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Emdb

by io.github.pipeworx-io

Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent), the description reveals key behaviors: data sources (SEC EDGAR/XBRL, FAERS), fiscal year handling, sorted results by primary metric, and return format (paired data + citation URIs). No contradictions with annotations.

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 thorough and front-loaded with usage examples, but slightly verbose. Every sentence adds value, but it could be tightened without loss of clarity.

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's complexity (two entity types, multiple data sources, no output schema), the description is comprehensive. It covers input, behavior, output (paired data + citations), and even addresses edge cases like off-calendar fiscal years.

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 coverage is 100%, but the description adds significant meaning: it explains that 'values' can be tickers/CIKs for companies and drug names for drugs, clarifies the min/max constraints, and maps enum options to specific data retrieval behaviors.

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 clearly states the tool's purpose: side-by-side comparison of 2-5 companies or drugs. It uses specific verbs ('Compare', 'rank') and resources (SEC EDGAR/XBRL, FAERS), and distinguishes it from sequential lookups with explicit preference guidance.

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?

The description provides explicit when-to-use triggers (e.g., 'which is bigger/better/larger', 'rank these companies') and when-not-to-use (sequential single-pack lookups). It explains the two entity types and their specific data sources, leaving no ambiguity about context.

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

A3.7/5.0
Disambiguation2/5

Multiple tool clusters have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical (the beta is explicitly the same right now); five polymarket_* tools all surface opportunity/edge information; entity_profile, compare_entities, and recent_changes all cover company research. The descriptions are detailed, but an agent can easily misselect between similar tools.

Naming Consistency4/5

All tool names use consistent snake_case and are descriptive, with clear prefix patterns for prediction-market tools (polymarket_*) and the router variants (ask_pipeworx_*). Some names are verb-noun while others are noun-phrases, but the convention is uniformly underscore-separated, with no camelCase or other mixing.

Tool Count2/5

33 tools is excessive for a coherent server, and the scope is sprawled across EMDB access, Pipeworx data routing, prediction markets, memory, subscriptions, and miscellaneous utilities. Even though each tool has a defined role, the sheer breadth and number make it feel like several servers' worth of functionality crammed into one.

Completeness2/5

The server's name suggests it should be focused on EMDB, but only two tools (get_map, search_maps) cover that domain — no browsing, filtering, or extended metadata beyond basic fields. Meanwhile, the bulk of the surface is devoted to unrelated Pipeworx/platform features. For the stated purpose, the coverage is severely incomplete.

Resources