High-frequency orderbook microstructure for a token: quality score (0-100), quote persistence, concentration HHI, imbalance volatility, active anomaly flags. Detects bot/MM activity and depth resilience. Null-safe: has_data=false when no HF capture exists.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Trading pair symbol (e.g. BTC/USDT) | |
| exchange | Yes | Exchange ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility. It discloses a null-safe behavior ('has_data=false when no HF capture exists'), which is valuable. However, it does not specify whether the operation is read-only, permission requirements, rate limits, or performance characteristics, leaving significant 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 extremely concise—two sentences with no redundancy. It front-loads the key outputs and includes a crucial behavioral note (null-safety). Every word earns its place.
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 complexity of microstructure data and no output schema, the description does a decent job listing output fields. However, it lacks structural details (e.g., whether fields are nested, always present) and does not contextualize the tool among siblings or provide usage heuristics. It is adequate but not comprehensive.
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?
Input schema has 100% coverage with clear descriptions for both parameters (symbol, exchange). The description adds no further parameter-level detail, so it does not exceed the baseline. The mention of 'token' is generic and not parameter-specific.
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 defines the tool as retrieving high-frequency orderbook microstructure metrics (quality score, quote persistence, etc.), distinguishing it from sibling tools like get_price or get_stats. The verb is implied but the resource and outputs are well-specified.
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 is provided on when to use this tool compared to alternatives (e.g., get_token_intel). The description does not mention prerequisites, limitations, or preferred contexts, leaving the agent to infer usage from the output listing alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but get_token_intel, get_token_research, and get_microburst overlap in coverage of token intelligence, which could cause agent misselection. Descriptions are detailed but some redundancy exists.
Tools follow a consistent verb_noun pattern (agent_*, get_*, search_tokens). Minor deviations like 'get_microburst' and 'get_quartermaster' use less conventional nouns, but overall pattern is clear.
With 15 tools, the count is well-scoped for an intelligence platform covering agent interaction, token data, trading insights, and cross-venue analysis. Each tool serves a distinct purpose without being overwhelming.
The set covers identity management, fundamental data, price/market stats, orderbook microstructure, cross-venue verification, and comprehensive token intelligence. Minor gaps like historical data or advanced analytics are omitted, but core workflows are well-supported.