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Goproxy

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

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description reveals data sources (SEC EDGAR for companies, FAERS for drugs), off-calendar fiscal year handling, sorting by primary metric, and return format (paired data + citation URIs). High transparency.

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?

Description is dense but every sentence adds value. Front-loaded with example queries. Could be slightly more structured but remains efficient for the information conveyed.

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, different data sources, sorting, citations) and no output schema, the description covers all essential behavioral aspects. Replaces 8-15 lookups context further clarifies efficiency.

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%, baseline 3. The description adds value by clarifying that 'values' for company are tickers/CIKs and for drug are names, and reinforces constraints. This is meaningful beyond schema literal 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 clearly states it performs side-by-side comparison of 2-5 companies or drugs, with example queries. It distinguishes itself from sequential single-pack lookups, making the purpose unmistakable.

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 instructs to 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use guidance. Also implies limits (2-5 entities) and data source 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

B3.1/5.0
Disambiguation2/5

Several tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical entry points, while discover_tools/suggest_questions and entity_profile/compare_entities/recent_changes serve similar discovery/comparison roles. The polymarket_* cluster also has tightly related boundaries that require reading long descriptions to disambiguate, and version-related helper tools (go_mod, module, versions, version_info) add further confusion.

Naming Consistency3/5

Most tools follow a lowercase snake_case verb_noun pattern (list_subscriptions, resolve_entity, validate_claim), but there are notable deviations: bare verbs like remember/recall/forget, noun-only names like go_mod, module, versions, and version_info, and brand-prefixed names like ask_pipeworx and pipeworx_feedback. The naming is readable and generally predictable, yet the mix of conventions keeps it from being highly consistent.

Tool Count2/5

With 35 tools, the surface is heavy for what is ultimately a data-access and research server. Many tools are conveniences or meta-wrappers that could be consolidated (e.g., three ask_pipeworx variants, multiple polymarket scanners, several onboarding/discovery tools). While the breadth is intentional, 35 feels bloated rather than well-scoped.

Completeness4/5

The toolset covers a remarkably broad domain: universal data lookup, grounded fact-checking, entity resolution, company profiles, comparisons, prediction-market analysis, memory, subscriptions, and feedback. Minor gaps exist—subscriptions can be created/cancelled but not updated/paused, and there is no direct resolver for pipeworx:// citation URIs—but these are workable and core workflows have no dead ends.

Resources