Natural language query on an HCS topic. Returns ranked messages and summary. 0.1 HBAR.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max messages to retrieve (default 50) | |
| query | Yes | Natural language question about the topic | |
| api_key | Yes | Your HederaIntel API key | |
| topic_id | No | Hedera topic ID (e.g. 0.0.8026796). Defaults to the HederaIntel platform topic. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral traits: that it returns 'ranked messages and summary' and costs '0.1 HBAR' – a financial side effect not visible in annotations. This goes beyond the baseline, though it omits details like rate limits or failure modes.
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 sentences with zero filler. It front-loads the core action, specifies the output, and notes the cost. Every word adds value, making it highly concise and well-structured.
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 read-only query tool with full schema coverage and annotations, the description adequately covers the return type and cost. It does not mention the default topic or when to choose this over sister tools, but given the low complexity and existing schema/annotation context, it is reasonably complete. A small gap is the lack of differentiation from similar hcs tools.
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 100% for all four parameters, so the schema fully documents each parameter. The description mentions 'natural language query' which aligns with the query param but does not add additional parameter-level meaning 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 performs a 'natural language query on an HCS topic' and returns 'ranked messages and summary' – a specific verb and resource with output scope. However, it does not explicitly differentiate from siblings like hcs_understand, hcs_monitor, or hcs_audit_trail, so it lacks explicit sibling distinction.
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?
There is no guidance on when to use this tool versus alternatives. The description only explains what the tool does without mentioning any prerequisites, exclusions, or preferred contexts. Sibling tools exist for monitoring, understanding, and auditing, but no comparison is offered.
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.
Each tool targets a distinct resource and action (e.g., contract read vs. contract call vs. contract analyze; hcs monitor vs. hcs query vs. hcs understand). Even within closely related groups, descriptions clearly differentiate the specific purpose, so an agent is unlikely to select the wrong tool.
Most tools follow a domain_action pattern (e.g., contract_analyze, token_monitor, hcs_verify_record), but some are verb_noun (get_terms, confirm_terms) and a few are noun_noun (account_info, token_price, fixatum_score). While not perfectly uniform, the prefixes make the intended domain clear and the overall structure is readable.
With 25 tools, the server is on the heavy end of the typical MCP scope. While each tool serves a specific niche across multiple Hedera domains (contracts, HCS, tokens, identity, governance, Fixatum), the sheer number could increase selection complexity, but it remains within the borderline range rather than being excessive.
The server covers a broad range of read/analyze operations across major Hedera areas, plus essential write/verification capabilities (hcs_write_record, hcs_verify_record) and an onboarding flow (get_terms, confirm_terms). Minor gaps exist, such as no token transfer history or unregister/update for Fixatum, but the core analytical purpose is well covered.