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Glama

AnswerPool (formerly CortexAssay)

Get any AnswerPool product

answerpool_get
Read-only

RUN ANY of the 133 AnswerPool products by id — this plus answerpool_search_catalog is the main path, and every product is reachable this way. Find an id with answerpool_search_catalog, then pass its inputs as a JSON object, e.g. {"product_id": "weather_alerts", "params": {"area": "CA"}}. Covers SEC filings, fundamentals and insider activity, Federal Register rulemaking, federal procurement and grants, NIH, clinical trials, FDA recalls and adverse events, US macro indicators, weather and severe-weather alerts, known-exploited vulnerabilities, research-topic momentum and entity resolution. Each product's input and output schema is in the catalog entry. Free products need no key; paid ones debit a prepaid credit key and are never charged on failure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsNo
product_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and non-destructive behavior, and the description adds meaningful behavioral context: paid products debit a prepaid credit key and are never charged on failure. It also discloses the broad product coverage, which helps the agent predict what kind of data will be returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main action and routes the call after catalog lookup. The domain list and billing note are long but earned, since they tell the agent what to expect and what a paid call may cost.

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 generic accessor with 133 possible products, the description covers the full call cycle: lookup, constructing the request, locating schemas, and billing behavior. An output schema exists for the response, so the lack of an explicit return-format description is not a gap.

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?

With 0% schema description coverage, the description compensates by showing the exact JSON shape (product_id plus params), explaining that product_id comes from the catalog search, and pointing to the catalog entry for each product's input and output schema. It does not enumerate every param, but that is delegated appropriately to the catalog.

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 ('RUN ANY ... by id') and a concrete resource ('the 133 AnswerPool products'), and immediately distinguishes this accessor from answerpool_search_catalog by describing it as the main execution path after lookup. This makes the tool's purpose unmistakable relative to its 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 an explicit workflow: find an id with answerpool_search_catalog, then pass the product inputs as a JSON object, with a concrete example. It does not spell out negative exclusions for other siblings, so it stops short of a full when-not-to-use statement.

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

A3.9/5.0
Disambiguation4/5

The discovery and account tools are clearly distinct, but answerpool_get is a generic executor that overlaps with answerpool_sec_company, since that SEC snapshot is likely also reachable through the catalog. list_products and search_catalog are similar in output but differ in 'list all' versus 'search', so the ambiguity is limited.

Naming Consistency3/5

All tools share the answerpool_ prefix, but the suffix style is mixed: list_products and search_catalog use verb_noun, while credit_balance and sec_company are noun phrases and get is a bare verb. This is readable but not a consistent naming convention.

Tool Count5/5

Five tools is well-scoped for a platform that exposes 133 products through a generic get endpoint plus discovery, account, and a convenience wrapper. Each tool has a clear role and none feel redundant enough to warrant removal.

Completeness5/5

The toolset covers the full user journey: discover products via search/list, execute any product via get, and check credit balance for paid calls. The sec_company shortcut is a bonus, and because answerpool_get can run any product, no product is left unreachable.

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