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drvvek
by drvvek

faers_warm_cache

Read-onlyIdempotent

Prebuild the EBGM background table and fit the prior before running analyses, preventing first-call timeouts and speeding later score retrieval.

Instructions

Build the EBGM background table and fit the prior ahead of time.

The first faers_ebgm call for a FAERS release takes ~100 s and 100+ API calls, which exceeds many MCP client timeouts. Calling this tool first moves that cost to a turn that expects it; subsequent faers_ebgm calls take a few seconds. Reports whether each cache was already warm.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
date_toNoEnd of a receivedate window, YYYYMMDD. Must be paired with date_from.
date_fromNoStart of a receivedate window, YYYYMMDD. Must be paired with date_to.
stratify_byNoAlso build the per-stratum marginals and fit the stratified prior.
background_drugsNoSubstances in the background table the prior is fitted on. Building it costs 3 + this many calls, once per FAERS release, then it is cached.
background_eventsNoTerms in the background table.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Discloses latency, API-call cost, timeout risk, and warm-cache reporting — none of which the annotations carry. The annotations cover safety (readOnly/idempotent/non-destructive), and the description adds the operational economics the agent needs to sequence calls, clarifying that the behavior is a benign cache build rather than a domain mutation.

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?

Four tight sentences, front-loaded with the action and immediately followed by the justification. No filler, and the cost/benefit framing is exactly what an agent needs before committing to a long call.

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?

An output schema exists, so return values need no explanation, and the description still notes it reports warm-cache state. With annotations covering safety and the schema covering all five parameters, nothing required to invoke this correctly is missing.

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 every parameter including the date-window pairing rule and the enum for stratify_by is already documented inline. The description only alludes to 'background table' and 'prior' without adding format or constraint detail beyond the schema, so the baseline 3 is correct.

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?

States a concrete action on a named artifact: build the EBGM background table and fit the prior. It explicitly positions itself relative to faers_ebgm, so an agent can distinguish it from all 14 siblings without opening a schema.

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?

Gives explicit when-to-use guidance (call this first, before faers_ebgm) and quantifies why (the first faers_ebgm call costs ~100 s and 100+ API calls and can exceed client timeouts). It also implies when not to bother, since it reports whether each cache was already warm.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.