Live $BOBAI liquidity depth: pool reserves, liquidity in USD, LP-burned percentage (perma-locked), and price-impact estimates for common buy sizes (0.1–5 BNB).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds context like 'Live' and 'perma-locked' but does not disclose details such as data source, update frequency, or calculation methodology for price-impact estimates. This is acceptable given the safety annotations but adds only modest behavioral context beyond them.
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, well-structured sentence that leads with the core purpose ('Live $BOBAI liquidity depth') followed by a compact list of deliverables. Every listed element adds value, and there is no redundant or filler text.
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 parameterless, read-only tool with no output schema, the description covers the essential information an agent needs: what data is available. Minor aspects like which DEX pool is queried or how 'live' is guaranteed are absent, but these are not critical for invoking the tool correctly.
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 tool accepts no parameters, and the baseline for zero-parameter tools is 4. The description still helps by listing what output areas the tool covers, which indirectly informs the agent what to expect despite having no input schema to elaborate on. No parameter documentation is needed here.
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 names a specific resource (BOBAI liquidity depth) and enumerates concrete data points it provides: pool reserves, USD liquidity, LP-burned percentage, and price-impact estimates. This clearly distinguishes it from siblings like bobai_price or bobai_burned by emphasizing depth and impact rather than simple price or burn totals.
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?
Usage is implied: an agent can infer this tool should be used when liquidity depth, pool reserves, or price-impact information is requested. However, it does not explicitly state when not to use it or mention any alternative tools, even though closely related siblings exist such as bobai_dex_info and bobai_burned.
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.
Several tools operate on overlapping data: bobai_burned, bobai_activity, and bobai_smart_money all report burn-related metrics, while bobai_token_info, bobai_circulating_supply, and bobai_burned overlap on supply numbers. The long descriptions mitigate this, but the purpose boundaries are less crisp than ideal. The BSC pool tools are more easily told apart.
Most token tools use a consistent bobai_* snake_case prefix, but the set mixes conventions: bnb_agent_*, bsc_pool_scan, find_agents_on_bnb_chain, and pancakeswap_fee_tiers break the pattern. The names are still readable and mostly noun-oriented. There is no chaotic mixing of camelCase or vague verbs, but the pattern is not uniform.
19 tools sits in the borderline heavy range for a token research server. Many are focused data views, but some could be consolidated, such as bobai_token_info with circulating supply/burned, or the informational guide/links/tokenomics cluster. Each tool has a defensible role, but the overall surface feels padded.
Core workflows are well covered: price, liquidity, DEX parameters, purchase guidance, burns, wallet balances, and BNB agent discovery. Missing historical price/volume, holder distributions, or detailed per-agent profiles, but these are minor gaps rather than dead ends. The server gives an agent what it needs to understand and interact with $BOBAI.