Get Market Context

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

Beyond annotations (readOnly, idempotent, non-destructive), the description discloses significant behavioral traits: token-budgeted greedy filling in priority order, per-section token counts, as_of timestamps, citable URLs, the inclusion of a 'Claude-written' outlook, and the return of omitted sections. This exceeds what annotations provide and gives agents a clear mental model of the tool's execution.

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 relatively long but well-structured, front-loaded with the use case, followed by content details, example, parameters, return format, and exclusions. Every sentence contributes meaning, but the density of information could be slightly overwhelming. Still, it is appropriately sized for the tool's complexity.

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?

Given the tool's complexity (multiple content sections, token budget, return structure) and the absence of a full output schema in the provided context, the description thoroughly covers the return shape, parameter usage, and behavioral nuances. It also includes citation guidance and exclusions, making it complete for agent decision-making.

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 coverage is 100%, so baseline is 3. The description adds value beyond the schema by providing a concrete example (market=columbus max_tokens=4000), explaining the valid source for market slugs (rank_markets), and detailing the greedy filling behavior of max_tokens. This is more than just a baseline but not exhaustive parameter documentation.

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's purpose: returning a token-budgeted whole-market context pack with specific content sections. It uses a specific verb ('get') and resource ('market context'), and distinguishes itself from sibling tools like get_market_dcpi_rank and rank_markets by explicitly describing its niche as a narrative briefing pack.

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 explicitly says when to use it ('when an agent needs a WHOLE-market briefing') and provides concrete examples of when NOT to use it, naming specific alternative tools (get_market_dcpi_rank, get_market_intel, rank_markets). It also includes an example invocation, making the usage context highly actionable.

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