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

Beyond the annotations (readOnly, idempotent, openWorld, non-destructive), the description reveals significant behaviors: handle-shape-based network inference, a networks_tried field for ambiguous handles, a clear distinction between null days (outside feed reach) and measured zeroes, and the directional-proxy limitation. These are rich, non-obvious details that materially affect interpretation of results. No contradiction with annotations.

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 fairly long but every sentence serves a purpose: scope, output, usage examples, network inference, null handling, exclusions, and caveats. It front-loads the core purpose and uses examples to ground the handle format. The length is justified by the tool's complexity, though a tighter phrasing could earn a 5.

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?

With no output schema, the description carries the burden of conveying return behavior, and it does so well by explaining the time series shape, average engagement, most recent post timestamp, and the networks_tried field. It also articulates edge cases (null vs zero, ambiguous handles). Minor gap: it never names the exact JSON structure of the time series (e.g., array of {date, count}), but the textual description is sufficient for an agent to interpret results.

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 schema already provides complete descriptions for both parameters (handle and days) at 100% coverage, so the schema carries the load. The description adds some contextual color—e.g., handle shape inference and examples—but does not introduce parameter syntax beyond what the schema states (e.g., 'days' min/max/default are already in the schema). Baseline 3 is appropriate because the description adds marginal value over the schema.

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 a specific verb+resource: 'POSTING CADENCE for one social media account as a TIME SERIES,' and enumerates exactly what is returned (posts per day, average engagement, final post timestamp). It also distinguishes itself from siblings by explicitly excluding X/Twitter and naming the supported networks (Bluesky, Mastodon, Reddit, Hacker News), making the tool's scope unmistakable.

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 answers typical user questions ('how often does this account post', 'has this account gone quiet', etc.), which strongly signals when to use it. It also sets an important boundary: 'X/Twitter is excluded (paid API),' so users don't expect X coverage. However, it does not explicitly name alternative sibling tools like social_topic_volume for topic-based queries, leaving some ambiguity about when to choose this over related tools.

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