match_news

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

With no annotations, the description carries the full behavioral disclosure burden. It does well by explaining the scoring semantics ('higher = stronger term overlap; >=15 is a confident match'), the output fields, and that matched_terms is provided so the agent can judge the match itself. It does not cover edge cases like no matches, but the disclosed behavior is substantive.

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 compact and front-loaded with the core purpose, then covers input format, output fields, and score interpretation. Every sentence adds useful information without restating schema boilerplate.

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 two-parameter read-oriented matching tool, the definition covers invocation, result contents, and score interpretation, and an output schema exists to handle remaining return-structure details. The main gaps are the unexplained top_k parameter and the lack of explicit routing against siblings like latest_matched_news, neither of which blocks correct use.

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 0%, so the description must compensate. It does explain the text parameter as a headline with optional body text, but it never mentions or explains top_k, leaving its meaning to be inferred from the parameter name and default. This is only partial compensation.

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 opening sentence identifies a specific verb-resource pair: 'Find the Polymarket markets a news headline or snippet could move.' It clearly distinguishes match_news from generic search_markets or trending_markets by focusing on news-to-market matching and term-overlap scoring.

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 explicit input guidance: pass a headline and optionally body text, and explains how to interpret results via match_score and matched_terms. It does not explicitly name alternative tools or state when not to use it, but the use case is clearly implied and actionable.

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