Skip to main content
Glama

Bls

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

A4.7/5.0
Behavior5/5

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

Beyond the readOnlyHint, it details data sources (SEC EDGAR, FAERS), handling of off-calendar fiscal years, sorting by primary metric, and return of paired data with citation URIs. This significantly aids the agent's understanding.

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 thorough but slightly lengthy; it front-loads example queries and prioritizes key info. Could be tightened marginally but remains efficient.

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 moderate complexity (2 params, no output schema), the description covers all necessary aspects: usage, data sources, sorting, return format, and edge cases, making it highly complete.

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?

The description adds rich context to both parameters: 'type' enum explained with data pulled, 'values' with examples and constraints. With 100% schema coverage, it still adds meaningful interpretation.

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 comparisons of 2-5 companies or drugs, with explicit verb phrases like 'Compare X and Y' and 'rank these companies'. It distinguishes itself from sequential single-pack lookups.

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?

It explicitly says 'ALWAYS PREFER over sequential single-pack lookups' and provides example queries. It lacks explicit 'when not to use' but strongly implies context.

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

All Bls 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

Most tools understandably fall into distinct clusters (BLS data, Polymarket, entity research, memory, subscriptions) and have detailed descriptions, but there is real overlap among the query entry points: ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx, deep_research, and validate_claim can all answer similar factual questions. The descriptions help an agent choose, but the set still contains more than a couple of near-duplicate paths.

Naming Consistency3/5

All names are lowercase snake_case and several clusters share domain prefixes like bls_, polymarket_, and pipeworx_, which keeps the surface readable. However, the semantic naming pattern is mixed: verb+noun names like resolve_entity and list_subscriptions coexist with noun phrases like entity_profile, recent_alerts, and bls_latest, plus brand-led names like ask_pipeworx and polymarket_edges.

Tool Count2/5

At 36 tools, the set is well past the 25+ threshold for a heavy tool surface, and several tools inflate the count: duplicate ask_pipeworx variants, multiple overlapping Polymarket scanners, and one-off meta helpers. The broad Pipeworx scope explains some of the breadth, but the redundancy makes the set feel bloated.

Completeness4/5

The set covers its core workflows well: data lookup, grounded verification, entity profiling and comparison, BLS series access, Polymarket research, subscriptions, memory, and feedback. Minor gaps remain, such as no subscription-editing tool, no dedicated citation-reader tool, and no general web-search tool, but ask_pipeworx acts as a catch-all router that lets agents work around most of them.

Resources