neuron_get_ws_frames

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

No annotations are provided, so the description carries the full disclosure burden. The verb 'Query' weakly implies a read-only operation, but nothing is disclosed about what is returned, whether frames are live-captured or persisted, how unfiltered queries behave, session requirements, or pagination. A single verb is insufficient behavioral context for a data-retrieval tool with zero annotation coverage.

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 sentence with zero wasted words, and the resource plus primary filter are front-loaded. It is efficient, though it arguably trades too much instructive content for brevity given the tool's four optional filter dimensions.

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 4-parameter query tool with no output schema, no annotations, and a sibling list of ~90 tools including several overlapping traffic/network queries, a single clause is incomplete. Missing: return shape, interaction between the three enum filters, default behavior when filters are omitted, and how it differs from neuron_search_traffic or neuron_get_requests.

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 only 25% (just the limit parameter is documented), so the description must compensate. It partially does by clarifying that platform is the key filter ('by platform'), but the semantics of direction (send/recv) and transport (ws/sse) are left entirely to enum inference, and limit is already covered by the schema. Three of four parameters receive no added meaning from the description.

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 verb (Query), a specific resource (WebSocket frame history), and the primary filter dimension (platform). The 'WebSocket frame' qualifier distinguishes it from sibling network tools like neuron_get_requests and neuron_get_logs, though it never explicitly names its closest sibling alternatives (neuron_search_traffic, neuron_export_har), leaving full differentiation to inference.

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 zero guidance on when to use this tool versus the dense field of traffic-related siblings (neuron_search_traffic, neuron_get_requests, neuron_export_har, neuron_waterfall). No conditions, exclusions, or alternatives are stated, so an agent trying to locate WebSocket frame data has no explicit routing help.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.