HCS topic status: message count, creation time, memo. Defaults to platform topic. 0.1 HBAR.
| Name | Required | Description | Default |
|---|---|---|---|
| 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 read-only nature is covered. The description adds useful behavioral context such as the default topic and the 0.1 HBAR cost, plus the specific output fields. However, it does not disclose error behavior, whether the fee is per call, or any details about pagination or data freshness. This adds some value beyond annotations but not substantial depth.
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: one sentence plus two short fragments, 13 words total. Every phrase adds information (purpose, defaults, cost). It is front-loaded with the core function and contains no fluff. This is an excellent example of minimal yet informative structure.
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 monitor with a small schema and clear annotations, the description is largely complete. It names the output fields (message count, creation time, memo), communicates the default topic, and notes the cost. The absence of an output schema makes the explicit return-field list helpful. Minor gaps like the meaning of '0.1 HBAR' and any error handling keep it from a perfect score.
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 both parameters (api_key and topic_id). The description adds a meaningful default behavior for topic_id ('Defaults to platform topic') and mentions the fee, but does not significantly expand on parameter semantics beyond what the schema already provides. The baseline of 3 applies because the schema carries the parameter documentation effectively.
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 identifies the tool's function as retrieving HCS topic status including message count, creation time, and memo. It is specific about the resource (HCS topic) and the output fields, but lacks an explicit verb like 'get' or 'retrieve'. While it mentions a default topic, it does not explicitly contrast with sibling tools such as hcs_query or hcs_understand, so it slightly misses on differentiation.
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?
The description provides minimal context about when to use this tool. It states the default to the platform topic and the cost (0.1 HBAR), but offers no guidance on when to choose this over alternatives like hcs_query or hcs_verify_record. No exclusions, prerequisites, or explicit use-case scenarios are mentioned.
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.