F
license-
qualityC
maintenanceProvides tools to count tokens, estimate API costs, optimize prompts, and compare AI model pricing for multiple LLMs.
112
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
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
MCP client
Glama
MCP server
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
100% free. Your data is private.
Tool Definition Quality
A4.2/5.0
Tool DescriptionsA
Average 4.4/5 across 2 of 2 tools scored. Lowest: 3.8/5.
Server CoherenceA
Disambiguation5/5
The two tools have clearly distinct purposes: one estimates cost based on text or token count, the other queries model pricing. There is no overlap or ambiguity between them.
Naming Consistency5/5
Both tool names follow a consistent verb_noun pattern: estimate_cost and query_model_price. The naming is predictable and well-aligned with their functions.
Tool Count3/5
With only 2 tools, the set is on the thin side. However, the server's scope is narrow (LLM cost estimation), and these two tools cover the essential operations, so the count is borderline but not unreasonable.
Completeness4/5
The core workflow of querying prices and estimating input cost is covered. The notable gap is the lack of a direct tool for full round-trip cost estimation (input + output), but the description explicitly guides users to combine the output price from query_model_price with the input estimate, making it a workable minor gap.
Available Tools
2 toolsestimate_cost评估大模型调用成本ARead-onlyIdempotentInspect
估算一段文本或指定 token 数在某个大模型上的输入侧调用成本，以人民币 ¥ 计价。text_or_tokens 支持两种模式：传自然语言文本时自动估算 token 数（中文按字符、英文按词分别计），传纯数字字符串时直接作为已知 token 数使用。返回：所用估算方法、token 数、按原价计的输入成本；若该模型提供缓存输入价，另给出缓存命中成本、相对原价的节省百分比与倍数差价；末尾附该模型的输入 / 输出 / 缓存输入单价（每百万 tokens）。⚠️ 本工具只计算输入成本，不含输出 token 费用——要估算完整往返成本，需另取输出单价自行相加。
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | 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。 | |
| text_or_tokens | Yes | 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。 |
Tool Definition Quality
A4.9/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (read-only, idempotent), the description discloses the token estimation heuristic (Chinese chars + English words), the dual-mode input behavior, the return structure (method, token count, cost, cache savings), and the caching condition. This is substantial behavioral context that annotations do not cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one well-organized paragraph that front-loads the purpose, then explains modes, return values, and ends with a warning. Every sentence adds necessary info without wasted words, balancing detail with brevity.
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?
Given the tool's moderate complexity (two modes, variable return conditions) and no output schema, the description is complete: it covers inputs, behavior, return values, and the output-cost caveat. The only minor omission is an explicit sibling tool name, but the instruction '另取输出单价' is sufficient for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds the token-count heuristic (Chinese characters vs English words) and clarifies the pure-digit string handling as a known token count, which enriches the semantics of text_or_tokens beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb+resource: '估算一段文本或指定 token 数在某个大模型上的输入侧调用成本' (estimate input-side cost). It clearly distinguishes itself from the sibling query_model_price by focusing on input-only cost and explicitly excluding output token fees.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-not guidance: '只计算输入成本，不含输出 token 费用——要估算完整往返成本，需另取输出单价自行相加' (only calculates input cost; for full round-trip, obtain output unit price and add). This implies the alternative of using the sibling tool to fetch the output price.
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ARead-onlyIdempotentInspect
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）。
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | 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。 |
Tool Definition Quality
A3.8/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive, so the description is not burdened with safety disclosure. It adds minor context like freshness ('最新') and price components, but does not disclose return format, error behavior, or whether results are cached. This is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tightly written sentence that front-loads the action, target, and key details. There is no filler, redundancy, or unnecessary explanation.
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?
For a simple read-only lookup with one well-documented parameter and no output schema, the description conveys what is returned (input/output/cached-input prices) and their unit. It could mention edge cases or the sibling relationship, but the existing content is sufficient for basic invocation.
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?
The input schema covers the single 'model' parameter with detailed matching-order semantics, achieving 100% schema description coverage. The description adds unit context (per million tokens) and example models, but does not meaningfully expand on the schema's parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('查询' / query) and clearly identifies the resource: latest API input, output, and cached-input unit prices for a specified model, with units defined (per million tokens). This distinguishes it from the sibling tool 'estimate_cost', which focuses on cost estimation rather than actual price lookup.
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?
The description implies its usage context (when you need current API prices for a model) but does not explicitly mention alternatives or exclusions. It fails to reference the sibling tool 'estimate_cost' or clarify when to prefer this over cost estimation, so guidance is present but implicit.
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.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
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
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
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
The URL of the server is wrong
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.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- AlicenseAqualityAmaintenanceDaily-verified LLM API pricing dataset (44+ models, CN & global) with a hosted MCP server for live price queries and token cost estimation.42CC BY-4.0
- AlicenseAqualityAmaintenanceLive LLM API pricing: current token prices, model comparisons, cheapest-model lookups, and The LLM Price Index for 150+ models across 20+ providers, re-verified daily. No API key required.51MIT