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Alphanume Datasets

by com.alphanume

Wikipedia Page Views

get_wikipedia_views
Read-onlyIdempotent

Wikipedia attention tracker: daily page views of each covered company's Wikipedia article, with a 30-day rolling average and a relative 30-day z-score. Retail and media attention shows up in Wikipedia lookups before (and during) big price moves -- a z-score spike means the name is suddenly being researched far more than its own baseline. One row per ticker per day: ticker, company name, date, raw views, avg_30d, zscore_30d.

Screen zscore_30d_gte=3 over recent dates for fresh attention spikes, or pull one ticker's history to line attention up against price. Pagination: results are capped at 50,000 rows per request; when the response has has_more=true, pass next_cursor's date and ticker back as cursor_date and cursor_ticker to fetch the next page.

Requires an Alphanume Pro API key. A 403 PRO_SUBSCRIPTION_REQUIRED or DATE_RANGE_RESTRICTED error means the key's plan does not cover the request -- it does not mean the data is missing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoExact date, YYYY-MM-DD. Cannot be combined with the date range parameters.
tickerNoTicker symbol filter, e.g. 'AAPL'. Case-insensitive.
date_gtNoStart of date range, exclusive (YYYY-MM-DD).
date_ltNoEnd of date range, exclusive (YYYY-MM-DD).
date_gteNoStart of date range, inclusive (YYYY-MM-DD).
date_lteNoEnd of date range, inclusive (YYYY-MM-DD).
max_rowsNoMaximum data rows to return to the client (applied after the API responds). Default 500. Use 0 for no cap. Prefer narrowing with date/ticker filters over raising this.
cursor_dateNoPagination: the 'date' value from the previous response's next_cursor. Must be sent together with cursor_ticker.
cursor_tickerNoPagination: the 'ticker' value from the previous response's next_cursor. Must be sent together with cursor_date.
zscore_30d_eqNoExact 30-day z-score match. Cannot be combined with the z-score range parameters.
zscore_30d_gtNoOnly rows with 30-day z-score > this value.
zscore_30d_ltNoOnly rows with 30-day z-score < this value.
zscore_30d_gteNoOnly rows with 30-day z-score >= this value. zscore_30d_gte=3 finds extreme attention spikes.
zscore_30d_lteNoOnly rows with 30-day z-score <= this value.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses pagination details (50,000-row cap, has_more, next_cursor and cursor parameters), requires an Alphanume Pro API key, and explains that 403 errors indicate plan coverage rather than missing data. It also describes the one-row-per-ticker-per-day shape since no output schema exists.

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 front-loaded with purpose and output shape, and every later section earns its place: use-case guidance, pagination mechanics, and error interpretation. Despite covering 14 parameters, it stays compact and scannable.

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?

For a 14-parameter tool with no output schema, the description plus 100% schema coverage covers output fields, date/ticker filters, pagination, auth requirements, and error semantics. Nothing essential is missing for an agent to select and invoke the tool correctly.

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%, so the baseline is 3. The description adds operational meaning by framing zscore_30d_gte=3 as an attention-spike screen and by explaining how cursor_date and cursor_ticker should be passed from next_cursor, which goes beyond the schema's standalone parameter descriptions.

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 opens with a specific resource ('daily page views of each covered company's Wikipedia article') and a clear analytical output (30-day average and z-score). The phrase 'one row per ticker per day' and the listed fields make the return shape concrete, and this Wikipedia-specific tool is clearly distinguishable from all financial-data siblings.

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?

It gives explicit usage patterns: screen zscore_30d_gte=3 for fresh attention spikes or pull one ticker's history to compare with price. It doesn't name alternative tools or state when-not-to-use, but no sibling appears to compete in this domain, so the context is sufficient.

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

A4.2/5.0
Disambiguation4/5

Each tool maps to a distinct dataset, and the descriptions are detailed enough to resolve most ambiguity. A few adjacent pairs (S-1 dilution vs. shelf registrations, IV-HV premium vs. IV rank, FDA votes vs. FDA adverse events) share thematic surface area and could be confused by name alone.

Naming Consistency4/5

The overwhelming majority of tools follow a clean get_<noun_phrase> snake_case pattern. The two exceptions, check_api_status and list_market_cap_tickers, are semantically appropriate utility/companion tools but break the otherwise uniform verb prefix.

Tool Count3/5

At 27 tools, the surface is heavy and spans many unrelated financial domains, making selection and prompt context more expensive. Each tool does earn its place as a distinct dataset, but the server would benefit from some consolidation or a higher-level catalog tool.

Completeness4/5

As a read-only datasets API, the surface is broadly complete: status checking, pagination, and one coverage-map companion exist where needed. Minor gaps include the absence of a global dataset catalog/coverage listing and the lack of companion list tools for most other datasets.

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