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SimpleFunctions

SimpleFunctions

by spfunctions
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

enrich_content

Cross-reference arbitrary text with live prediction markets: submit content and get matching markets plus an LLM digest for contextual enrichment.

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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "content": "<content>",
      "topics": "<topics>"
    },
    "name": "enrich_content"
  }
}

Input 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changedv0.1.2
    • changedInput schema / properties / content / description
      Previous value: -"Text content to analyze (max 50,000 chars)"New value: +"Raw text to analyze. Required. Max 50,000 characters."
    • addedInput schema / properties / content / maxLength
      Added value: +50000
    • addedInput schema / properties / content / minLength
      Added value: +1
    • changedInput schema / properties / model / description
      Previous value: -"LLM model for digest (default: gemini-2.5-flash)"New value: +"LLM model id for the digest step. Default: gemini-2.5-flash."
    • changedInput schema / properties / topics / description
      Previous value: -"Topics to search for in prediction markets"New value: +"Topic hints used to narrow the market search. Required, at least one. Free-form strings like \"oil\", \"fed rates\", \"tsmc\"."
    • addedInput schema / properties / topics / minItems
      Added value: +1
  2. First observedv0.1.1

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that the tool POSTs content (write operation), requires no auth, and does not persist data. This is good behavioral disclosure, though rate limits or error handling are not mentioned.

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, each earning its place: core functionality, technical traits, usage guidance. No wasted words, well-structured.

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

Completeness5/5

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

Given no output schema, the description adequately describes the return (markets and LLM digest). It covers purpose, behavior, parameters, and usage guidance comprehensively for a tool with 3 simple params.

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?

Schema coverage is 100%, so baseline is 3. The description adds a note about the default model for the digest step but does not significantly enhance parameter understanding beyond what the schema already provides.

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 the tool cross-references arbitrary text against live prediction markets and returns relevant markets plus an LLM digest. It distinguishes itself from siblings by specifying one-off article enrichment vs. scheduled 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?

Explicitly says 'Use for one-off article enrichment; use monitor_the_situation for scheduled URL scraping with webhook delivery.' This provides clear when-to-use and when-not-to-use guidance with a specific alternative sibling.

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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