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Htmltext

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

A4.5/5.0
Behavior5/5

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

Beyond annotations (readOnly, openWorld, idempotent), the description details data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, sorted results, and 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 front-loaded with example queries and structured into clear sentences about data sources and behavior. It is slightly verbose but every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description explains return format (paired data + citation URIs) and sorting. It covers key use cases and data sources. Could specify handling of missing entities, but acceptable.

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%, but description adds value by giving concrete examples (tickers/CIKs for company, drug names) and explaining behavior based on type. It clarifies max/min items and use of primary metric sorting.

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 performs side-by-side comparison of 2-5 companies or drugs, with specific example queries. It distinguishes itself from siblings like entity_profile by focusing on multiple entities comparison.

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 says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing strong when-to-use guidance. It does not explicitly mention when not to use or alternatives, but the context is clear.

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

A3.8/5.0
Disambiguation2/5

Several tools have overlapping or nested roles: ask_pipeworx_beta is explicitly identical to ask_pipeworx currently, ask_pipeworx_grounded uses the same router, and polymarket_arbitrage/polymarket_edges both surface mispricings. ai_visibility_check and scan_competitor_ai_presence are also tightly coupled, making tool selection error-prone.

Naming Consistency3/5

Names are uniformly snake_case and mostly readable, but the pattern is mixed: verb-first names like extract_links and discover_tools coexist with noun-first product names like polymarket_edges and entity_profile, plus bare verbs like remember and forget. This breaks the predictable verb_noun convention.

Tool Count2/5

34 tools is far too many for a server named Htmltext, and most tools are unrelated to HTML processing. Even as a broad data-research server, the count exceeds the usual 3-15 sweet spot and includes meta-tools, near-duplicate query modes, and niche utilities that bloat the surface.

Completeness3/5

The set is unusually broad—covering data research, prediction markets, memory, subscriptions, HTML extraction, AI visibility, and package scanning—but no single domain is fully fleshed out. HTML tools only do extraction, prediction-market tools lack a simple market browser, and there is no general web fetch tool. Most gaps can be worked around via ask_pipeworx, but the surface feels like a grab bag rather than a cohesive lifecycle.

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