agent-toolbelt
The Agent Toolbelt server provides 27 tools for AI agents: 7 stock research tools powered by live financial data + Claude AI, and 20 utility tools for common agent workflows.
📈 Stock Research Tools
Stock Investment Thesis — Motley Fool-style thesis with bullish/neutral/bearish verdict, strengths, risks, and valuation read
Earnings Analysis — EPS beat/miss history, revenue trends, and earnings consistency
Insider Signal — Interpret Form 4 insider trading with signal strength rating
Valuation Snapshot — Assess P/E, P/S, EV/EBITDA, FCF yield, ROE, and margins with cheap/fair/expensive verdict
Bear vs Bull — Steelmanned 3-bull / 3-bear cases with net verdict
Moat Analysis — Buffett-style competitive moat assessment (brand, switching costs, network effects, IP, scale)
Compare Stocks — Head-to-head comparison of 2–3 tickers with a winner and per-ticker strengths/concerns
🛠️ Utility Tools
Text & Data: Extract emails/URLs/phones/dates/JSON from raw text, convert CSV to typed JSON, convert HTML ↔ Markdown
Document Analysis: Semantic document diff, contract clause extraction with risk flags, meeting action item extraction
AI/Prompt Utilities: Token counter with cost estimates (15+ models), context window packer (priority/greedy/balanced), prompt optimizer
Dev Tools: Schema generator (JSON Schema/TypeScript/Zod), regex builder, cron expression builder, API response mocker, dependency auditor (npm/PyPI via OSV)
Design: Brand kit generator (colors, typography, CSS/Tailwind tokens), color palette generator with WCAG scores
Web/Image: URL metadata fetcher (OG tags, favicon, author), US address normalizer (USPS format), image metadata stripper (EXIF/GPS/XMP)
Discovery: List all available tools with descriptions and pricing
Generates CSS variables, Tailwind design tokens, and WCAG-compliant color palettes from natural language descriptions.
Offers 16 ready-to-use DynamicStructuredTools for seamless integration into LangChain-based AI agent workflows.
Supports building complex AI agents in LangGraph by providing specialized tools for document analysis and data transformation.
Includes a markdown converter to transform HTML into clean Markdown, optimized for consumption by Large Language Models.
Enables the use of tools within OpenAI GPT Actions through a dedicated OpenAPI specification for custom GPTs.
Generates Python code snippets for regular expressions constructed from natural language descriptions.
Available via the RapidAPI marketplace, allowing developers to access and bill tool usage on a per-call basis.
Generates TypeScript interfaces and code snippets for regex patterns from plain English descriptions.
Normalizes and parses US addresses into the standard USPS format with associated confidence scores.
Automatically generates Zod validation schemas from natural language descriptions to facilitate data validation.
Agent Toolbelt
Stock research tools for AI agents. Live financial data + Claude-synthesized analysis, served as 7 focused tools — not raw OHLCV. Plus 20 utility tools for the rest of an agent's work.
Production API: https://www.agenttoolbelt.live
Quickstart
# Get a free API key (1,000 calls/month, no credit card)
curl -X POST 'https://www.agenttoolbelt.live/api/clients/register' \
-H "Content-Type: application/json" \
-d '{"email": "you@example.com"}'
# Generate a Motley Fool-style investment thesis for any ticker
curl -X POST https://www.agenttoolbelt.live/api/tools/stock-thesis \
-H "Authorization: Bearer atb_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"ticker": "NVDA", "timeHorizon": "3-5 years"}'Returns: bullish/neutral/bearish verdict, thesis paragraphs, key strengths, key risks, valuation read, insider read, analyst consensus read, and what to watch in the next earnings report.
Related MCP server: agent-utils-mcp
Stock research tools (7)
LLM-synthesized analysis on top of live financial data from Polygon.io, Finnhub, and Financial Modeling Prep.
Tool | What it does | Price |
| Full Motley Fool-style investment thesis: verdict + thesis paragraphs + strengths + risks + valuation read | $0.05/call |
| EPS beat/miss history, revenue trend, long-term earnings consistency read, upcoming earnings date | $0.05/call |
| Form 4 interpretation — distinguishes meaningful open-market purchases from routine sales/awards. Signal strength + confidence | $0.05/call |
| P/E, P/S, EV/EBITDA, FCF yield, ROE, margins → cheap/fair/expensive verdict + specific buy zone | $0.05/call |
| Steelmanned 3-bull / 3-bear case with specific data, net verdict, key debate question | $0.05/call |
| Head-to-head comparison of 2-3 tickers. Winner + per-ticker strengths/concerns + ifYouValue map (growth / value / quality) | $0.05/call |
| Buffett-style competitive moat assessment (brand / switching costs / network / scale / IP / cost). Wide/narrow/none + durability | $0.05/call |
Every stock tool returns a dataSources block with fetchedAt + per-source success flags so you know exactly what data backed the analysis.
Utility tools (20)
Common agent infrastructure. Rule-based tools billed at $0.0001–$0.001/call; LLM-powered tools at $0.005–$0.10/call.
Tool | What it does | Price |
| Extract emails, URLs, phones, dates, currencies, addresses, names from text | $0.0005/call |
| Count tokens across 15 LLM models with cost estimates | $0.0001/call |
| JSON Schema / TypeScript / Zod validator from plain English | $0.001/call |
| CSV to typed JSON with auto delimiter and type casting | $0.0005/call |
| HTML ↔ Markdown conversion | $0.0005/call |
| Title, OG tags, favicon, author from any URL | $0.001/call |
| Fetch + summarize a URL with key points | $0.02/call |
| Natural language → regex with JS/Python/TS snippets | $0.0005/call |
| Schedule description → cron expression with next-run preview | $0.0005/call |
| US address → USPS format with component parsing | $0.0005/call |
| Color palettes with WCAG scores and CSS vars | $0.0005/call |
| Full brand kit — colors, typography, CSS/Tailwind tokens | $0.001/call |
| Strip EXIF/GPS/IPTC/XMP metadata for privacy | $0.001/call |
| Action items, decisions, summary from meeting notes | $0.05/call |
| Score and rewrite LLM prompts | $0.05/call |
| Semantic diff between two document versions | $0.05/call |
| Key clauses + risk flags from contracts | $0.10/call |
| Realistic mock data from a JSON Schema | $0.0005/call |
| CVE scan for npm/PyPI packages via OSV database | $0.005/call |
| Pack content into a token budget for LLM context | $0.001/call |
Do you know what your agent is actually calling?
You wired up the tools. Once the agent is running, though, you have no visibility into which ones it calls or what it passes to them. I also built Cordon for this. It sits in front of any MCP server and logs every call. You can write rules to block the ones you don't want touching production.
getcordon.com — npx @getcordon/cli init
npm SDK + LangChain
npm install agent-toolbeltTyped client
import { AgentToolbelt } from "agent-toolbelt";
const client = new AgentToolbelt({ apiKey: process.env.AGENT_TOOLBELT_KEY! });
// Stock research
const thesis = await client.stockThesis({ ticker: "NVDA", timeHorizon: "3-5 years" });
const moat = await client.moatAnalysis({ ticker: "AAPL" });
const compare = await client.compareStocks({ tickers: ["NVDA", "AMD"] });
// Utility
const tokens = await client.tokenCounter({ text: myDocument });
const contacts = await client.textExtractor({
text: emailBody,
extractors: ["emails", "phone_numbers", "addresses"],
});LangChain integration
import { AgentToolbelt } from "agent-toolbelt";
import { createLangChainTools } from "agent-toolbelt/langchain";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { ChatOpenAI } from "@langchain/openai";
const client = new AgentToolbelt({ apiKey: process.env.AGENT_TOOLBELT_KEY! });
const tools = createLangChainTools(client); // 27 ready-to-use DynamicStructuredTools
const agent = createReactAgent({
llm: new ChatOpenAI({ model: "gpt-4o" }),
tools,
});Claude MCP
Use the stock research tools (and the rest of the toolbelt) directly inside Claude Desktop or Claude Code via the agent-toolbelt-mcp package.
Claude Desktop — add to claude_desktop_config.json:
{
"mcpServers": {
"agent-toolbelt": {
"command": "npx",
"args": ["-y", "agent-toolbelt-mcp"],
"env": {
"AGENT_TOOLBELT_KEY": "atb_your_key_here"
}
}
}
}Claude Code — one command:
claude mcp add agent-toolbelt -e AGENT_TOOLBELT_KEY=atb_your_key_here -- npx -y agent-toolbelt-mcpOnce installed, ask Claude things like "Give me a full analysis of NVDA — thesis, earnings quality, insider activity, and whether it's cheap right now" and it'll call the tools in parallel.
Discover tools programmatically
Agents can auto-discover all 27 tools at runtime:
curl https://www.agenttoolbelt.live/api/tools/catalogPricing
Tier | Price | Monthly calls | Rate limit |
Free | $0/mo | 1,000 | 10/min |
PAYG | prepaid credits | unlimited | 60/min |
Starter | $29/mo | 50,000 | 60/min |
Pro | $99/mo | 500,000 | 300/min |
Enterprise | Custom | 5,000,000 | 1,000/min |
Integrations
npm SDK —
npm install agent-toolbelt— typed client + LangChain toolsMCP —
npx -y agent-toolbelt-mcp— works with Claude Desktop and Claude CodeOpenAI GPT Actions — OpenAPI spec at
/openapi/openapi-gpt-actions.jsonRapidAPI — listed on the RapidAPI marketplace
Smithery, Glama, PulseMCP, MCP registry — discoverable in MCP directories
Going to production
When the agent moves out of dev, a new set of questions shows up. What did it call last night? What arguments did it pass? Who approved the destructive one?
Cordon is an MCP gateway that sits in front of servers like this one. Point your client at Cordon instead of directly at Agent Toolbelt; Cordon forwards every call through and adds:
A real-time audit log of every tool invocation (name, arguments, response, latency)
Per-API-key policy: which tools each caller can use, under what conditions
Slack-based human approvals for tool calls you've flagged as high-risk
From the agent's perspective nothing changes — same tools, same schemas. Free tier covers 1,000 events/month.
License
MIT
Available Tools
25 toolsaudit_dependenciesDependency AuditorA
Audit npm and PyPI packages for known CVEs using the OSV database (GitHub Dependabot's source). Pass packages directly or paste package.json / requirements.txt content.
Inputs: packages, manifest, manifestType, includeDevDependencies, +1 more.
bear_vs_bullBear vs BullA
Generate a structured bull vs. bear case for any stock. Steelmans both sides with specific data, then delivers a net verdict and the key question investors need to answer before buying.
Inputs: ticker (required).
build_cronCron Expression BuilderA
Convert natural language schedule descriptions into cron expressions. Examples: 'every weekday at 9am', 'first Monday of each month at noon', 'every 5 minutes'. Returns the expression, human-readable confirmation, and next 5 run times.
Inputs: description (required), timezone.
build_regexRegex Builder & TesterA
Build and test regular expressions from natural language descriptions. Supports emails, URLs, phones, dates, IPs, colors, UUIDs, and 15+ more patterns. Returns the pattern, code snippets in JS/Python/TS, and optional test results.
Inputs: description (required), testStrings, flags.
compare_documentsDocument ComparatorA
Compare two versions of a document and produce a semantic diff with additions, deletions, and modifications. Works with contracts, READMEs, policies, essays, or any text. Powered by Claude.
Inputs: original (required), revised (required), mode, context.
convert_markdownMarkdown ConverterA
Convert HTML to clean Markdown, or Markdown to HTML. Use HTML→Markdown when you've fetched a web page and need readable text for an LLM — strips tags, preserves headings, lists, code blocks, links, and tables. Use Markdown→HTML when rendering content in a web context.
Inputs: content (required), from (required), to (required).
count_tokensToken CounterA
Count tokens for any text across multiple LLM models and get per-model cost estimates. Use before sending text to an LLM to check context window usage or compare costs across models. Supports GPT-4o, GPT-4, GPT-3.5-turbo, Claude 3.5 Sonnet, Claude 3 Opus, and 10+ more.
Inputs: text (required), models.
csv_to_jsonCSV to JSONA
Convert CSV data to typed JSON. Auto-detects delimiters, uses the first row as headers, and casts values to proper types (numbers, booleans, nulls). Use when processing spreadsheet exports or any CSV-formatted data.
Inputs: csv (required), delimiter, hasHeader, typeCast, +1 more.
earnings_analysisEarnings AnalysisB
Analyze a stock's earnings track record — EPS beat/miss history, revenue trend, and what it means for long-term investors. Returns verdict, beat rate, revenue trajectory, last quarter summary, and what to watch next.
Inputs: ticker (required).
extract_contract_clausesContract Clause ExtractorB
Extract key clauses from a contract — parties, payment terms, termination, liability, IP ownership, confidentiality, and more. Optionally flags risky or one-sided clauses with severity ratings. Powered by Claude.
Inputs: contract (required), clauses, flagRisks.
extract_from_textText ExtractorA
Extract structured data from raw text: emails, URLs, phone numbers, dates, currencies, addresses, names, or JSON blocks. Useful for parsing documents, emails, web content, or any unstructured text into clean structured data.
Inputs: text (required), extractors (required), deduplicate.
extract_meeting_action_itemsMeeting Action ItemsA
Extract structured action items, decisions, and a summary from meeting notes or transcripts. Identifies task owners, deadlines, and priorities. Powered by Claude.
Inputs: notes (required), format, participants.
fetch_url_metadataURL MetadataA
Fetch a URL and extract its metadata: title, description, Open Graph tags (og:image, og:type), Twitter card tags, favicon, canonical URL, author, and publish date. Use to enrich links with context or understand what a page is about without reading the full content.
Inputs: url (required), timeout.
generate_brand_kitBrand Kit GeneratorA
Generate a complete brand kit from a company name, industry, and aesthetic keywords. Returns a color palette with WCAG accessibility scores, curated typography pairings, and design tokens in JSON, CSS, or Tailwind format.
Inputs: name (required), industry, vibe, format.
generate_color_paletteColor Palette GeneratorA
Generate a color palette from a description, mood, industry, or hex seed color. Accepts moods (calm, energetic, luxurious), industries (fintech, healthcare, fashion), nature themes (sunset, ocean, forest), or a specific hex color. Returns hex/RGB/HSL values, WCAG accessibility scores, and CSS custom properties.
Inputs: description (required), count, format, includeShades.
generate_schemaSchema GeneratorA
Generate a JSON Schema, TypeScript interface, or Zod validation schema from a natural language description of a data structure. Examples: 'a user profile with name, email, and signup date', 'a product listing with title, price, and inventory count'.
Inputs: description (required), format, strict.
insider_signalInsider SignalA
Interpret insider trading activity for any stock. Classifies open-market purchases vs. routine sales/awards, identifies cluster buying, and explains whether the activity is a meaningful signal. Returns signal strength (strong_buy → strong_sell) and a plain-English verdict.
Inputs: ticker (required).
list_toolsList Available ToolsA
List all tools available in the Agent Toolbelt API catalog, including descriptions and pricing.
Inputs: no named parameters.
mock_api_responseAPI Response MockerA
Generate realistic mock API responses from a JSON Schema. Supports nested objects, arrays, string formats (email, uuid, date-time, url), field-name heuristics, enums, and min/max constraints. Set seed for reproducible output. Returns 1–100 records.
Inputs: schema (required), count, seed.
normalize_addressAddress NormalizerA
Normalize a US mailing address to USPS standard format. Expands abbreviations (st→ST, ave→AVE), standardizes directionals, converts state names to codes. Returns parsed components and a confidence score (high/medium/low).
Inputs: address (required), includeComponents.
optimize_promptPrompt OptimizerA
Analyze and improve an LLM prompt. Scores clarity, specificity, structure, and completeness. Returns an optimized rewrite with a summary of what changed and why. Powered by Claude.
Inputs: prompt (required), model, task, mode.
pack_context_windowContext Window PackerA
Pack content chunks into a token budget for an LLM context window. Selects the best subset of chunks that fits within the token limit using priority, greedy, or balanced strategies. Use when you have more content than fits in the context window.
Inputs: chunks (required), tokenBudget (required), model, strategy, +3 more.
stock_thesisStock Investment ThesisA
Generate a long-term investment thesis for any stock. Pulls live financials, valuation metrics, insider trades, and analyst ratings, then synthesizes them into a Motley Fool-style research note. Returns a bullish/neutral/bearish verdict, thesis paragraphs, key strengths, risks, and valuation read. Use when you want fundamental analysis of a stock for long-term investing.
Inputs: ticker (required), timeHorizon.
strip_image_metadataImage Metadata StripperA
Strip EXIF, GPS, IPTC, XMP, and ICC metadata from an image for privacy. Use before uploading or sharing images to remove sensitive embedded data like GPS coordinates, camera model, timestamps, and editing history. Accepts base64-encoded JPEG, PNG, WebP, or TIFF. Returns cleaned base64 image with a removal report.
Inputs: image (required), format, quality.
valuation_snapshotValuation SnapshotA
Assess whether a stock is cheap, fair, or expensive. Pulls P/E, P/S, EV/EBITDA, FCF yield, ROE, and margins, then synthesizes them into a verdict with a specific buy zone price level.
Inputs: ticker (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 25 tools
Tools are mostly distinct despite the large set. The five financial analysis tools (bear_vs_bull, earnings_analysis, insider_signal, stock_thesis, valuation_snapshot) target different aspects of stock research, though an agent might briefly confuse stock_thesis with bear_vs_bull. Text extraction tools are well-differentiated by scope (general entities vs. contracts vs. meetings).
Approximately 80% of tools follow a consistent verb_noun pattern (e.g., extract_contract_clauses, generate_schema, normalize_address). However, the financial analysis cluster breaks convention with noun_noun naming (earnings_analysis, insider_signal, stock_thesis, valuation_snapshot) or noun_vs_noun (bear_vs_bull), creating a mixed convention that reduces predictability.
With 25 tools, this sits at the upper bound of 'borderline heavy' per the rubric. While each tool serves a distinct utility purpose (text extraction, stock analysis, data conversion), the breadth covers many unrelated domains (finance, cron syntax, image metadata, address normalization), making it a 'kitchen sink' collection that risks selection paralysis without being completely unmanageable.
For a general agent utility belt, the surface covers the targeted domains reasonably well. The financial cluster provides fundamental analysis capabilities (thesis, valuation, earnings, insider activity, bull/bear cases). Text extraction covers general entities, contracts, and meetings. Missing minor operations like json_to_csv or additional image processing don't create critical dead ends given the toolset's utility-focused nature.
Maintenance
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214 tools for AI agents over one MCP endpoint: image, video, audio, 3D, data, storage, mail, GPUs.
111 tools for AI assistants, so an answer comes from a rule instead of a guess. 84 deterministic utilities. IBAN and VAT validation, cron parsing, regex testing, hashing, JWT decoding, conversion between JSON, CSV, XML and YAML, units, number bases, timezones and date arithmetic. Eleven of them encode Dutch rules that a model tends to get wrong: AOW pension age, inheritance and gift tax, unemployment benefit duration, notice period, severance, the 30 percent ruling, holiday allowance, mileage allowance, and public and school holidays. Five more generate test data that is structurally valid and belongs to nobody: BSN, IBAN, BRP person records, UPA pension files and document numbers. 14 render tools that return a hosted file. HTML or a live URL to PDF, full page screenshots, QR codes, PDF merge and split, images to PDF, image resize, convert and compress, metadata stripping, PDF to DOCX and back. Plus read_page, which returns the readable content of a JavaScript rendered page as Markdown. 4 memory tools give an agent key-value state that survives between sessions, scoped to its own API key. 3 workflow tools list and run saved multi-step workflows on the caller's account. 6 AI tools for the jobs where a model is the right instrument rather than the wrong one: check a message for scam patterns, write a spreadsheet formula, turn a question into SQL, translate code between languages, improve a prompt, draft an email. Utilities and memory are free. Renders and AI tools have a free tier and credit packs. Workflows need an API key, because a workflow belongs to an account rather than to a key. Streamable HTTP at https://toolforte.com/api/mcp
The OpenRouter for tools. One MCP connection gives any AI agent 254 hosted tools, pay per call.
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