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Glama

Alphanume Datasets

by com.alphanume

Sector & Industry Classification

get_ticker_classification
Read-onlyIdempotent

Ticker sector/industry classification: maps every covered US ticker into Alphanume's own 11-sector / 25-industry scheme (derived from SIC codes, but normalized into groups actually useful for trading -- e.g. 'semiconductors' and 'software' are separate industries under 'technology'). One row per ticker: ticker, alphanume_sector, alphanume_industry.

Use it to group any other Alphanume dataset by sector, build sector-neutral baskets, or pull all names in one industry. Look up one ticker, or filter by sector/industry to enumerate a group. No date dimension and no tier restriction -- the mapping is current-state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectorNoFilter to one Alphanume sector.
tickerNoTicker symbol filter, e.g. 'AAPL'. Case-insensitive.
industryNoFilter to one Alphanume industry.
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.

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable context beyond that: it is a current-state snapshot with no date dimension, one row per ticker, and no tier restriction. It also explains the taxonomy origin (SIC-derived but normalized for trading), which helps the agent reason about grouping semantics.

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 definition sentence, a sentence clarifying the taxonomy, a use-case sentence, and a constraints sentence. Everything included earns its place, and the key output shape is front-loaded rather than buried.

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 the tool has no output schema, the description compensates by naming the exact output columns and row granularity. It covers filtering options, taxonomy design, use cases, and scope limitations. An agent has enough context to invoke it correctly and interpret the result.

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 description coverage is 100% and each parameter already has a clear description. The tool description adds general usage framing ('Look up one ticker, or filter by sector/industry') but does not add new semantic detail beyond the schema. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

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 verb-resource pair: 'maps every covered US ticker into Alphanume's own 11-sector / 25-industry scheme.' It clearly distinguishes itself from the sibling event/risk tools by being a classification/universe tool, and names the output schema (ticker, sector, industry) so an agent immediately knows what it returns.

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 explicit use cases: group datasets by sector, build sector-neutral baskets, enumerate a group by industry. It also states scoping constraints: no date dimension, no tier restriction, current-state mapping. It does not explicitly name an alternative tool or say when not to use it, but the sibling set contains no directly comparable classification tool, so this is adequate.

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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