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enrich_content

Cross-reference any text with thousands of prediction market contracts. Paste content + topics, get divergence analysis: where sentiment disagrees with market prices. No auth, no Firecrawl needed. Demo/trial entry point.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoLLM model for digest generation. Default: google/gemini-2.5-flash
topicsYesTopics to search in prediction markets, e.g. ["iran", "oil"]
contentYesText content to cross-reference (up to 50K chars)
includeIndexNoInclude SimpleFunctions Index

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / includeIndex / description
      Previous value: -"Include SF Index"New value: +"Include SimpleFunctions Index"
  2. First observed

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the burden. It adds behavioral notes: 'No auth, no Firecrawl needed' and 'Demo/trial entry point', indicating a lightweight, possibly limited service. Still, it does not disclose read-only status, rate limits, or response format, so only partial transparency.

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?

The description is two sentences, front-loaded with the primary action. Every phrase earns its place: the resource, the input, the output, and the access prerequisites. No redundant or filler text.

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

Completeness3/5

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

For a 4-parameter tool with no output schema, the description covers the main use case and access conditions but omits expected return structure or limitations (e.g., what the divergence analysis looks like, any output size constraints). It's adequate for a demo entry point but leaves the agent with some uncertainty.

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

Parameters4/5

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

Schema coverage is 100%, but the description adds meaning beyond the schema by explicitly tying 'content' and 'topics' to the paste-and-analyze workflow, and explaining that they drive the divergence analysis. It doesn't elaborate on model/includeIndex, but the schema already handles those; the added context for the core required params is valuable.

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 uses a specific verb ('cross-reference') and resource ('prediction market contracts'), and clearly explains the output ('divergence analysis'). It distinguishes itself from sibling tools like query or scan_markets by specifying the unique input (text + topics) and purpose (sentiment vs market prices).

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

Usage Guidelines3/5

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

It clarifies that no auth or Firecrawl is needed and that this is a demo/trial entry point, which gives some context on when it's appropriate. However, it does not explicitly name alternative tools or state when not to use it, leaving the comparison to siblings implicit.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple market query tools (scan_markets, screen_markets, get_market_detail, get_market_diff, get_market_history, inspect_ticker) and legislative tools (legislation, get_legislation, list_legislation, query_gov). Aliases like get_heartbeat_config/get_heartbeat_status and explore_public/explore_theses add further confusion. An agent would struggle to select the correct tool without deeply reading each description.

Naming Consistency3/5

Most tools follow a verb_noun pattern (get_, list_, create_, update_), but there are notable deviations: 'legislation' lacks the 'get_' prefix, 'stt' and 'tts' are acronyms, 'monitor_the_situation' is a full phrase, and 'x_account/x_news/x_volume' use a non-standard prefix. The overall style is readable, but the mixed conventions reduce predictability.

Tool Count1/5

108 tools is extreme for any server, even one covering prediction markets, trading, portfolio management, forum, skills, and speech. The massive surface area overwhelms agents and makes the server feel more like a platform than a coherent toolkit. This many tools inevitably leads to redundancy and maintenance burden.

Completeness4/5

The server covers an impressively broad domain: market data, thesis management, intents, strategies, positions, portfolio, forum, skills, legislative and economic queries, and audio/visual processing. Minor gaps exist (e.g., no delete for skills/theses, no update for some portfolio items) but core workflows are well-supported. Overall lifecycle coverage for most entities is strong.

Resources