Skip to main content
Glama

waveguard_market_data

Read-onlyIdempotent

Fetch live crypto market data from CoinGecko and DexScreener. No external data needed — WaveGuard pulls it for you.

Use 'coin_id' for CoinGecko (e.g. 'bitcoin', 'ethereum', 'solana'). Use 'contract_address' for DexScreener (any chain). Use 'search' to find token IDs by name/symbol.

Returns: price, volume, market cap, liquidity, price history, OHLC candles — ready to feed into waveguard_token_risk, waveguard_volume_check, or waveguard_price_manipulation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoNumber of days of history (default: 90 for price_history, 30 for ohlc).
countNoNumber of results for top_coins (default: 25).
queryNoSearch query. Required for search, dex_search.
actionYesWhat data to fetch: - token_data: full metrics for a CoinGecko coin - price_history: daily prices (for price_manipulation) - ohlc: OHLC candles (for volume_check) - top_coins: top N by market cap (training baseline) - search: find CoinGecko coin IDs - dex_token: DEX data by contract address - dex_search: search DEX pairs
coin_idNoCoinGecko coin ID (e.g. 'bitcoin', 'ethereum'). Required for token_data, price_history, ohlc.
contract_addressNoToken contract address (any chain). Required for dex_token.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already mark the tool as readOnly and idempotent, but the description adds behavioral context beyond that: it pulls data from external sources ('WaveGuard pulls it for you') and outlines the return content (price, volume, market cap, etc.). This helps the agent understand side effects and output expectations without contradicting annotations.

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

Conciseness5/5

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

The description is compact and front-loaded, with the main purpose in the first sentence and key parameter usage in short bullet-like lines. Every sentence serves a purpose: clarifying data sources, parameter mapping, and downstream integration. No fluff or repetition.

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

Completeness4/5

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

The tool has a moderate number of actions and parameters, but the schema already explains each action and parameter. The description adds value by clarifying data source selection, integration with other tools, and high-level return fields. Given no output schema, it covers the essential context for an agent to invoke the tool correctly.

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 schema descriptions are complete (100% coverage), so the baseline is 3. The description repeats examples already in the schema (e.g., 'bitcoin', 'any chain') but does not add new semantic details beyond what the schema provides. It serves as a quick reference but not a meaningful extension.

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

Purpose5/5

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

The description clearly states the tool's function: 'Fetch live crypto market data from CoinGecko and DexScreener.' The verb 'fetch' plus the named resources (CoinGecko, DexScreener) makes the purpose specific. It also distinguishes from sibling tools by highlighting integration with risk/volume/manipulation tools, setting it apart as the data provider.

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 offers explicit parameter guidance ('Use coin_id for CoinGecko', 'Use contract_address for DexScreener') and mentions the tool's output is 'ready to feed into' other WaveGuard tools, implying when it should be used. However, it does not explicitly state exclusions or alternatives, so it falls short of a 5.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.4/5.0
Disambiguation3/5

Several tools occupy overlapping anomaly-detection territory (scan, scan_timeseries, price_manipulation, volume_check, token_risk, wallet_profile), which could cause misselection when an agent needs generic vs. specialized analysis. However, descriptions clarify data types and use cases, so the overlap is manageable.

Naming Consistency5/5

All tools share the consistent 'waveguard_' prefix with descriptive underscore-separated names (e.g., waveguard_cascade_risk, waveguard_volume_check). The occasional verb like 'scan' or 'compare' fits the overall pattern, making the set highly predictable.

Tool Count3/5

With 19 tools, the server is on the heavy side for a typical MCP but not extreme. The breadth reflects a comprehensive risk-analysis platform, though some specialized detectors (e.g., waveguard_price_manipulation vs. waveguard_scan_timeseries) could potentially be consolidated without losing functionality.

Completeness4/5

The tool surface covers the full analytical workflow: data ingestion (market_data), generic anomaly detection (scan, scan_timeseries), specialized crypto risk (token_risk, volume_check, wallet_profile), structural similarity (fingerprint, compare), and scenario/impact analysis (counterfactual, cascade_risk, mechanism_probe). Minor gaps like direct report generation exist but are not critical for the core purpose.

Resources