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

The description goes far beyond the annotations by detailing the internal model families (crypto_price, news_momentum, partition_overround, concentrated_longshot), per-sport alpha values, filter logic (placeholder slug filter, partition skip threshold), the 24h-move warning, caching behavior (1h at KV level), and diagnostics. This rich behavioral context is additive to the readOnly/openWorld/idempotent hints, which it does not contradict.

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 dense and lengthy, cramming model details, per-sport alphas, gate thresholds, and diagnostics into a wall of text. It is somewhat structured with bolded segment labels (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, etc.), which aids navigation, but not every sentence earns its place—e.g., specific α values could be externalized. It is not concise; it is thorough but overstuffed.

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 high complexity and absence of an output schema, the description covers everything needed: the top-level response structure (by_segment, fed_candidates, _diagnostics), per-opportunity fields (edge_pp_net, kelly_fraction, liquidity), the tradeable-edge knobs, and the Fed candidates exclusion rationale. It also explains caching and why segments may be empty, providing a complete picture for an agent.

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?

Although the input schema provides 100% parameter coverage, the description adds meaningful grouping and behavioral nuance: 'TRADEABLE-EDGE KNOBS' groups min_liquidity/max_spread_pp, and it explains that min_kelly does not apply to partition arbs, pointing to min_partition_leg_kelly instead. It also gives actionable advice (e.g., 'Bump for very thin partitions; drop to 0 if you have a smarter fill model'), which is beyond the schema's descriptive sentences.

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: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' This clearly states the tool's purpose and distinguishes it from sibling tools like polymarket_arbitrage (which likely finds cross-market discrepancies) and polymarket_edge_tracker (which suggests historical tracking). The 'what should I bet on today' phrase grounds the use case.

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 gives clear context for when to use it ('Built for "what should I bet on today"—agents discover opportunities without paging hundreds of markets') and explains usage knobs (e.g., min_liquidity, max_spread_pp) and the tradeable-edge concept. However, it does not explicitly name alternatives or state when not to use it relative to siblings, so it falls short of a 5.

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