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faers_signal_screen

Read-onlyIdempotent

Screen a drug against all its reported adverse events or an event against all drugs, calculating ROR, PRR, and chi-square disproportionality signals with optional date filters.

Instructions

Screen a drug against all its reported events, or an event against all its drugs.

ROR, PRR and chi-square for every term with named criteria. The database-wide marginals come from a single cached count call rather than one call per term, so a 200-term screen costs ~13 calls cold and ~10 warm. Any date window or raw_filter is applied to every marginal.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	No	Screen a drug against its events, or an event against its drugs.	drug
`term`	Yes	Active substance (mode='drug') or MedDRA PT (mode='event').
`top_n`	No	How many terms to screen.
`date_to`	No	End of a receivedate window, YYYYMMDD. Must be paired with date_from.
`sort_by`	No	Statistic to sort rows by.	ror_lower
`return_n`	No	How many rows to return.
`date_from`	No	Start of a receivedate window, YYYYMMDD. Must be paired with date_to.
`min_cases`	No	Minimum case count for a row to be reported.
`raw_filter`	No	Extra Lucene clause ANDed onto the preset query, e.g. 'patient.patientsex:2'. Applied to every marginal of a contingency table. See faers_describe_fields.
`signals_only`	No	Return only rows meeting the EMA ROR criterion.
`max_fallback_calls`	No	Cap on per-term lookups for terms absent from the cached global marginal table.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 13, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld). The description adds genuinely useful behavior beyond that: the cached-marginal cost model (~13 calls cold, ~10 warm) and the rule that any date window or raw_filter propagates to every marginal of the contingency table. It stops short of rate-limit or failure behavior.

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?

Four sentences, front-loaded with the purpose, then statistics, then cost, then filter propagation. Efficient overall, though the call-count detail is more than strictly required for selection and slightly dilutes the otherwise tight structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present and 100% schema coverage, the description need not explain return values. It covers scope, statistics, cost, and filter propagation, leaving only explicit sibling routing as a gap for a tool with 15 siblings.

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 goes slightly beyond the schema by clarifying propagation semantics for date window and raw_filter ('applied to every marginal'), which the per-parameter schema text does not state, warranting a step above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (screen) and resource scope (a drug against all its events, or an event against all its drugs), and names the statistics produced (ROR, PRR, chi-square). The 'all its reported events/drugs' scope distinguishes it from the single-pair sibling faers_disproportionality, though it never names that sibling explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the bulk scope and the cost discussion, but the description never states when to pick this over faers_disproportionality, faers_ebgm, or faers_top_events, nor any prerequisites or exclusions. The agent must infer the right context from scope alone.

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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