数，请混入任意非数字字符。"
    • Changedquery_model_price1 field changed
      • changedInput schema / properties / model / description
        Previous value: -"模型 ID 或模糊名称（例如 'gpt-5'、'deepseek-v4'、'claude-sonnet'）"New value: +"模型 ID 或模糊名称，大小写不敏感（例如 'gpt-5'、'deepseek-v4'、'claude-sonnet'）。匹配顺序：精确匹配 ID → 精确匹配名称 → 按包含关系匹配 ID → 按包含关系匹配名称，取首个命中。"
  3. 2 tool updates
    • First observedestimate_cost
    • First observedquery_model_price

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