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Glama

Unicef

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?

Discloses behavioral traits beyond annotations: pulls latest 10-K data for companies, handles off-calendar fiscal years, sorts results by primary metric, uses SEC EDGAR/XBRL and FAERS, returns citation URIs. Annotations already declare readOnlyHint and idempotentHint, which the description supports with no contradiction.

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 slightly verbose but well-structured with examples, clarifications, and behavioral notes. Every sentence adds essential information, making it mostly concise for the level of detail needed.

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 explains return format (paired data + citation URIs), covers both entity types, and addresses edge cases (off-calendar fiscal years). All relevant context for using the tool is provided.

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?

Adds meaning beyond schema: for type, it specifies what data each type pulls (revenue, net income, etc. for companies; adverse-event counts for drugs). For values, it gives examples (tickers for company, drug names). This enriches the schema descriptions which were already present (100% coverage).

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 does side-by-side comparison of 2-5 companies or drugs, with example phrases like 'compare X and Y' and 'rank these companies'. It distinguishes from sibling tools like entity_profile which do single-entity lookups, emphasizing 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?

Explicitly instructs to 'ALWAYS PREFER over sequential single-pack lookups when comparing entities'. Provides clear context that it is for comparisons and replaces many sequential lookups, giving strong usage guidance.

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

A3.5/5.0
Disambiguation2/5

Several tool families heavily overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve 'find/query Pipeworx data' with blurry boundaries, and the six Polymarket tools (bet_research, polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) have overlapping purposes. An agent could easily pick the wrong one without reading every description.

Naming Consistency3/5

Most tools follow snake_case verb_noun patterns (list_dataflows, get_data, compare_entities, resolve_entity), and families share prefixes (pipeworx_*, polymarket_*, ask_pipeworx_*). However, the server is named 'Unicef' while almost all tool names reference Pipeworx/Polymarket, and verb choices vary widely, so the overall set lacks a unified naming story.

Tool Count1/5

34 tools is already on the high side, but the real problem is scope: only 3 tools (list_dataflows, dataflow_structure, get_data) relate to the server's stated UNICEF purpose, while the other 31 are an unrelated grab bag of Pipeworx research, prediction-market betting, memory utilities, npm scanning, and llms.txt generation. This is a severe mismatch between count and purpose.

Completeness2/5

For the UNICEF domain implied by the server name, the surface is minimal: browse, structure, and fetch data cover read-only access but nothing else, and the overwhelming majority of tools are off-domain. If the inferred domain is instead 'Pipeworx + prediction markets', coverage is broad, but then the server name is fundamentally misleading and the UNICEF subset is an incomplete afterthought.

Resources