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

The description goes beyond the annotations by disclosing the side effect of mark_read:true ('flag returned events read so the next call only shows newer ones') and describing the return payload (source, citation_uri, raw event). It also notes that polling is fine, addressing potential reliability concerns. This is exactly the kind of behavior context that annotations don't provide.

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 three tight sentences: the first states purpose and return contents, the second covers filtering and mark_read, and the third addresses polling alternatives. Every sentence earns its place, with no fluff or redundancy. The structure is front-loaded with the core action, making it easy for an agent to parse.

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 read-only tool with 5 optional parameters and no output schema, the description covers all critical aspects: what the tool returns, how to filter, the mark_read side effect, and polling behavior. It even provides an alternative access method. The inclusion of return payload details compensates for the lack of an output schema, making the description sufficiently complete for an agent to invoke 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?

With 100% schema description coverage, the baseline is 3. The description adds value by giving a concrete type example ('sec_8k'), clarifying that 'since' expects an ISO timestamp, and explaining the behavioral effect of mark_read. These enrich the raw schema definitions without repeating them, justifying a score above baseline.

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 opens with 'Pull fired events from your subscription feed', a specific verb+resource statement that clearly identifies the tool's function. It distinguishes itself from subscription management siblings like list_subscriptions and subscribe by focusing on the feed of fired alerts, and mentions the returned data fields (source, citation_uri, raw payload), leaving no ambiguity about its scope.

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 clear usage context: it states that polling works fine and offers an explicit alternative (the HTTP endpoint at registry.pipeworx.io/alerts.json) for scripts and dashboards. While it doesn't contrast with specific sibling tools, it does guide an agent on when to use this tool versus a non-tool alternative, which meets the bar for 'clear context, no exclusions'.

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