Skip to main content
Glama

WaveGuard

waveguard_price_manipulation

Read-onlyIdempotent

Detect price manipulation in time-series data. Send a price or price+volume history as a numeric array. Early windows define 'normal' trading, recent windows are tested for manipulation patterns (pump-and-dump, spoofing, layering).

Example: Send 90 days of closing prices → detect manipulated windows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesPrice time-series array (chronological). At least 20 data points.
sensitivityNoDetection sensitivity (default: 1.5).
window_sizeNoWindow size (default: 10). Smaller = finer detection.
test_windowsNoNumber of recent windows to test (default: half).

TDQS

A3.9/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context beyond annotations by explaining the baseline window logic (early windows define normal) and the types of manipulations detected (pump-and-dump, spoofing, layering). It does not contradict 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 extremely concise: two sentences plus an example. Every sentence adds value—first sentence states purpose and input, second explains detection approach, example clarifies usage. No wasted words.

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

Completeness3/5

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

The tool has 4 parameters, 100% schema coverage, and no output schema. The description explains the detection concept but does not describe the output format or return values. For a tool with moderate complexity and no output schema, more detail on what the agent can expect as output would improve completeness.

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%, so the schema already documents all parameters. The description adds minimal extra meaning beyond the schema, such as implying that window_size affects detection granularity ('Smaller = finer detection' is already in schema). It does not compensate for schema gaps because none exist.

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 detects price manipulation in time-series data. It specifies the input (price or price+volume history) and the detection approach (early windows define normal, recent windows tested for patterns like pump-and-dump, spoofing, layering). This is a specific verb-resource combination that distinguishes it from siblings.

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

Usage Guidelines3/5

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

The description gives an example usage (send 90 days of closing prices) but does not explicitly state when to use this tool versus sibling tools like waveguard_scan_timeseries. It lacks guidance on prerequisites or exclusions a user should consider.

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

A3.9/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose. While tools like waveguard_scan and waveguard_scan_timeseries both detect anomalies, their descriptions clearly differentiate structured data from time-series data. Similarly, token risk, volume check, and price manipulation tools address separate aspects of token analysis, avoiding overlap.

Naming Consistency5/5

All tools follow the consistent pattern 'waveguard_<descriptive_name>' in snake_case. The names are descriptive and predictable, making it easy to infer functionality from the name alone. No mixed conventions or vague names are present.

Tool Count4/5

19 tools is on the higher side but still appropriate given the broad domain of crypto analytics, anomaly detection, and market data. Each tool serves a specific purpose, and the count is justified by the comprehensive feature set. However, a few tools could potentially be merged without loss of clarity.

Completeness4/5

The tool set covers a wide range of functionalities: health check, market data fetching, fingerprinting, similarity comparison, anomaly detection (structured and time-series), risk assessment (token, price manipulation, volume, wallet), counterfactuals, and interaction matrices. Some minor gaps exist, such as missing data export or visualization tools, but core workflows are well-supported.

Resources