blocknative-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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| predict_gas_priceC | |
| estimate_gas_costB | |
| get_supported_chainsA | |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| gas_price_query | Prompt template for querying gas prices at a specific confidence level and chain. Parameters: - confidence (int): The confidence level for gas price prediction (0-100). - chain_id (int): The ID of the blockchain network (e.g., 1 for Ethereum Mainnet). Default: 1. |
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 with no overlap: estimate_gas_cost calculates transaction costs, get_supported_chains lists available networks, and predict_gas_price forecasts prices. The descriptions clearly differentiate these gas platform functionalities, making tool selection unambiguous for an agent.
All three tools follow a consistent verb_noun naming pattern (estimate_gas_cost, get_supported_chains, predict_gas_price) with clear action-object relationships. The naming is uniform throughout, using snake_case consistently without any deviations or mixed conventions.
With only 3 tools, the server feels somewhat thin for a gas platform domain that could benefit from additional operations like historical gas price analysis, transaction simulation, or multi-chain batch operations. While the core functions are covered, the limited tool count may restrict agent capabilities in more complex scenarios.
The toolset covers the essential gas platform operations well: cost estimation, chain discovery, and price prediction. However, there are minor gaps such as the inability to simulate transactions with actual data, compare gas prices across multiple chains simultaneously, or access historical gas price trends, which could limit some advanced workflows.