Skip to main content
Glama

Hgnc

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"]).

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context: it specifies the data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), mentions off-calendar fiscal year handling, and notes sorted results. This goes beyond 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 efficient but somewhat verbose. It front-loads example queries and key instructions, earning its length with dense information. Minor redundancy like 'side-by-side comparison' and 'parallel call' is acceptable.

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, sorting, citation URIs), the description is highly complete. It explains what data is pulled for each type, how results are sorted, and mentions the response includes paired data and citation URIs. No output schema is present, but the description adequately covers return format.

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% with both parameters described. The description adds semantic value by explaining that 'values' expects tickers/CIKs for companies and drug names for drugs, and provides examples (e.g., ['AAPL','MSFT']). This enriches the schema definition.

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 verb 'compare' and the resource 'entities' (companies or drugs). It provides concrete natural language examples and distinguishes itself as a side-by-side comparison tool, differentiating it from sibling tools like entity_profile.

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?

The description explicitly states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' giving clear when-to-use guidance. It details the two entity types (company/drug) and their respective use cases, but lacks explicit when-not scenarios.

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

All Hgnc toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation3/5

Tools are mostly distinct but several ask_pipeworx variants and research tools overlap in purpose, which could lead to agent confusion. The presence of memory and subscription tools adds unrelated functionality.

Naming Consistency2/5

Naming is inconsistent, mixing snake_case with varying verb patterns (ask, get, search, scan, etc.) and no clear convention. Some tools have descriptive phrases (e.g., generate_llms_txt) further breaking consistency.

Tool Count2/5

34 tools is excessive for a coherent server, covering too many disparate domains (genes, data queries, betting, memory) without clear focus. A gene server should have far fewer tools.

Completeness3/5

Gene-related tools are complete for basic queries (search, get, resolve), but the server's main purpose (HGNC) is overshadowed by many unrelated Pipeworx tools, creating a mismatch. The overall surface is broad but lacks domain focus.

Resources