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Confluence

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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds specific data sources (SEC EDGAR/XBRL for companies, FAERS for drugs) and explains sorting by primary metric. 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.

Conciseness4/5

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

The description is front-loaded with example triggers and structured by entity type. Every sentence contributes unique information, though slightly long. Efficient for the complexity.

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 explains return data (paired data + citation URIs) and behavior per type. With 2 well-documented parameters, this is complete for an AI agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%. The description adds examples of parameter values (tickers/CIKs for company, names for drug) and max count (5), but this is largely redundant with the schema. The added context is helpful but baseline is 3.

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 explicit verb phrases like 'compare', 'vs', 'rank'. It distinguishes from sibling tools like entity_profile by stating 'ALWAYS PREFER over 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?

The description provides clear when-to-use guidance ('ALWAYS PREFER over sequential lookups') and includes natural language triggers. It doesn't explicitly exclude alternatives but implies this is best for comparisons.

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

B3.4/5.0
Disambiguation2/5

The set is dominated by near-overlapping research tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions with heavily overlapping descriptions, and the six polymarket_* tools plus bet_research form a second confused cluster. The five confluence_* tools are distinct, but an agent would struggle to pick among the research/betting alternatives without reading thousands of words of caveats.

Naming Consistency2/5

Most names are snake_case, but the conventions are mixed: verb_noun (confluence_create_page, validate_claim), noun_verb (bet_research), prefixed nouns (polymarket_arbitrage, pipeworx_feedback), and bare verbs (recall, forget). The glaring issue is that the server is named Confluence yet only 5 of 36 tools carry the confluence_ prefix, leaving the other 31 tools with no thematic prefix and no consistent pattern.

Tool Count2/5

36 tools is already in the 'too many' range, but the mismatch is deeper: only 5 tools relate to Confluence while 31 tools cover an entirely different domain (Pipeworx data research, prediction markets, subscriptions). For a wiki server this is wildly over-scoped; as a combined surface it is bloated and lacks a unifying purpose.

Completeness2/5

For the Confluence domain the surface is incomplete: pages can be created, fetched, listed, and searched, but there is no update_page, delete_page, comment, attachment, or content-type coverage, leaving obvious CRUD dead ends. For the Pipeworx domain, coverage is broad but disorganized, with overlapping research paths and no clear hierarchy.

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