BrianKnows MCP Server
The BrianKnows MCP server enables AI assistants to connect to BrianKnows' blockchain knowledge base, offering several tools:
Ping Tool: Check if the BrianKnows API server is online/responsive
Search Tool: Query BrianKnows' knowledge engine for blockchain and DeFi information, with ability to specify particular knowledge bases (e.g., Polygon, Lido, Starknet)
Agent Tool: Chat with the BrianKnows agent about DeFi protocols, optionally providing blockchain address and chain ID for context-aware responses
Provides access to Circle's knowledge base through BrianKnows' knowledge engine, allowing users to search for Circle-specific blockchain information.
Provides blockchain knowledge about Ethereum and its Layer 2 solutions through BrianKnows' knowledge engine and agent capabilities.
Offers access to NEAR Protocol's knowledge base, enabling users to search for NEAR-specific blockchain information through BrianKnows' knowledge engine.
Enables searching of Polygon-specific knowledge base through BrianKnows' knowledge engine, providing specialized information about the Polygon blockchain ecosystem.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@BrianKnows MCP Serversearch for Ethereum Layer 2 scaling solutions"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
BrianKnows MCP Server
A Model Context Protocol (MCP) server that connects Claude to BrianKnows' blockchain knowledge base.
What is MCP? 🤔
The Model Context Protocol (MCP) lets AI assistants like Claude Desktop connect to external tools and data sources in a secure way while keeping users in control.
Related MCP server: GOAT MCP Server
What does this server do? 🚀
The BrianKnows MCP server provides three main tools:
Ping Tool: Check if the BrianKnows API server is responsive
Search Tool: Query BrianKnows' knowledge engine for blockchain and DeFi information
Agent Tool: Chat with the BrianKnows agent about DeFi protocols
Supported knowledge bases include:
public-knowledge-box (default)
circle_kb, lido_kb, Polygon_kb, taiko_kb
near_kb, clave_kb, starknet_kb, consensys_kb
The server maintains a cache of your 5 most recent searches for quick reference.
Prerequisites 📋
Node.js (v18 or higher)
Configuration ⚙️
Add this to your Claude Desktop configuration file (accessible via Developer Settings):
{
"mcpServers": {
"brianknows": {
"command": "npx",
"args": ["mcp-brianknows"],
"env": {
"BRIAN_API_KEY": "your-api-key-here"
}
}
}
}Replace your-api-key-here with your actual BrianKnows API key.
Example Usage 🎯
Can you check if the BrianKnows API is online?
Use BrianKnows to search for information about Ethereum's Layer 2 solutions.
Ask the BrianKnows agent to explain how Uniswap V3 works.Features ✨
Multiple Knowledge Bases: Access specialized knowledge for different blockchain protocols
Cached Searches: Quick access to your 5 most recent searches
Error Handling: User-friendly error messages
Type Safety: Full TypeScript implementation
Acknowledgments 🙏
BrianKnows for their blockchain knowledge API
Anthropic for Claude Desktop
Available Tools
3 toolsagentC
Chat with Brian agent
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | User prompt or question | |
| address | No | User blockchain address (required for blockchain operations) | |
| chainId | No | Blockchain chain ID | |
| kbId | No | Knowledge box ID to use (default: public-knowledge-box) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Chat with Brian agent' suggests an interactive conversation, but it doesn't disclose whether this is a read-only operation, what authentication might be required, rate limits, or what kind of responses to expect. The description provides minimal behavioral context.
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?
While technically concise with just three words, this is under-specification rather than effective conciseness. The description doesn't earn its place by providing meaningful information - it's too brief to be helpful. A single sentence with actual content would be more appropriate.
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 4 parameters, no annotations, and no output schema, the description is completely inadequate. It doesn't explain what the tool returns, what 'Brian agent' refers to, or how this differs from standard chat interactions. The description fails to compensate for the lack of structured metadata.
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?
The schema has 100% description coverage, so all parameters are documented in the structured schema. The description adds no additional parameter semantics beyond what's already in the schema. The baseline score of 3 is appropriate when the schema does the heavy lifting for parameter documentation.
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 'Chat with Brian agent' is a tautology that essentially restates the tool name 'agent' without specifying what the tool actually does. It doesn't distinguish this tool from its siblings (ping, search) and provides no specific verb+resource combination that clarifies the tool's function.
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 provides no guidance on when to use this tool versus its siblings (ping, search) or any alternatives. There's no mention of appropriate contexts, prerequisites, or exclusions for using this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pingA
Check if the Brian API server is alive
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the behavioral trait of checking server liveness, which is useful context, but does not mention response format, timeout behavior, or error handling. For a zero-parameter tool with no annotations, this is adequate but leaves gaps in operational details.
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, efficient sentence that directly states the tool's function with zero waste. It is front-loaded and appropriately sized for a simple tool, making it easy to understand without unnecessary elaboration.
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 the tool's low complexity (0 parameters, no output schema, no annotations), the description is complete enough for its purpose. It clearly defines what the tool does, though it could benefit from slight elaboration on expected outcomes or usage scenarios to enhance agent decision-making.
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?
With 0 parameters and 100% schema description coverage, the baseline is 4. The description does not need to add parameter semantics, as there are no parameters to document, and it appropriately focuses on the tool's purpose without redundant information.
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 the specific action ('Check if') and target resource ('Brian API server is alive'), distinguishing it from siblings like 'agent' and 'search' by focusing on health/availability testing rather than data operations. It uses precise language that conveys the exact function without ambiguity.
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 context (verifying server status), but does not explicitly state when to use this tool versus alternatives or provide exclusions. It suggests a diagnostic purpose, which gives clear context, but lacks explicit guidance on scenarios like pre-operation checks or troubleshooting steps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchC
Search using Brian's knowledge engine
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| kb | No | Knowledge box to search in (default: public-knowledge-box). Options: circle_kb, lido_kb, Polygon_kb, taiko_kb, near_kb, clave_kb, starknet_kb, consensys_kb |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Search using Brian's knowledge engine' implies a read-only operation but doesn't specify what kind of results to expect, whether there are rate limits, authentication requirements, or any constraints on the search. The description is too minimal to provide adequate behavioral context for a search tool.
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 extremely concise with just one sentence containing 5 words. It's front-loaded with the core functionality and wastes no words. This is an appropriate level of conciseness for a simple search tool.
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 search tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what kind of results to expect, the format of returns, whether there are limitations on query complexity, or how results are ranked. The minimal description leaves too many questions unanswered for effective tool selection and invocation.
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 description coverage is 100%, so the schema already documents both parameters (query and kb) with descriptions and enum values for kb. The description doesn't add any additional meaning beyond what's in the schema - it doesn't explain what 'Brian's knowledge engine' means in relation to the parameters or provide usage examples.
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 the tool performs a search operation using a specific knowledge engine (Brian's knowledge engine), which provides a specific verb+resource combination. However, it doesn't distinguish this tool from its siblings (agent, ping) since they appear to be unrelated tools rather than alternative search methods.
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 provides no guidance on when to use this tool versus alternatives. There's no mention of when this search tool is appropriate versus other search methods, nor any context about when not to use it. The sibling tools appear unrelated, so no explicit comparison is made.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
- First observed
agent - First observed
ping - First observed
search
TDQS
Each tool has a clearly distinct purpose with no overlap: 'agent' is for chatting with an AI agent, 'ping' is for health checking the API server, and 'search' is for querying a knowledge engine. The descriptions make it easy to differentiate these functions, eliminating any risk of misselection.
The naming is mixed in style: 'agent' and 'ping' are simple verbs, while 'search' is a verb that could fit a pattern, but there is no consistent verb_noun structure or uniform convention. However, the names are readable and straightforward, avoiding chaotic or confusing formats.
With only 3 tools, the count feels thin for a server named 'BrianKnows MCP Server', which suggests a broader knowledge or interaction domain. While the tools cover basic operations (chat, health check, search), it may lack depth for more complex workflows, making it borderline in scope.
The tool surface has significant gaps for a knowledge-oriented server: there is no way to create, update, or manage knowledge entries, and the 'agent' tool lacks clarity on its capabilities beyond chatting. This incompleteness could lead to agent failures when trying to perform comprehensive tasks in the domain.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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