Skip to main content
Glama
masa-med-ai

typesafe-screening-mcp

by masa-med-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

screen_pmids

Screens PubMed articles against a research question by fetching titles and abstracts server-side, returning include/maybe/exclude decisions with calibrated probabilities.

Instructions

Judge whether the given PubMed articles match the user's query / clinical question (CQ).

Use this when you already have PMIDs; use search_and_screen to search and judge in one step. Titles and abstracts are fetched server-side from PubMed, so abstracts stay out of the conversation. Each article gets include / maybe / exclude plus the underlying probabilities, sorted by match probability.

Args: research_question: The user's query or CQ, as ONE self-contained sentence in ENGLISH (Jev is most accurate in English and reads literally - translate Japanese input, spell out abbreviations, avoid negations/double negatives). pmids: PubMed IDs to screen. inclusion_criteria: Optional extra criteria, each a short positive English statement (e.g. "The study is a randomized controlled trial"). An unmet criterion demotes include to maybe; it never excludes, since abstracts often omit such details. exclusion_criteria: Optional; a confidently met criterion excludes the article (e.g. "The article is a case report"). include_threshold: match probability at or above which an article is included. exclude_threshold: match probability at or below which an article is excluded. return_decisions: Which groups to list in "results". Default ["include", "maybe", "error"]; "counts" always covers every article. Add "exclude" only for small batches. save_full_results_to: Optional file path (.json). Every result, including excluded articles, is written there in full detail. Fails if the file already exists. detailed: False (default) returns one line per article, grouped by decision: "PMID | match | title". True returns every probability per article (much longer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pmidsYes
detailedNo
return_decisionsNo
exclude_thresholdNo
include_thresholdNo
research_questionYes
exclusion_criteriaNo
inclusion_criteriaNo
save_full_results_toNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A5/5.0
Behavior5/5

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

With no annotations, the description carries full responsibility and does so thoroughly: it discloses that titles/abstracts are fetched server-side so abstracts stay out of the conversation, explains how inclusion_criteria demote include to maybe but never exclude, notes exclusion_criteria behavior, states save_full_results_to fails if the file already exists, and describes sorting by match probability. This is rich behavioral detail beyond the schema.

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 long but every sentence earns its place. It front-loads purpose and usage, then organizes argument semantics in a scannable Args list. The length is justified because there are 9 undocumented parameters and no annotations, and the wording is tight rather than redundant.

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 9 parameters, no output schema, and no annotations, the description is remarkably complete. It covers all parameters, return groups, threshold behavior, criteria semantics, output verbosity, and the side effect of save_full_results_to. There are no critical gaps an agent would need to guess about.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully compensate. It does: every parameter is explained with constraints, defaults, examples, and behavioral consequences. For instance, research_question must be one self-contained English sentence, inclusion_criteria get a concrete example and demotion rule, and return_decisions explains default group membership and the 'counts' coverage.

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 and resource: 'Judge whether the given PubMed articles match the user's query / clinical question.' It also distinguishes itself from the sibling search_and_screen by stating 'Use this when you already have PMIDs.' This leaves no ambiguity about what the tool does.

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?

Explicit usage guidance is provided: 'Use this when you already have PMIDs; use search_and_screen to search and judge in one step.' It also gives practical advice on thresholds, criteria behavior, and warns to add 'exclude' only for small batches. The description clearly tells the agent when to choose this tool over an alternative.

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

Deploy Server

Other Tools