agentfit-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| count_tokensA | Estimate tokens in a string or chat-message array. Fast, dependency-free, within ~10-20% of true tokenizer counts on English prose. Pass a model name to pick the right per-family estimator (openai, anthropic, google, llama, default). |
| fit_messagesA | Drop messages from the input array until the total is under maxTokens. Three strategies: drop-oldest (default), drop-middle, priority (uses each message's |
| list_estimatorsA | List the built-in estimator families this server knows about. Useful for picking a model alias when the exact model name isn't recognized. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: counting tokens, fitting messages under a token limit, and listing available estimator families. No overlap or confusion.
All tool names follow a consistent verb_noun pattern in snake_case (count_tokens, fit_messages, list_estimators), making them predictable and easy to understand.
With 3 tools, the server is tightly scoped to token estimation and message fitting. Each tool earns its place, and the count is appropriate for this focused domain.
The tool surface covers core operations: counting tokens, fitting messages with multiple strategies, and listing estimators. A minor gap is the lack of a tool for direct model-specific tokenization, but the per-family estimator covers most use cases.