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enrich_content

Cross-reference text against live prediction markets to identify relevant markets and generate an AI digest summarizing key claims.

Instructions

Cross-reference arbitrary text against live prediction markets: paste an article or note, get back the markets relevant to its claims plus an LLM digest. POSTs content to the server; no auth required, no persistence. Use for one-off article enrichment; use monitor_the_situation for scheduled URL scraping with webhook delivery.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesRaw text to analyze. Required. Max 50,000 characters.
topicsYesTopic hints used to narrow the market search. Required, at least one. Free-form strings like "oil", "fed rates", "tsmc".
modelNoLLM model id for the digest step. Default: gemini-2.5-flash.
Behavior4/5

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

No annotations provided, so description carries full burden. Discloses critical behavioral traits: HTTP method ('POSTs content'), auth requirements ('no auth required'), and data persistence ('no persistence'). Minor gap: does not mention error handling behavior or LLM latency expectations, but covers key safety/privacy concerns.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with zero waste: sentence 1 defines function, sentence 2 covers technical/behavioral constraints, sentence 3 provides usage guidelines. Front-loaded with specific action and perfectly sized for the complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 3-parameter tool with POST behavior and LLM processing, description adequately covers input constraints (implied by schema), processing behavior, and conceptual output ('markets relevant to its claims plus an LLM digest') despite lacking formal output schema. Could enhance by mentioning rate limits or error scenarios.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage, establishing baseline 3. Description adds minor usage context ('paste an article or note') mapping to the content parameter, but largely relies on schema documentation for parameter semantics without adding format examples or validation details beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description provides specific verb ('Cross-reference') and resource ('live prediction markets'), clearly defining the tool's function. It explicitly distinguishes from sibling tool 'monitor_the_situation' by contrasting 'one-off article enrichment' vs 'scheduled URL scraping'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Excellent guidance with explicit when-to-use ('Use for one-off article enrichment') and explicit alternative ('use monitor_the_situation for scheduled URL scraping with webhook delivery'). Clear distinction between ephemeral text processing and scheduled monitoring workflows.

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

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