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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

stock-thesis

Full Motley Fool-style investment thesis: verdict + thesis paragraphs + strengths + risks + valuation read

$0.05/call

earnings-analysis

EPS beat/miss history, revenue trend, long-term earnings consistency read, upcoming earnings date

$0.05/call

insider-signal

Form 4 interpretation — distinguishes meaningful open-market purchases from routine sales/awards. Signal strength + confidence

$0.05/call

valuation-snapshot

P/E, P/S, EV/EBITDA, FCF yield, ROE, margins → cheap/fair/expensive verdict + specific buy zone

$0.05/call

bear-vs-bull

Steelmanned 3-bull / 3-bear case with specific data, net verdict, key debate question

$0.05/call

compare-stocks

Head-to-head comparison of 2-3 tickers. Winner + per-ticker strengths/concerns + ifYouValue map (growth / value / quality)

$0.05/call

moat-analysis

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

text-extractor

Extract emails, URLs, phones, dates, currencies, addresses, names from text

$0.0005/call

token-counter

Count tokens across 15 LLM models with cost estimates

$0.0001/call

schema-generator

JSON Schema / TypeScript / Zod validator from plain English

$0.001/call

csv-to-json

CSV to typed JSON with auto delimiter and type casting

$0.0005/call

markdown-converter

HTML ↔ Markdown conversion

$0.0005/call

url-metadata

Title, OG tags, favicon, author from any URL

$0.001/call

web-summarizer

Fetch + summarize a URL with key points

$0.02/call

regex-builder

Natural language → regex with JS/Python/TS snippets

$0.0005/call

cron-builder

Schedule description → cron expression with next-run preview

$0.0005/call

address-normalizer

US address → USPS format with component parsing

$0.0005/call

color-palette

Color palettes with WCAG scores and CSS vars

$0.0005/call

brand-kit

Full brand kit — colors, typography, CSS/Tailwind tokens

$0.001/call

image-metadata-stripper

Strip EXIF/GPS/IPTC/XMP metadata for privacy

$0.001/call

meeting-action-items

Action items, decisions, summary from meeting notes

$0.05/call

prompt-optimizer

Score and rewrite LLM prompts

$0.05/call

document-comparator

Semantic diff between two document versions

$0.05/call

contract-clause-extractor

Key clauses + risk flags from contracts

$0.10/call

api-response-mocker

Realistic mock data from a JSON Schema

$0.0005/call

dependency-auditor

CVE scan for npm/PyPI packages via OSV database

$0.005/call

context-window-packer

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-toolbelt

Typed 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-mcp

Once 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/catalog

Pricing

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 tools

  • MCP — npx -y agent-toolbelt-mcp — works with Claude Desktop and Claude Code

  • OpenAI GPT Actions — OpenAPI spec at /openapi/openapi-gpt-actions.json

  • RapidAPI — 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 tools
audit_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

    A3.6/5.0

    Scored across 25 tools

    Disambiguation4/5

    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).

    Naming Consistency3/5

    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.

    Tool Count3/5

    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.

    Completeness4/5

    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

    ActivityMaintained
    ResponsivenessLimited data

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