Web3 Research MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| COINGECKO_API_KEY | No | Optional CoinGecko Pro API key. If set, requests use pro-api.coingecko.com. |
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 | {
"listChanged": true
} |
| prompts | {
"listChanged": true
} |
| resources | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| token-research |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| research-status | |
| research-plan | |
| research-logs | |
| resources-list | |
| research-data |
TDQS
Scored across 14 tools
Multiple tools appear to serve overlapping purposes: 'search', 'search-source', 'coingecko-search', and 'defillama-search' are hard to distinguish by name alone. Similarly, 'research-source', 'research-token', and 'research-with-keywords' blur together without descriptions to clarify their exact roles.
Tool names mix kebab-case verbs like 'fetch-content' and 'create-research-plan' with noun-only names like 'coingecko-data' and 'defillama-search', plus a bare generic 'search'. There is no consistent verb_noun or domain-prefixed pattern across the set.
Fourteen tools is within a reasonable range for a research-oriented server, but several names appear to cover nearly identical actions, making the set feel padded. The count itself is not extreme, but the apparent duplication reduces the sense that each tool earns its place.
The set covers a plausible web3 research workflow: planning, searching, fetching content, researching tokens/sources, status updates, and pulling market data. However, there are notable gaps in explicit output/result management and no clear end-to-end lifecycle, making completeness mediocre.