MCP data layer for AI-assisted market research. 13 live finance tools that drop into Claude Code, Cursor, Cline, Windsurf, or any MCP-compatible client. Free tier requires no signup.
Not a signal service. Not investment advice. Public market data with light enrichment for AI agents. Backtest before acting.
Option 1: hosted MCP (zero install). Point your agent at the hosted endpoint, no Python needed.
{
"mcpServers": {
"falsifylab-alpha": {
"url": "https://mcp.falsifylab.com/"
}
}
}Free tier auto-applies. OAuth 2.1 + PKCE flow for paid plan via Whop. Streamable HTTP transport. Full OpenAPI 3.1 spec at https://docs.falsifylab.com/openapi.html.
Option 2: stdio (this package).
pip install falsifylab-alpha-mcpZero runtime dependencies (Python stdlib only). Then wire to your agent. see client setup.
Related MCP server: crypto-quant-signal-mcp
When the user asks your AI agent things like:
"What insider clusters are forming today?"
"Show me Hyperliquid vaults with sharp Sharpe and <10% drawdown"
"Are BTC ETF flows confirming or diverging from price action?"
"What's in the macro tape right now?"
"Find Polymarket whales with 60%+ win rate"
...the agent calls one of 13 tools and grounds its answer in real-time numbers from public-market data — instead of speculating from training-data knowledge.
Tool | Returns | Use case |
| DeFi yields with emissions stripped, IL risk priced | Find real yield, not headline yield |
| Hyperliquid vaults by NAV / 30d / drawdown / score | Copy-trade reference |
| SEC Form 4 cluster buys (3+ insiders, 24h window) | Equity conviction signal |
| 8-K filings filtered by material item codes | Earnings, M&A, officer changes |
| SPX/NDX/VIX/UST/DXY/GOLD/WTI/BTC/ETH snapshot | Regime gate for trade ideas |
| US spot BTC + ETH ETF aggregates | Institutional positioning |
| DefiLlama yield-gap airdrops | Farms where you're paid to wait |
| Top wallets + active positions | Prediction-market copy-trade |
| Post-earnings drift + IV-crush candidates | Equity event-volatility scanner |
| Forward token unlock schedule + size | Supply-overhang risk radar |
| Fed speaker/event volatility windows | Macro event-risk scanner |
| Cross-source signal alignment | Stacks 2+ signals on same asset |
| Live Solana copy-trade bot scores | Production wallet rankings |
All tools have full JSON schemas exposed at /.well-known/mcp/server-card.json.
claude mcp add falsifylab-alpha -- python -m falsifylab_alpha_mcpFor Pro tier:
claude mcp add falsifylab-alpha \
--env FL_API_KEY=fl_your_key \
-- python -m falsifylab_alpha_mcpDrop into ~/.cursor/mcp.json or workspace root .mcp.json:
{
"mcpServers": {
"falsifylab-alpha": {
"command": "python",
"args": ["-m", "falsifylab_alpha_mcp"],
"env": {
"FL_API_KEY": "fl_your_key_or_omit_for_free_tier"
}
}
}
}Restart Cursor. Tools auto-discovered.
Settings → MCP Servers → Add Server. Paste the same JSON as Cursor above.
Settings → MCP. Same JSON config. Auto-indexed from Smithery within 48h.
Connect via Smithery's hosted gateway — no local Python install needed. See smithery.ai/servers/falsifylab/falsifylab-alpha-mcp.
In your agent, ask:
Use macro_tape. Show me SPX, VIX, BTC, ETH with 1d and 5d returns.
If the agent returns numbers, you're live.
Copy-paste these. They work on first run.
Daily macro brief:
Use macro_tape. Tell me the macro regime in 3 sentences. Cite specific
numbers — last price, 1d return, 5d return. Then list 2 tickers from
sec8k_material_today that might benefit from this regime.Insider + 8-K stacker:
Run insider_buy_clusters (min_insiders=3) and sec8k_material_today
(items=["2.02","8.01","5.02"]). Find tickers in both within 24h. For
each match, summarize what insiders bought and what the 8-K said. End
with the bear case in one sentence.Confluence scan with bear case:
Use confluence_today with min_signals=2. For each asset/ticker, explain
why these signals stacking is meaningful in 2 sentences. Skip noise
(DeFi LP pools with stablecoins). Highlight the most contrarian stack.8 more recipes at falsifylab.com/cookbook.
Tier | Price | What |
Free | $0 | 24h cached, 10 results/query, no signup, 60 req/hr |
Pro | $19/mo | Real-time (5min), 100 results/query, 90d history, no rate limit |
Pro Plus | $49/mo | 1-min refresh, email + Slack alerts, 365d on equity feeds, webhooks |
Teams | $199/mo | 5 seats, 50k req/day, priority support |
Launch promo EARLY50: 50% off first 3 months, capped 25 redemptions. Get a key at falsifylab.com/pro.
Public agent calling these tools every 15 minutes: falsifylab.com/demo. Same MCP server you'd install. Real numbers, no curation.
Built it because the author hand-scraped SEC EDGAR and DefiLlama at 6am every morning to feed a fleet of trading bots. The bots that survived ate from this exact pipeline. Wrapped it as an MCP server so any agent can pull the same data.
The bot fleet receipts (alive + dead, P&L, drawdown) are public at falsifylab.com/vaults.
What we do: aggregate + lightly enrich public market data, expose via MCP
What we don't: trade execution, brokerage, advisory, signal selling
Data sources: SEC EDGAR (Form 4, 8-K), SoSoValue (ETF flows), DefiLlama (yield), Hyperliquid public API, Polymarket public API, Yahoo Finance (macro)
Privacy: free-tier key issued by hash(IP+UA), not stored long-term. Request bodies never logged.
The local stdio server writes minimal per-tool telemetry JSONL rows to:
/var/log/falsifylab/mcp_tool_calls.jsonl (override with FL_MCP_TOOL_CALL_LOG)
Each row includes only:
ts, tool, args_keys (argument names only), has_api_key, key_hash (sha256 prefix), version
Never logged by this telemetry path:
raw API keys
argument values / request bodies
IP address
email / name / other user identifiers
To disable telemetry completely:
export FL_DISABLE_TELEMETRY=1Honest list. Updated every release.
All 13 tools return live data inside agent calls under 1s p95.
Free tier has zero signup friction. No API key needed for the cached path.
Form 4 insider clusters get refreshed every 15 min on weekdays from SEC EDGAR direct, no 3rd-party reseller in the pipeline.
DefiLlama yields are emissions-stripped (headline APY minus reward tokens), so a 200% farm and a 200% farm are not the same number any more.
Hyperliquid vault NAV updates hit our R2 mirror within 60s of the source ticker.
Telegram + Slack webhook delivery on Pro Plus is on-time within the cron window. No queue backups in the launch month.
8-K material filings list is empty over weekends because SEC does not file. Returns count: 0 Sat/Sun. Expected.
Macro tape uses Yahoo Finance free endpoints. Intraday gap of 15-20 min during US market hours. We plan to swap for a paid feed at Teams tier, not before.
onchain_smart_wallets only covers Solana right now. Base + Ethereum copy-trade scores are queued for v0.3.x.
Free tier cache is 24h. So confluence_today on a free key shows yesterday's confluence, not today's. Pro $19/mo plan hits live.
Historical replay across multiple quarters is not in v0.2.x. Tracking as roadmap item.
No SDK for TypeScript or Go yet. Python MCP server only. Use stdio transport from any MCP-compatible client meanwhile.
Stripe Connect direct subscriptions (operator KYC blocker). Whop is the path.
Substack paid plan (Stripe dependency).
A previous package version 0.2.0 shipped a broken api.falsifylab.com host. Fixed in 0.2.1 within 4 days. Apologies if you grabbed 0.2.0 first.
If you find something broken, dm the FL X account or open an issue. We ship fixes inside the same day for prod-blocking bugs.
your AI agent
↓ (MCP stdio)
falsifylab_alpha_mcp (Python stdlib, no deps)
↓ (HTTPS)
falsifylab.com/api/* (Cloudflare Workers)
↓ (cached 60s-1hr)
R2 (data feeds refreshed every 15min via cron)
↓ (every 15min)
Backend cron scripts (Form 4, 8-K, ETF, yield, polymarket, onchain, etc.)Free tier hits cached data with 24h age. Pro tier hits the same endpoints with auth header, bypasses cache to fresh upstream.
Live demo: falsifylab.com/demo
Install guide: falsifylab.com/install
Cookbook: falsifylab.com/cookbook
Pricing: falsifylab.com/pro
Comparison (vs Token Terminal / Nansen / Dune): falsifylab.com/vs
MCP server card: /.well-known/mcp/server-card.json
Substack: falsifylab.substack.com
Smithery: smithery.ai/servers/falsifylab/falsifylab-alpha-mcp
Agent starter repo: github.com/opsfalsifylab/falsifylab-agent-starter
Issues: GitHub issues
Email: ops@falsifylab.com
Security: report to ops@falsifylab.com with subject [security]. 48h response. Responsible disclosure credited in CHANGELOG.
PRs welcome for:
New tool ideas (open issue first to discuss scope)
Documentation improvements
Bug fixes
Do not submit PRs for trading-signal generators or copy-trading helpers — out of scope for this server (we are research data infrastructure, not advisory).
MIT. See LICENSE.
FalsifyLab Alpha v0.3.3 · Built for AI coding agents · Free tier no signup · falsifylab.com
Active airdrop / points-farming opportunities. Detected from DefiLlama yield gaps (where reported APY exceeds base+rewards = likely points program). Includes realistic APY, TVL, capital required, confidence score. Sourced from Suki defi_scanner.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| min_apy | No | filter floor pct (realistic APY) | |
| min_tvl_usd | No | filter pools <X TVL | |
| chain | No | filter (ethereum, base, arbitrum, etc.) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It mentions detection from DefiLlama and included fields (APY, TVL, etc.), implying a read-only list operation. However, it does not specify update frequency, side effects, or rate limits, leaving some gaps.
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 three sentences with no wasted words. Each sentence adds value: purpose, detection method, and data source. It is front-loaded and appropriately sized for the tool's simplicity.
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 no output schema and 4 parameters, the description sufficiently explains the tool's output fields (APY, TVL, capital, confidence) and source. It lacks details on ordering, pagination, or error handling, but for a list tool this is acceptable. A more complete description would include output structure.
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 75% (3 of 4 params described). The description adds marginal value by mentioning derived fields like 'confidence score' but does not detail the 'limit' parameter or clarify parameter usage beyond what the schema already provides. Baseline 3 is appropriate.
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 provides 'Active airdrop / points-farming opportunities' and distinguishes from siblings like 'top_yield_farms' by focusing on airdrops. It specifies the detection method and data source, making the purpose unambiguous.
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 (for airdrop/points opportunities) but does not explicitly exclude alternatives or mention when not to use. It provides context for usage but lacks formal guidance compared to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
US-listed spot crypto ETF aggregate flows today. BTC + ETH net flow, 5d streak, cumulative AUM. Source: SoSoValue.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It mentions the data source and scope but does not state read-only nature, data freshness, or any limitations. It is adequate but not explicit.
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, one sentence with key details, front-loaded with the main purpose. Every part is essential with no redundancy.
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 no parameters, no output schema, and no annotations, the description fully covers what the tool does. It provides sufficient context for an agent to understand the data returned.
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 has zero parameters with 100% coverage. The description adds meaning by detailing the output fields (BTC+ETH net flow, 5d streak, cumulative AUM), providing value beyond the schema.
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 it provides US-listed spot crypto ETF aggregate flows today, including BTC+ETH net flow, 5-day streak, and cumulative AUM. It specifies the source (SoSoValue), making it distinct from sibling tools like macro_tape or top_yield_farms.
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 for obtaining today's ETF flow data but lacks explicit guidance on when to use versus alternatives, or any context on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Hyperliquid vault leaderboard with NAV, 30d return, max drawdown, follower count, composite score. Real-time scrape of HL info API.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| sort_by | No | score |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions 'real-time scrape of HL info API', implying a live data fetch, but doesn't disclose potential rate limits, caching, or error handling. Some transparency, but not comprehensive.
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 very concise: two sentences that front-load the key content and data source. No unnecessary words.
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 simple leaderboard tool with two optional parameters and no output schema, the description covers what data it returns but lacks detail on parameter behavior and error scenarios. Adequate but with 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?
Schema coverage is 0% (no descriptions for parameters). The description does not explain the purpose or effect of 'limit' or 'sort_by' beyond what the schema's enum and default values implicitly show.
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 retrieves a Hyperliquid vault leaderboard, listing specific metrics (NAV, 30d return, etc.). This distinguishes it from sibling tools which cover different financial topics like airdrop farms or ETF flow.
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?
No guidance on when to use this tool versus alternatives. The description only states what it does, not the context or any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Form 4 insider buy clusters (3+ insiders bought same ticker in 24-48h). Bullish signal. Filtered to open-market purchases (P code), excluding awards/gifts.
| Name | Required | Description | Default |
|---|---|---|---|
| window_hours | No | ||
| min_insiders | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden. It discloses key filters (P code, excluding awards/gifts) and time window, but does not explain if it returns historical data, requires authentication, or has rate limits. Adequate but not comprehensive.
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 concise at two sentences, front-loading the core purpose and key filter. It avoids waste but could structure the information more clearly.
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 lack of annotations, output schema, and simple parameters, the description provides the essential idea and filter. However, it omits output format and any request/response details, leaving some gaps for agent usage.
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 0%, so the description must add meaning. It hints at parameters with '24-48h' and '3+ insiders', matching defaults, but does not explicitly describe the parameters' purpose, ranges, or constraints, leaving the schema largely unexplained.
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 identifies insider buy clusters (3+ insiders in 24-48h) from Form 4 filings, with filtering to open-market purchases. This verb-resource pair distinguishes it from sibling tools covering airdrops, ETFs, vaults, macro, etc.
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 explicit guidance on when to use this tool versus its siblings. While 'Bullish signal' implies a use case, there is no mention of alternatives, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Live US macro snapshot: SPX, NDX, RUT, VIX, UST 2y/10y, DXY, GOLD, WTI, BTC, ETH. Last price + 1d/5d % change.
| Name | Required | Description | Default |
|---|---|---|---|
| symbols | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should fully disclose behavioral traits. It does not explicitly state that the tool is read-only or indicate any side effects, though the context implies a read operation.
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 two concise sentences that efficiently convey the core function, though it could benefit from slightly more structure or elaboration on parameters.
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 simple snapshot tool with one optional parameter and no output schema, the description covers the main function but lacks detail on parameter behavior and return format, leaving moderate 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 schema has 0% description coverage and the description does not mention the 'symbols' parameter, leaving its purpose and usage unclear. The description lists specific symbols, but does not clarify if the parameter filters or overrides this.
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 it provides a live US macro snapshot with specific assets (SPX, NDX, etc.) and data points (last price + 1d/5d % change). It is distinct from sibling tools which cover different domains.
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 for macro data but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Top Polymarket whale wallets and their current active positions sized >$10k. Copy-trade reference.
| Name | Required | Description | Default |
|---|---|---|---|
| min_position_usd | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only states that the tool returns whale positions, but does not mention rate limits, data freshness, authentication needs, or whether it is read-only. Essential behavioral context is missing.
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 very concise, consisting of two short sentences. It is front-loaded and every phrase adds value. The 'Copy-trade reference' might be integrated, but overall it is efficient.
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?
Lacks important information such as output structure (fields returned), number of results, pagination, or error handling. For a tool with no output schema, the description should help the agent understand what data to expect, but it only mentions 'wallets and positions' without detail.
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 single parameter 'min_position_usd' is explained by the description mentioning 'sized >$10k' which aligns with the default. The schema has no property descriptions, but the tool description adds meaningful context for the parameter, though it could specify constraints like positive values.
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 returns top Polymarket whale wallets with positions >$10k, with a copy-trade reference. It is specific about the resource and its scope, and distinct from sibling tools which cover different topics.
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?
No explicit guidance on when to use this tool vs alternatives. The description mentions 'copy-trade reference' implying a use case, but does not specify conditions or alternatives. Differentiation from siblings is implicit via domain but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Material SEC 8-K filings today filtered by item code: 2.02 (earnings), 5.02 (officer change), 2.01 (M&A), 3.02 (dilution), 4.02 (restatement), 3.01 (delisting).
| Name | Required | Description | Default |
|---|---|---|---|
| items | No | item codes (e.g. ['2.02','5.02']) | |
| ticker | No | filter by ticker symbol |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description provides minimal behavioral information. It does not disclose whether results are real-time, if authentication is needed, or what happens when no filings match. Since no annotations exist, the description carries the full burden, and it falls short.
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, front-loaded sentence that efficiently conveys the core functionality and filtering options. Slightly more structure (e.g., bullet points for item codes) could improve readability, but it is still concise and informative.
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 description covers the essential aspects: today's filings, filtering by item codes and ticker, and the specific codes that are material. Given the simplicity of the tool and absence of an output schema, it is sufficiently complete for correct 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 coverage is 100% with basic descriptions. The tool description adds significant value by mapping each item code to its meaning (e.g., 2.02 = earnings), which is not present in the schema alone. This helps agents understand correct parameter values.
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 retrieves material SEC 8-K filings today, filtered by specific item codes. It lists the relevant codes with brief explanations, making the purpose explicit. However, it could start with an action verb like 'Retrieve' for even greater clarity.
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 (today's filings, specific item codes) but does not explicitly state when to use this tool versus alternatives. No guidance on when not to use or how it compares to sibling tools, which are different in nature.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Latest 24h top DeFi yield farm picks with realistic APY (emissions stripped), risk notes, TVL, protocol. Sourced from FalsifyLab daily aggregator.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | max results (1-50) | |
| min_apy | No | filter floor in pct | |
| asset | No | filter by asset symbol (BTC, ETH, SOL, etc.) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions 'realistic APY (emissions stripped)' and 'risk notes', hinting at data processing, but does not detail calculation methods, data freshness beyond '24h', error handling, or potential limitations. Adds some context but lacks comprehensive behavioral disclosure.
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?
Two concise sentences that front-load the main purpose and key features. No extraneous information. Every sentence adds value.
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 simple list tool with 3 optional parameters and no output schema, the description covers purpose, data source, and key outputs. However, it lacks explanation of risk notes derivation, default limit, or pagination behavior. Could be more complete but is adequate.
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 coverage is 100%, so baseline is 3. The description does not mention any parameters (limit, min_apy, asset) or explain their purpose beyond what the schema already provides. No added value over schema.
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 returns 'top DeFi yield farm picks' with specific attributes like 'realistic APY (emissions stripped), risk notes, TVL, protocol'. It specifies 'latest 24h' and the data source 'FalsifyLab daily aggregator', which distinguishes it from sibling tools like 'active_airdrop_farms'.
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?
While the description implies use for yield farming recommendations, it does not explicitly state when to use this tool versus alternatives like 'active_airdrop_farms' or provide any conditions or limitations. No guidance on prerequisites or exclusions.
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.
v0.1.0active_airdrop_farmsetf_flow_todayhl_vault_leaderboardinsider_buy_clustersmacro_tapepolymarket_whale_positionssec8k_material_todaytop_yield_farmsEach tool has a clear, distinct purpose covering different market signals (airdrop farms, ETF flows, vault leaderboard, insider trades, macro snapshot, Polymarket whales, SEC filings, yield farms). There is no overlap or ambiguity.
All tool names follow a consistent lowercase_with_underscores pattern, using descriptive phrases that clearly indicate the tool's function. The naming style is predictable and readable.
With 8 tools, the scope is well-balanced for a specialized market intelligence server. Each tool earns its place without being too few or too many.
The tools cover a diverse range of signals, but the set feels somewhat eclectic. Missing categories like general news, options data, or on-chain metrics do exist, but for the intended 'alpha signals' purpose, the coverage is reasonable.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
The financial MCP for AI agents - 90+ financial tables, SEC filings, signals, alt-data.
Real SEC, 13F, insider, congress & macro data your AI agent can cite. Hosted MCP, 24 tools.
Polymarket + Hyperliquid + macro for AI agents. 38 tools, signal backtest, SSE streaming. Free tier.
52 paid x402 API endpoints for AI agents — crypto, data, DeFi, market intelligence.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/FalsifyLab/falsifylab-alpha-mcp'
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