LLM Abacus
Server Details
Compare up-to-date pricing for 40+ LLMs (incl. Chinese) & estimate cost from tokens. EN/zh.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Full call logging
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Tool access control
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Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.5/5 across 2 of 2 tools scored.
Both tools serve distinct purposes: one calculates actual costs from text/tokens, the other fetches unit prices. No overlap in functionality.
Both tool names follow a consistent verb_noun pattern using snake_case, making them predictable and readable.
Two tools is minimal but appropriate for a cost estimation server. Could potentially benefit from a model listing tool, but the count is reasonable for the focused domain.
Covers core cost query and estimation, but lacks a tool to list available models, which is a minor gap for agents that need to discover model options before pricing.
Available Tools
2 toolsestimate_cost评估大模型调用成本BInspect
评估给定文本或 Tokens 数量在指定模型上的 API 输入和缓存调用成本（人民币 ¥ 计费）。
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | 模型 ID 或模糊名称（例如 'deepseek-v4-pro'、'gpt-5-5'） | |
| text_or_tokens | Yes | 待评估的文本内容，或者是纯 tokens 数量的数字字符串（如 '200000'） |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions API input and cache cost in RMB, but lacks details on whether costs are approximate, how caching is accounted, or what happens when text is provided instead of token count. Behavioral traits are insufficiently disclosed.
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?
The description is a single, front-loaded sentence covering purpose and key details. It is concise, though could be more structured by separating input clarification or adding example. No waste, but slightly short for a complete picture.
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?
For a tool with no output schema and no annotations, the description lacks details on return value format (e.g., numeric cost, breakdown). It does not address what the user can expect as output, leaving the context incomplete.
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?
Schema coverage is 100%, so the schema already documents both parameters. The description clarifies acceptable values for text_or_tokens (text or numeric string), but this adds minimal meaning beyond the schema. Baseline of 3 is appropriate.
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 explicitly states the verb '评估' (evaluate) and the resource '大模型调用成本' (large model call cost), specifying input types and billing currency. It clearly distinguishes from sibling 'query_model_price' by focusing on cost calculation for a given input rather than listing prices.
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 implies usage for estimating costs given text or token count, but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_model_price查询模型价格AInspect
查询指定大模型（如 GPT-5, Claude, DeepSeek）的最新 API 输入、输出和缓存输入单价（单位：每百万 tokens）。
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | 模型 ID 或模糊名称（例如 'gpt-5'、'deepseek-v4'、'claude-sonnet'） |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'latest' implying real-time data but does not disclose whether it is read-only, rate limits, or error behavior (e.g., if model not found).
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?
Single sentence, front-loaded with action, no waste. Efficiently communicates purpose and scope.
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?
Given simple tool (one parameter, no output schema, no annotations), description covers what is returned (input, output, cache prices), units, and examples. Lacks edge case handling but mostly adequate.
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?
Schema coverage is 100% with parameter 'model' described. The description adds example models but does not provide additional meaning beyond the schema's description of model ID or fuzzy name. Baseline 3 is appropriate.
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 it queries latest API input, output, and cache input unit prices for specified large models, with examples (GPT-5, Claude, DeepSeek) and units (per million tokens). This distinguishes it from sibling 'estimate_cost' which estimates total cost.
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 implies usage for getting current prices but does not provide explicit guidance on when to use this tool versus alternatives, or any exclusions. No comparison with sibling 'estimate_cost'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
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Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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