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Glama

Usajobs

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

A5/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses detailed behavior: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), specific metrics pulled, sorting by primary metric, and citation URIs. It also notes correct handling of off-calendar fiscal years, which is non-obvious and valuable.

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 appropriately sized for the tool's complexity. It front-loads example user queries for quick recognition, then provides structured details on data sources and behavior. Every sentence adds value, with no filler or redundancy.

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 fully accounts for the tool's interface: it explains what data is returned, how results are sorted, and that citations are provided per entity. It also gives context on when to use this tool relative to alternatives, making it complete for an agent to select and invoke correctly.

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 schema already covers both parameters well (100% coverage with enums and examples), and the description enriches them further by explaining what each type pulls and how values should be formatted (e.g., tickers vs drug names). This adds significant meaning beyond the schema.

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 identifies the tool's purpose: side-by-side comparison of 2–5 companies or drugs in a single parallel call. It uses specific verbs like 'compare' and lists concrete example queries, and it distinguishes itself from single-lookup alternatives by stating it replaces 8–15 sequential lookups.

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?

The description explicitly says to prefer this tool over sequential single-pack lookups when comparing entities, and provides concrete trigger phrases. It also specifies when to use type='company' vs type='drug', giving clear guidance on when this tool is appropriate.

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

Multiple tools overlap significantly: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all handle factual queries, and the Polymarket suite (polymarket_arbitrage, polymarket_edges, bet_research) has fuzzy boundaries. Even with long descriptions, agents will struggle to choose correctly among these clusters.

Naming Consistency3/5

Naming is snake_case but inconsistent in style: some tools are verb-led (get_job, list_agencies, validate_claim), others are noun-led (polymarket_edges, ai_visibility_check, recent_changes). The pattern is predictable only in that everything is snake_case, but the verb/noun order varies.

Tool Count2/5

36 tools is excessive for a server nominally called 'Usajobs'; the bulk of tools (Pipeworx, Polymarket, memory) are unrelated to the server's apparent purpose. The count feels like a bundled platform rather than a focused tool set.

Completeness4/5

For the USAJOBS subset, coverage is solid: search, get_job, and reference lists for agencies/occupational series/pay grades cover the read-only domain. However, the broader tool surface is sprawling and lacks a clear organizational principle, making completeness hard to assess; major data lookup features are present but via meta-tools rather than dedicated ones.

Resources