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., "@Axon Research MCP Server research the latest AI agent frameworks for DeFi trading"
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.
Research capabilities exposed as MCP tools for AI agents.
git clone https://github.com/your-username/axon-research-mcp.git
cd axon-research-mcp
npm installnpm startAdd to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"axon-research": {
"command": "node",
"args": ["/path/to/axon-research-mcp/server.js"]
}
}
}Related MCP server: hyre-mcp
ai_agent_researchResearch the AI agent ecosystem - frameworks, tools, monetization, best practices.
{
"topic": "agent monetization",
"depth": "detailed"
}Returns:
Structured research findings
Sources and citations
Actionable insights
defi_analysisAnalyze DeFi protocols on Base, Ethereum, and Solana.
{
"protocol": "aerodrome",
"chain": "base",
"analysis_type": "full"
}Returns:
TVL analysis
Yield opportunities
Risk assessment
Tokenomics review
market_researchGeneral market research - crypto trends, AI developments.
{
"query": "AI agent frameworks comparison",
"scope": "ai"
}Returns:
Market analysis
Competitive landscape
Key findings
Tier | Price | Features |
Free | $0 | Static knowledge base |
Premium | $10/month | Live web search |
Enterprise | $100/month | SLA, custom topics |
Add mcpName to package.json:
{
"mcpName": "io.github.your-username/axon-research"
}Publish to npm:
npm publish --access publicInstall mcp-publisher:
curl -L "https://github.com/modelcontextprotocol/registry/releases/latest/download/mcp-publisher_$(uname -s | tr '[:upper:]' '[:lower:]')_$(uname -m | sed 's/x86_64/amd64/;s/aarch64/arm64/').tar.gz" | tar xz mcp-publisher && sudo mv mcp-publisher /usr/local/bin/Publish to MCP Registry:
mcp-publisher publishnpm run buildnode server.jsMIT
Axon - Autonomous AI Orchestrator
Issues and PRs welcome!
Status: Ready for publishing (needs npm account)
Research the AI agent ecosystem - frameworks, tools, monetization, best practices. Covers LangGraph, CrewAI, AutoGen, MCP, ACP, and more. Returns synthesized research with citations.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | How deep to research (default: detailed) | |
| topic | Yes | Research topic (e.g., 'agent monetization', 'MCP servers', 'LangGraph patterns') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It states the tool returns synthesized research with citations, indicating a read-like behavior, and the depth parameter implies varying effort. However, it does not mention authentication needs, rate limits, statefulness, or any potential side effects beyond the implied non-destructive nature of research.
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 highly concise: two well-crafted sentences. The first sentence front-loads the core purpose and scope, listing specific covered areas. The second sentence describes the output format. There is no redundant or gratuitous information; every word earns its place.
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 low complexity (2 simple parameters, full schema coverage, no output schema), the description covers essentials: purpose, scope, output format, and example topics. It does not detail the exact structure of the synthesized research beyond 'with citations', but this is sufficient for an AI agent to infer reasonable usage. The lack of annotations is compensated by the clear research nature. Some minor missing details (e.g., maximum results, time constraints) prevent a 5.
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% with both parameters described. The description adds value by giving concrete examples of valid topics (e.g., 'agent monetization', 'MCP servers'), which enhances the schema's generic 'Research topic' description. However, the depth parameter gains no new meaning beyond what the enum values already provide. Overall, the description modestly supplements the schema but does not significantly elevate understanding.
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?
Description clearly states the tool researches the AI agent ecosystem, listing specific frameworks like LangGraph, CrewAI, AutoGen, MCP, and ACP. It uses a specific verb-resource combination ('Research the AI agent ecosystem') and naturally distinguishes itself from sibling tools (defi_analysis and market_research) by focusing on AI agents, leaving no ambiguity about its domain.
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 when to use this tool through its explicit focus on AI agent topics and examples, but does not provide explicit when-not-to-use guidance or mention alternatives. It clearly sets context for research on frameworks, tools, monetization, and best practices, which differentiates it from general market research or DeFi analysis siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze DeFi protocols on Base, Ethereum, and Solana. TVL, yields, risks, tokenomics. Returns structured analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | Blockchain network (default: base) | |
| protocol | Yes | Protocol name or contract address | |
| analysis_type | No | Type of analysis (default: full) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It only says 'Returns structured analysis' without specifying output format, data freshness, API dependencies, rate limits, or side effects. For an analysis tool, behavioral traits like real-time vs. historical data or authentication needs are undisclosed.
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 (two sentences, ~20 words) with no fluff. Every word contributes to stating the domain and scope. Front-loaded with the main action.
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?
The tool has 3 parameters, no output schema, and no annotations. The description fails to explain the return value structure, valid protocol input formats, or any limitations. It is insufficient for an agent to fully understand the tool's capabilities and output format.
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% (all parameters have descriptions). The description adds 'TVL, yields, risks, tokenomics' which maps partially to the analysis_type enum, but 'tokenomics' is not an enum value. The description does not clarify how to use protocol as name vs. address or the default chain behavior. Baseline 3 is appropriate given high schema coverage but limited additional value.
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 analyzes DeFi protocols on specific chains (Base, Ethereum, Solana) and mentions analysis dimensions (TVL, yields, risks, tokenomics). It distinguishes from sibling tools (ai_agent_research, market_research) which are broader. However, the description omits Arbitrum, which appears in the schema, causing minor inconsistency.
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 vs. alternatives (sibling tools). No mention of prerequisites, when not to use it, or comparison with similar research tools. The agent is left to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
General market research - crypto trends, AI developments, competitive analysis. Returns findings with sources.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Research question or topic | |
| scope | No | Market scope (default: general) |
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. It only states that the tool returns findings with sources, which is minimal. It does not mention any side effects, safety traits, authentication needs, or limitations (e.g., read-only nature, rate limits, or data freshness). The description is too sparse to adequately inform the agent about the tool's behavior.
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, concise sentence of 14 words that is front-loaded with the core purpose. Every word is necessary and there is no redundancy or wasted text. It is an excellent example of conciseness.
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 simplicity (2 parameters, no output schema, no annotations), the description is minimally adequate. It tells the agent what the tool does and what it returns, but it lacks context on default scope behavior, how to formulate effective queries, or any limitations. For a straightforward research tool, the description is functional but leaves gaps.
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 input schema already provides 100% coverage on both parameters with descriptions. The description adds general context about the research domains (crypto, AI, competitive analysis) but does not add meaningful parameter-specific details beyond what the schema offers. Thus, the description adds only marginal value, meeting the baseline of 3 for high schema coverage.
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 general market research specifically on crypto trends, AI developments, and competitive analysis, and returns findings with sources. This is a specific verb+resource combination. However, it does not explicitly differentiate from sibling tools like ai_agent_research or defi_analysis, so it does not fully achieve a 5.
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 nor any conditions for use. It simply states what the tool does without any explicit when-to-use or when-not-to-use instructions, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.1.0ai_agent_researchdefi_analysismarket_researchThe tools cover different domains: AI agent ecosystem, DeFi analysis, and general market research. While ai_agent_research and market_research could both touch on AI trends, their descriptions clearly differentiate the specialized focus on agent tooling vs broader market analysis.
All tools use a consistent pattern: a two-word snake_case name with a specific domain prefix (ai_agent_, defi_, market_) followed by a research/analysis verb. This is clear and predictable, though 'research' is used as both a noun in the server name and a verb for two tools, which could cause minor confusion.
Three tools is on the lower end, but the server's stated purpose ('Axon Research') is fairly broad, covering multiple domains (AI agents, DeFi, general market). The count feels slightly thin for such broad coverage, as each domain could benefit from additional specialized tools.
Each tool is essentially a 'do general research' function for its domain, with no breakdown into more specific operations (e.g., no separate tools for getting recent news, analyzing specific tokens, searching by criteria, or exporting results). This makes it hard for agents to perform granular tasks without overloading the single tool per domain.
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If you are the server author, to access and configure the admin panel.
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