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Ecb

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent. Description adds critical behavioral details: data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, sorting by primary metric, citation URIs, and efficiency gain (replaces 8–15 lookups). 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?

Description is fairly long but each sentence adds value. Front-loaded with trigger phrases and examples. Could be slightly more concise, but structure is logical and effective.

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 2 params, no output schema, and rich annotations, the description fully covers use case, data sources, behavior, return format, and efficiency, leaving no gaps for the AI agent.

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%, baseline 3. Description adds concrete examples for values (tickers/CIKs for company, drug names) and clarifies enum semantics, enhancing understanding 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?

Description explicitly states 'side-by-side comparison of 2–5 companies or drugs' and provides clear examples like 'compare X and Y', 'X vs Y'. It distinguishes from sequential single-entity lookups, making the purpose unmistakable.

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?

Description gives explicit when-to-use trigger phrases ('which is bigger', 'rank these companies') and instructs ALWAYS PREFER over sequential lookups, providing clear context for tool selection.

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

A3.9/5.0
Disambiguation2/5

There is significant overlap between tool clusters: ask_pipeworx_beta is currently functionally identical to ask_pipeworx, and polymarket_arbitrage/polymarket_edges/bet_research all target similar opportunity-discovery tasks. While the descriptions are extremely detailed, the sheer number of similar variants makes it hard to reliably pick the right one.

Naming Consistency4/5

Names are consistently snake_case with meaningful prefixes (ask_, ecb_, polymarket_, pipeworx_) and mostly verb-first structure. Minor deviations like entity_profile and ecb_hicp_inflation are noun-first, but they do not break the overall pattern.

Tool Count2/5

35 tools is well beyond the well-scoped 3–15 range, and the surface feels bloated with redundant meta-tools (ask_pipeworx variants, deep_research, discover_tools, suggest_questions) and peripheral utilities like generate_llms_txt and scan_dependency. Many entries could be consolidated without losing function.

Completeness4/5

For the server's broad data-research domain, coverage is thoughtful and full: query, grounded answer, validation, entity resolution, profiling, comparative analysis, subscription lifecycle, memory, and feedback are all represented. Minor gaps exist (e.g., no direct raw ECB flow browser beyond generic SDMX) but nothing blocking.

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