Skip to main content
Glama
OctagonAI

mcp-octagon

Official
by OctagonAI

Octagon: MCP for Public & Prediction Markets Intelligence

Favicon The Octagon MCP server provides specialized AI-powered financial research and analysis by integrating with the Octagon Market Intelligence API, enabling users to analyze and extract insights from public filings, earnings calls, financial metrics, stocks & crypto data, stock news, and prediction markets news & research within Claude Desktop and other popular MCP clients.

Demo

Tools

octagon-agent orchestrates broad market intelligence analysis

  • Public market insights: SEC filings (10-K, 10-Q, 20-F, 8-K, S-1, 13-F, DEF 14A), earnings call transcripts

  • Stocks & crypto market data: stock trading, crypto trading data, news, press release, ownerships and holdings

octagon-deep-research-agent for comprehensive deep research

  • Multi-source synthesis for investment research questions

  • Best for up-to-date, cross-source thematic analysis

✅ Prediction market research tooling

  • octagon-prediction-markets-agent for updated research reports on each prediction markets

  • prediction_markets_history for structured historical market data retrieval

✅ Live Octagon documentation access

  • octagon-docs-search, octagon-docs-read, octagon-docs-list, and octagon-docs-refresh

  • MCP resources for octagon-docs://catalog, octagon-docs://status, and octagon-docs://page/{target}

Related MCP server: FundzWatch MCP Server

Get Your Octagon API Key

To use Octagon MCP, you need to:

  1. Sign up for a free account at Octagon

  2. After logging in, from left menu, navigate to API Keys

  3. Generate a new API key

  4. Use this API key in your configuration as the OCTAGON_API_KEY value

Prerequisites

Before installing or running Octagon MCP, you need to have npx (which comes with Node.js and npm) installed on your system.

Mac (macOS)

  1. Install Homebrew (if you don't have it):

    /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
  2. Install Node.js (includes npm and npx):

    brew install node

    This will install the latest version of Node.js, npm, and npx.

  3. Verify installation:

    node -v
    npm -v
    npx -v

Windows

  1. Download the Node.js installer:

  2. Run the installer and follow the prompts. This will install Node.js, npm, and npx.

  3. Verify installation: Open Command Prompt and run:

    node -v
    npm -v
    npx -v

If you see version numbers for all three, you are ready to proceed with the installation steps below.

Installation

Running on Claude Desktop

To configure Octagon MCP for Claude Desktop:

  1. Open Claude Desktop

  2. Go to Settings > Developer > Edit Config

  3. Add the following to your claude_desktop_config.json (Replace your-octagon-api-key with your Octagon API key):

{
  "mcpServers": {
    "octagon-mcp-server": {
      "command": "npx",
      "args": ["-y", "octagon-mcp@latest"],
      "env": {
        "OCTAGON_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}
  1. Restart Claude for the changes to take effect

Running on Cursor

Configuring Cursor Desktop 🖥️ Note: Requires Cursor version 0.45.6+

To configure Octagon MCP in Cursor:

  1. Open Cursor Settings

  2. Go to Features > MCP Servers

  3. Click "+ Add New MCP Server"

  4. Enter the following:

    • Name: "octagon-mcp" (or your preferred name)

    • Type: "command"

    • Command: env OCTAGON_API_KEY=your-octagon-api-key npx -y octagon-mcp

If you are using Windows and are running into issues, try cmd /c "set OCTAGON_API_KEY=your-octagon-api-key && npx -y octagon-mcp"

Replace your-octagon-api-key with your Octagon API key.

After adding, refresh the MCP server list to see the new tools. The Composer Agent will automatically use Octagon MCP when appropriate, but you can explicitly request it by describing your investment research needs. Access the Composer via Command+L (Mac), select "Agent" next to the submit button, and enter your query.

Running with npx

env OCTAGON_API_KEY=your_octagon_api_key npx -y octagon-mcp

Manual Installation

npm install -g octagon-mcp

Documentation

For comprehensive documentation on using Octagon agents, please visit our official documentation at: https://octagonai.co/docs/

The documentation includes:

  • Detailed API references

  • Agent-specific query guidelines

  • Examples and use cases

  • Best practices for investment research

For the latest hosted MCP client setup guide, see:

This MCP server also exposes the live docs corpus directly to MCP clients. Documentation access is fetched from the public Octagon docs LLM entry point at https://octagonai.co/docs/llms.txt; it does not use or transmit OCTAGON_API_KEY.

Available Tools

The MCP server currently exposes the following tools:

Documentation Tools

These tools expose live Octagon docs inside the MCP session. They work even before OCTAGON_API_KEY is configured, which makes them useful for setup, troubleshooting, and discovering the right Octagon agent or API workflow.

octagon-docs-list

Lists live docs sections and pages from the Octagon docs corpus.

Parameters

  • section (string, optional): filter by docs section.

  • source (docs, site, or all, optional): defaults to the docs corpus.

  • limit (number, optional): maximum entries to return.

Searches the Octagon API, agent, MCP, and plugin docs with source URLs and optional snippets.

Parameters

  • query (string, required): search query.

  • section (string, optional): filter by docs section.

  • source (docs, site, or all, optional): defaults to the docs corpus.

  • limit (number, optional): maximum results to return.

  • includeSnippets (boolean, optional): include matched snippets in results.

Example:

Search Octagon docs for Claude plugin connector setup.

octagon-docs-read

Reads one docs page or section as Markdown by title, URL, path, or catalog id.

Parameters

  • target (string, required): docs title, URL, path, or catalog id.

  • source (docs, site, or all, optional): defaults to the docs corpus.

  • maxChars (number, optional): maximum Markdown characters to return.

  • preferCachedContent (boolean, optional): use the indexed docs corpus when available.

Example:

Read the Octagon MCP server guide.

octagon-docs-refresh

Refreshes the in-memory docs catalog from the live LLM-friendly docs endpoints.

Parameters

  • includeSite (boolean, optional): also refresh the broader https://octagonai.co/llms.txt site index.

Documentation Resources

Clients that support MCP resources can also browse:

  • octagon-docs://catalog: normalized live docs catalog.

  • octagon-docs://status: cache state, source endpoints, and refresh metadata.

  • octagon-docs://page/{target}: one docs page or section as Markdown.

octagon-agent

Orchestrates public and private market intelligence analysis.

Parameters

  • prompt (string, required): natural language research request.

  • conversation (string, optional): existing Octagon conversation ID to continue a prior octagon-agent thread. Omit this on the first turn.

  • newConversation (boolean, optional): if true, starts a fresh Octagon thread for the active session/thread anchor. Recommended for the first turn of a brand new visible chat in top-layer hosts such as Claude Desktop.

Threaded usage

octagon-agent is the only MCP tool that forwards Octagon conversation threading. It is a stateful tool and expects session continuity. The MCP resolves session/thread state in this order:

  1. stored conversation for MCP transport session identity, when the transport actually provides it

  2. stored conversation for the server-managed default stdio session

  3. explicit conversation can still override the active session conversation for that call

This package currently runs as a stdio MCP server. In stdio mode, the server automatically establishes a process-local session for continuity across calls. Most local hosts such as Claude Desktop or Cursor can therefore use octagon-agent without supplying any extra threading fields for basic follow-up behavior.

When a top-layer host knows a call is the first turn of a new visible chat, it should pass newConversation: true. That explicitly clears any stored Octagon thread for the active MCP session anchor before the call, which prevents stale continuity when a stdio host reuses the same long-lived MCP process across multiple visible chats.

This means you can use any of these patterns:

  1. First call: send only prompt

  2. Let the MCP host preserve transport session continuity or rely on the default stdio session

  3. Second call: either

    • send the new prompt in the same MCP session, or

    • keep using the same stdio MCP process, or

    • explicitly pass the previous conversation

Transport session identity is the canonical continuity primitive for standards-compliant stateful MCP transports. For local stdio usage, the server-managed process session provides default continuity.

Session identity and Octagon conversation identity are different concepts:

  • MCP session identity controls server-side continuity across tool calls

  • Octagon conversation controls the active Octagon thread inside that session

The MCP result keeps the answer in content, and also returns structured metadata for orchestrators in structuredContent:

{
  "model": "octagon-agent",
  "text": "Which stock would you like the latest price for?",
  "conversation": "conv_123",
  "responseId": "resp_123",
  "followUp": {
    "required": true,
    "inputTemplate": "<ticker or company name>",
    "instructions": "Reply with just the missing detail and reuse the conversation value from this response."
  }
}

Explicit carry-forward example:

{
  "prompt": "AAPL",
  "conversation": "conv_123"
}

New visible chat example:

{
  "prompt": "Analyze Apple",
  "newConversation": true
}

Explicit refresh example:

{
  "prompt": "Start a fresh Octagon thread for this chat",
  "newConversation": true
}

Stateful tool policy

  • octagon-agent: stateful, uses a usable continuity anchor. In stdio hosts, that defaults to the server-managed process session unless you provide explicit conversation

  • other MCP tools: stateless and may run without session continuity

Example:

Compare NVIDIA and AMD on latest quarterly revenue growth, margins, and management commentary.

More examples:

  • "What were Amazon's revenue and net income figures in Q4 2023?"

  • "Analyze Tesla's R&D spending trends over the last 3 years."

  • "What guidance did NVIDIA's CEO provide regarding AI chip demand in their latest earnings call?"

  • "Compare the price-to-earnings, price-to-sales, and EV/EBITDA ratios for the top 5 semiconductor companies."

  • "What was Anthropic's latest funding round size, valuation, and key investors?"

  • "How many investments did Andreessen Horowitz make in AI startups in the last 12 months?"

  • "What are the latest news articles for NVIDIA stock from the past week?"

octagon-deep-research-agent

Performs comprehensive multi-source deep research and synthesis.

Parameters

  • prompt (string, required): natural language research request.

Example:

Research the impact of lower interest rates on late-stage private software valuations over the next 12 months.

More examples:

  • "Extract all data fields from zillow.com/san-francisco-ca/"

  • "Research the financial impact of Apple's privacy changes on digital advertising companies' revenue and margins"

  • "Retrieve historical Bitcoin price data from 2023 and analyze the price volatility trends"

  • "Analyze the competitive dynamics in the EV charging infrastructure market"

octagon-prediction-markets-agent

Generates research reports for Kalshi prediction market events.

Parameters

  • prompt (string, required): natural language research request.

  • cache (boolean, optional): controls agent variant routing.

    • omitted: prediction-markets-agent

    • false: prediction-markets-agent:refresh

    • true: prediction-markets-agent:cache

Example:

Generate a report for the Kalshi market https://kalshi.com/markets/kxbtcy/btc-price-range-eoy/kxbtcy-27jan0100

prediction_markets_history

Fetches historical data for a prediction market event ticker with optional pagination and time filters.

Parameters

  • event_ticker (string, required)

  • limit (number, optional)

  • cursor (string, optional)

  • captured_from (string, optional)

  • captured_to (string, optional)

  • include_analysis (boolean, optional; when true, requests analysis columns)

Example:

Fetch historical data for the Kalshi event https://kalshi.com/markets/kxbtcy/btc-price-range-eoy/kxbtcy-27jan0100

Troubleshooting

  1. API Key Issues: Ensure your Octagon API key is correctly set in the environment or config file.

  2. Connection Issues: Make sure the connectivity to the Octagon API is working properly.

  3. Rate Limiting: If you encounter rate limiting errors, reduce the frequency of your requests.

License

MIT

Individual Specialized MCP Servers

While this server provides comprehensive market intelligence combining all our specialized agents, you can also use our individual MCP servers for specific use cases:

Research Tools


⭐ Star this repo if you find it helpful!

Available Tools

3 tools
octagon-agentB

[COMPREHENSIVE MARKET INTELLIGENCE] Orchestrates all agents for comprehensive market intelligence analysis. Capabilities: Combines insights from SEC filings, earnings calls, financial metrics, stock data, institutional holdings, private company research, funding analysis, M&A transactions, investor intelligence, and debt analysis to provide holistic market intelligence. Best for: Complex research requiring multiple data sources and comprehensive analysis across public and private markets. Example queries: 'Retrieve year-over-year growth in key income-statement items for AAPL, limited to 5 records and filtered by period FY', 'Analyze the latest 10-K filing for AAPL and extract key financial metrics and risk factors', 'Retrieve the daily closing prices for AAPL over the last 30 days', 'Analyze AAPL's latest earnings call transcript and extract key insights about future guidance', 'Provide a comprehensive overview of Stripe, including its business model and key metrics', 'Retrieve the funding history for Stripe, including all rounds and investors'.

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesYour natural language query or request for the agent

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. While it mentions the tool 'orchestrates all agents' and lists data sources, it doesn't disclose critical behavioral traits like whether this is a read-only operation, potential rate limits, authentication requirements, response format, or error handling. The description focuses on capabilities rather than operational behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose and capabilities, but becomes verbose with the lengthy list of data sources and multiple example queries. While all content is relevant, the example section could be more concise. The structure is logical but could be more efficiently organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given this is a complex orchestration tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns, how results are structured, error conditions, or operational constraints. The example queries help but don't compensate for the lack of behavioral and output documentation needed for effective agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with the single 'prompt' parameter well-documented as 'Your natural language query or request for the agent.' The description adds value through example queries that illustrate what constitutes a good prompt, but doesn't provide additional parameter-specific guidance beyond what the schema already states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'orchestrates all agents for comprehensive market intelligence analysis' and lists specific capabilities like SEC filings, earnings calls, financial metrics, etc. It distinguishes from siblings by emphasizing comprehensive multi-source analysis, though it doesn't explicitly name the sibling tools for comparison.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context with 'Best for: Complex research requiring multiple data sources and comprehensive analysis across public and private markets.' It includes example queries that illustrate appropriate use cases. However, it doesn't explicitly state when NOT to use this tool or directly compare it to the sibling tools by name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

octagon-deep-research-agentB

[PUBLIC & PRIVATE MARKET INTELLIGENCE] A comprehensive agent that can utilize multiple sources for deep research analysis. Capabilities: Aggregate research across multiple data sources, synthesize information, and provide comprehensive investment research. Best for: Investment research questions requiring up-to-date aggregated information from the web. Example queries: 'Research the financial impact of Apple's privacy changes on digital advertising companies' revenue and margins', 'Analyze the competitive landscape in the cloud computing sector, focusing on AWS, Azure, and Google Cloud margin and growth trends', 'Investigate the factors driving electric vehicle adoption and their impact on battery supplier financials'.

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesYour natural language query or request for the agent

TDQS

B3.2/5.0
Behavior2/5

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 mentions capabilities like aggregation and synthesis, but lacks critical behavioral details such as rate limits, authentication requirements, data freshness guarantees, or potential costs. The description doesn't contradict annotations (since none exist), but provides insufficient operational context for a tool performing complex research.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately structured with capability lists, usage guidance, and examples, but could be more front-loaded. Some sentences like 'Capabilities: Aggregate research across multiple data sources, synthesize information, and provide comprehensive investment research' could be more efficiently integrated. The bracketed '[PUBLIC & PRIVATE MARKET INTELLIGENCE]' adds little value and disrupts flow.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex research tool with no annotations and no output schema, the description is incomplete. It doesn't explain what format the research results will take, whether they include citations or sources, how comprehensive the aggregation is, or any limitations on research scope. The examples help but don't compensate for missing behavioral and output context that an agent would need to use this tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for its single parameter ('prompt'), which is well-documented as 'Your natural language query or request for the agent'. The description adds value by providing example queries that illustrate appropriate prompt content, but doesn't add significant semantic information beyond what the schema already provides. With high schema coverage, the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as a comprehensive research agent that aggregates, synthesizes, and provides investment research using multiple data sources. It specifies the verb ('utilize multiple sources for deep research analysis') and resource ('investment research questions'), but doesn't explicitly differentiate from sibling tools like 'octagon-agent' or 'octagon-scraper-agent' beyond mentioning its comprehensive nature.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: 'Best for: Investment research questions requiring up-to-date aggregated information from the web' and includes example queries. However, it doesn't explicitly state when NOT to use it or mention alternatives like the sibling tools, leaving some ambiguity about tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

octagon-scraper-agentB

[PUBLIC & PRIVATE MARKET INTELLIGENCE] Specialized agent for financial data extraction from investor websites. Capabilities: Extract structured financial data from investor relations websites, tables, and online financial sources. Best for: Gathering financial data from websites that don't have accessible APIs. Example queries: 'Extract all data fields from zillow.com/san-francisco-ca/', 'Extract all data fields from www.carvana.com/cars/'.

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesYour natural language query or request for the agent

TDQS

B3.3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but provides minimal behavioral disclosure. It mentions 'financial data extraction' but doesn't describe rate limits, authentication needs, error handling, or what happens when extraction fails. The description doesn't contradict annotations since none exist, but it's insufficient for a tool performing web scraping operations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise with three focused sentences: purpose statement, capabilities, usage guidance, and examples. The bracketed '[PUBLIC & PRIVATE MARKET INTELLIGENCE]' adds some noise, but overall the structure is clear and front-loaded with the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a web scraping tool with no annotations and no output schema, the description is incomplete. It doesn't explain what structured data format to expect, error conditions, rate limits, or authentication requirements. The examples help but don't compensate for missing behavioral and output information critical for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description doesn't mention the 'prompt' parameter at all, though schema description coverage is 100% with the parameter well-documented as 'Your natural language query or request for the agent'. The description's example queries imply what the prompt should contain, but adds minimal value beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'financial data extraction from investor websites' with specific capabilities like extracting structured data from tables and online financial sources. It distinguishes from siblings by specifying 'financial data' focus, though not explicitly contrasting with 'octagon-agent' or 'octagon-deep-research-agent'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: 'Best for: Gathering financial data from websites that don't have accessible APIs.' It gives two example queries showing practical applications. However, it doesn't explicitly state when NOT to use it or mention alternatives among sibling tools.

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.

  1. 10 tool updatesv1.0.0
    • Addedoctagon-agent
    • Removedoctagon-companies-agent
    • Removedoctagon-deals-agent
    • Removedoctagon-debts-agent
    • Removedoctagon-financials-agent
    • Removedoctagon-funding-agent
    • Removedoctagon-investors-agent
    • Removedoctagon-sec-agent
    • Removedoctagon-stock-data-agent
    • Removedoctagon-transcripts-agent
  2. 11 tool updates
    • First observedoctagon-companies-agent
    • First observedoctagon-deals-agent
    • First observedoctagon-debts-agent
    • First observedoctagon-deep-research-agent
    • First observedoctagon-financials-agent
    • First observedoctagon-funding-agent
    • First observedoctagon-investors-agent
    • First observedoctagon-scraper-agent
    • First observedoctagon-sec-agent
    • First observedoctagon-stock-data-agent
    • First observedoctagon-transcripts-agent

TDQS

B3.1/5.0

Scored across 3 tools

Disambiguation2/5

The tools have overlapping purposes, causing significant ambiguity. All three tools are described as providing comprehensive market intelligence, with octagon-agent and octagon-deep-research-agent both focusing on aggregated research across multiple sources, making it unclear when to choose one over the other. The descriptions do not clearly delineate distinct boundaries, leading to potential misselection.

Naming Consistency5/5

The tool names follow a highly consistent pattern with the prefix 'octagon-' followed by a descriptive suffix ('agent', 'deep-research-agent', 'scraper-agent'). This uniform naming convention makes the tools easily identifiable and predictable, with no deviations in style or structure.

Tool Count3/5

With only 3 tools, the count feels thin for the broad scope of 'comprehensive market intelligence' covering public and private markets. While the tools aim to cover multiple data sources and analyses, the limited number may not adequately support the complex workflows implied by the descriptions, bordering on under-scoped for the domain.

Completeness2/5

There are significant gaps in the tool surface for market intelligence. The tools focus on aggregation and scraping but lack dedicated operations for specific actions like updating data, deleting records, or managing user queries, which are essential for a complete CRUD lifecycle. This incompleteness could lead to agent failures in handling varied tasks.

Maintenance

ActivityStale
ResponsivenessNo issues

Related MCP Connectors

Related MCP Servers

  • F
    license
    Not graded
    quality
    Not graded
    maintenance
    Provides access to a comprehensive financial intelligence platform featuring real-time market data, quantitative models, and alternative data sources. It enables users to perform advanced financial analysis including options analytics, portfolio modeling, and SEC filing research.
    -
  • A
    license
    A
    quality
    C
    maintenance
    Provides real-time business event intelligence and AI-scored sales leads to help users track funding rounds, acquisitions, and executive hires. It enables AI agents to generate strategic market briefs and manage company watchlists for predictive business insights.
    7
    375 npm
    3
    MIT
  • A
    license
    Not graded
    quality
    C
    maintenance
    Financial data and research MCP for AI agents: filings with full-text and fact search, statements as reported, earnings, insider and institutional ownership, corporate events, executives, analyst data, company discovery and research signals for US, China and Japan equities. Every figure traced to its filing. Browser sign-in.
    6
    MIT