chainvectorPredictionsEdge

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the output fields and explains the interpretation of a positive buy_edge ('the market underprices the outcome per the model'). It also notes that edges are 'net of the spread.' However, it doesn't explicitly state that this is a read-only query or mention any rate limits or connection behaviors, which prevents a perfect score.

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 two concise sentences. The first sentence defines the tool and lists its outputs; the second clarifies the meaning of a positive buy_edge. Every phrase earns its place, with no redundancy or filler.

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 tool with only two parameters (one required) and a query-like purpose, the description is largely complete. It names the four output fields, which mitigates the lack of an output schema. The only minor gaps are the absence of an explicit read-only statement and the lack of any mention of potential errors or rate constraints, but these are not critical for such a simple tool.

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 100%: both 'venue' and 'market_id' include descriptions in the schema. The tool description does not add additional parameter-specific meaning beyond the schema, so the baseline of 3 is appropriate. It does indirectly imply that market_id refers to the market being analyzed, but that is already captured in 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 clearly states the tool's purpose: computing the 'model-vs-market edge' by repricing the ChainVector probability-engine ensemble against a prediction market's live quote. It names the specific output fields (model_prob, market_prob, buy_edge, sell_edge) and distinguishes itself from sibling tools like chainvectorPredictionsQuotes and chainvectorProbability by combining both into an edge metric.

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 clearly implies when to use this tool: when you need to compare ChainVector model probabilities against a market's live quote to assess edge. It doesn't explicitly name alternatives or exclusions, but the phrase 'model-vs-market edge' sets it apart from related tools that provide only quotes or probabilities.

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