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Glama

Protein Atlas

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?

Beyond annotations (readOnlyHint=true, etc.), the description adds context: pulls latest 10-K from SEC, handles off-calendar fiscal years, returns sorted data by primary metric, includes citation URIs. No contradiction 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (~7 sentences) with front-loaded examples. Every sentence adds value, no 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?

Given the simple schema (2 params, no output schema), the description covers purpose, usage, data sources, sorting, and citations. It is fully sufficient for an agent to use the tool correctly.

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%, so baseline 3. The description adds meaning by explaining what data is pulled for each type ('company' → revenue/net income/cash/debt; 'drug' → FAERS/FDA/trials) and specifies input formats (tickers/CIKs for company, names for drug).

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 explicitly states 'side-by-side comparison of 2–5 companies or drugs' and uses verbs like 'compare' and 'rank'. It distinguishes from siblings by noting it replaces 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 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and provides example queries. This gives clear guidance on when to use this tool.

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

All Protein Atlas toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all handle routed research queries, while ai_visibility_check and scan_competitor_ai_presence overlap directly and the six Polymarket tools form a dense, easily confused cluster. The descriptions are detailed, but an agent will frequently struggle to pick the right tool among near-duplicate research and prediction-market options.

Naming Consistency3/5

Names are readable and mostly snake_case, with useful prefixes like ask_pipeworx_ and polymarket_. However, conventions are mixed: some are verb_noun (search_genes, get_protein, generate_llms_txt), some are bare verbs (remember, recall, forget), and some are noun phrases (entity_profile, recent_changes, top_tissues). There is no single predictable pattern.

Tool Count2/5

34 tools is above the 25+ threshold for a heavy, hard-to-navigate set, and most of them are not related to the server's stated 'Protein Atlas' identity. Only three tools actually concern proteins, while the rest form a general data-research, Polymarket, memory, and subscription toolkit that feels like several servers merged into one.

Completeness2/5

For a Protein Atlas server, the surface is severely incomplete: only search_genes, get_protein, and top_tissues cover HPA, leaving pathology, cell-line, single-cell, blood, and other major HPA dimensions unaddressed. If the intended domain is instead the broader Pipeworx data router, the protein tools are an odd vestige and the completeness story is still muddled by overlapping meta-tools.

Resources