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Glama

Lobsters

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations declare readOnly, idempotent, and openWorld. The description adds substantial context: data sources (SEC EDGAR for companies, FAERS for drugs), handling of off-calendar fiscal years, result sorting by primary metric, return of citation URIs, and efficiency claim. No contradictions, and it significantly aids understanding of tool behavior.

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 a single, front-loaded paragraph with example queries. Every sentence adds distinct value: triggers, data sources, behavior, return format, and efficiency. No redundancy or fluff, making it highly concise and well-structured.

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?

Despite no output schema, the description explains return structure (paired data + citation URIs), sorting, and data sources for each entity type. It covers all key aspects needed for usage, including the number of entities and data freshness. Given tool complexity, it is sufficiently complete.

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 covers both parameters (type with enum, values with min/max items and descriptions). The description adds meaning: for type, it explains what data each enum value retrieves; for values, it clarifies format (tickers/CIKs for company, drug names for drug) and gives examples. This adds value beyond the schema, though the schema already provides basic descriptions.

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 explicitly states the tool performs side-by-side comparisons of 2-5 entities, provides natural language triggers ('Compare X and Y', 'X vs Y'), and distinguishes itself from sequential single-pack lookups, covering the specific verb+resource and differentiation from siblings.

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?

It explicitly states 'ALWAYS PREFER over sequential single-pack lookups' and gives example queries for comparing or ranking. It implies when to use (comparison) and when not (single entity), and identifies the alternative (sequential lookups), providing clear 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

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., three tools for querying Pipeworx data (ask_pipeworx, ask_pipeworx_grounded, deep_research), and six tools for Polymarket prediction markets. The boundaries between them are unclear, causing potential for misselection.

Naming Consistency3/5

Tool names are all snake_case but follow inconsistent patterns: some use verb_noun (get_hottest, get_newest), some are single verbs (remember, forget), and others are noun_noun (bet_research, polymarket_arbitrage). While readable, the lack of a consistent pattern adds confusion.

Tool Count2/5

With 34 tools, the server is overloaded, especially given its name 'Lobsters' suggests a focus on that site, yet only 4 tools are Lobsters-related. The majority belong to Pipeworx, Polymarket, and generic utilities, making the scope extremely broad and unfocused.

Completeness2/5

For the implied domain of Lobsters, the tool set is incomplete (missing create/update/delete). For the broader domains (Pipeworx, Polymarket), the set is extensive but doesn't align with the server's name. The lack of a coherent domain leaves obvious gaps relative to any single purpose.

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