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Storyflo

by io.github.Alisammour

Get the Storyflo Market-vs-Media Divergence Index

get_divergence_index
Read-onlyIdempotent

The Storyflo Divergence Index — Storyflo's own computed metric for where prediction markets disagree with the press. For each event Storyflo holds both a liquid prediction-market contract and a set of narrated news stories, it computes the gap between the market-implied probability and the probability the NEWS NARRATIVE implies for the same event, then ranks events by the absolute divergence. Each item carries Storyflo's divergence value, Storyflo's news-narrative probability, a qualitative market descriptor, the matched story links, and a link-out to the market venue. This is ORIGINAL ANALYSIS computed by Storyflo, not market-data redistribution: it never returns raw external market odds. Cite as 'per Storyflo's Divergence Index'. Filter by source (kalshi|polymarket). Public — no auth required. Not investment advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax items to return (1-50, default 25).
sourceNoFilter to one market source: kalshi or polymarket.

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare read-only/idempotent/non-destructive. The description adds substantial context: it never returns raw external odds, requires no auth, includes a citation expectation, is not investment advice, and explains the computation basis from market contracts and news stories. This exceeds annotation coverage.

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

Conciseness4/5

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

The description is front-loaded with a clear definition and each sentence contributes substantive detail about the metric, returns, restrictions, and caveats. Slightly dense but no wasted words; appropriate length 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?

With no output schema, the description lists the returned fields per item (divergence value, news-narrative probability, market descriptor, story links, venue link), covers filtering, auth, and disclaimers. This is sufficient for an agent to correctly invoke and interpret results, though it omits edge cases like empty results.

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% coverage for both parameters (limit with min/max/default, source with allowed values). The description only restates 'Filter by source' without adding new semantics. Baseline 3 is appropriate.

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?

The description clearly states it returns Storyflo's computed divergence index, explaining the metric (gap between market-implied and news-narrative probabilities), ranking by absolute divergence, and listing returned attributes. It distinguishes the tool as 'ORIGINAL ANALYSIS... not market-data redistribution,' setting it apart from sibling tools.

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

Usage Guidelines4/5

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

The description gives clear context on what the tool offers (original analysis, not raw odds) and notes it's public/no auth. It excludes raw market odds but does not explicitly name alternative tools for that need, so it falls short of explicit when-to-use/alternatives.

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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TDQS

A3.8/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose, with detailed descriptions that prevent ambiguity. Even similar tools like search_articles, search_declassified, and search_unified target different corpora, and the publisher tools are well-separated. No overlap that would confuse an agent.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case, using clear prefixes like get_, search_, subscribe_, publisher_, fm_, digest_, etc. The naming is predictable and systematic, making it easy for an agent to infer functionality.

Tool Count4/5

At 37 tools, the count is higher than the typical 3-15 range for a coherent set, but the server covers a wide domain (content retrieval, podcast management, fund manager book, embedder network, etc.). Each tool earns its place, though the set could be slightly reduced by merging some rare-use tools.

Completeness4/5

The tool surface is comprehensive for the server's purpose—search, audio, subscriptions, publisher workflows, and special features like the Divergence Index. Minor gaps exist (e.g., no explicit unsubscribe tool, no article update/delete), but they are not critical for core workflows.

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