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Glama

Okx

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 read-only and idempotent annotations, the description reveals data sources (SEC EDGAR/XBRL, FAERS/FDA), handling of off-calendar fiscal years, sorting by primary metric, and citation URI output. This is substantial behavioral context.

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?

Dense and front-loaded with natural-language triggers, covering all key aspects in one paragraph. Minor redundancy in example phrasings, but overall efficient and well-organized.

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?

With no output schema, description specifies return shape (paired data + citation URIs) and ordering. It covers both entity types with clear data sources, making the tool fully understandable for an agent.

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 covers both params, but description adds crucial semantics: what type='company' vs 'drug' actually return, acceptable value formats (tickers/CIKs vs drug names), and how values array is used. This exceeds schema 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?

Description clearly states it does 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call' and gives trigger phrases. It distinguishes itself from sequential single-pack lookups, making its purpose unambiguous.

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?

Explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' giving a clear when-to-use rule and naming the alternative pattern. It also differentiates company vs drug usage.

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

B3.2/5.0
Disambiguation2/5

The tool set mixes OKX exchange tools with a large set of Pipeworx data query tools and prediction market tools. Many tools overlap in purpose, e.g., ask_pipeworx, deep_research, and ask_pipeworx_grounded all answer questions but with different modes. OKX tools like ticker and tickers are clear but the overall set is confusing.

Naming Consistency2/5

Naming is inconsistent: OKX tools use single nouns or underscores (ticker, order_book), Pipeworx tools use verb phrases (ask_pipeworx, validate_claim), and prediction market tools use prefixed names (polymarket_arbitrage, bet_research). No consistent pattern.

Tool Count2/5

43 tools is excessive for a coherent server. The scope is unclear—combining exchange, data lookup, and prediction market tools into one server results in a cluttered surface.

Completeness2/5

The server tries to cover too many domains. OKX coverage is decent, but the inclusion of many unrelated tools (e.g., generate_llms_txt, scan_dependency) makes the set feel incomplete for any single purpose. Gaps exist in each sub-domain due to the broad scope.

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