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Helium MCP Server - News, Markets & AI

by io.github.connerlambden

search_memes

Search Helium's meme database by text (OCR + caption).

Returns matching memes ranked by relevance. Each result includes:
- id, caption, ocr (text extracted from the image)
- image: full URL to the meme image
- source: origin platform (e.g. 'reddit')
- num_likes: likes/upvotes on the original post
- date, is_video, rank

Args:
    query: Search keywords (required). Matched against OCR text and captions.
    limit: Max results (1-100, default 20).
    days_back: Only include memes from the last N days. 0 means no date filter (default).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
days_backNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, but it provides return field details and mentions ranking by relevance. It does not disclose potential issues like rate limits, authentication, or behavior on no results, but the search context implies no side effects.

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 well-structured: a concise purpose statement, a list of return fields, and an Args block. Every sentence adds value, and it is front-loaded with the main use case.

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?

The description covers purpose, parameters, and return format. Despite no annotations, it is self-sufficient for a search tool, and the output schema exists to provide additional structure. It lacks only edge-case info, which is not critical for this simple search.

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

Parameters5/5

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

The description provides detailed explanations for all parameters in an Args section, going beyond the bare schema. It clarifies query matching, limit bounds, and the meaning of days_back=0, which is essential since schema description coverage is 0%.

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's function: searching Helium's meme database by text using OCR and captions. It distinguishes itself from sibling tools (news, bias, options) by specifying the meme database and the fields returned.

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 provides clear context on when to use the tool (to search memes by text) and implies a read-only search operation. It does not explicitly mention alternative tools or exclusions, but the domain difference from siblings makes usage apparent.

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

A4.4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but two pairs could cause confusion: get_all_source_biases vs. get_source_bias, and search_news vs. search_balanced_news. The descriptions explicitly differentiate them (list vs. single source; RSS vs. synthesized stories), so an agent can disambiguate with careful reading.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case: get_* for retrievals and search_* for searches. Any variations (e.g., get_all_source_biases vs. get_source_bias) are natural qualifiers and don't break the pattern.

Tool Count5/5

With 10 tools spanning news bias analysis, options data, ticker data, and trading strategies, the count is well-scoped. Each tool has a distinct role and the set is neither too thin nor overloaded.

Completeness4/5

The surface covers the core workflows: searching news, analyzing bias at both article and source level, retrieving market data, options chains, and strategy rankings. Minor gaps exist, such as no direct way to fetch a full article by ID or list all news sources, but these are workarounds with existing tools.

Resources