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Funcheap

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already mark it as read-only, idempotent, and non-destructive. The description adds behavioral details: fans out to SEC EDGAR, GDELT→GNews fallback (explains when GNews is used), USPTO (notes soft-fail). Describes return structure: changes[] grouped by source, total_changes, citation URIs. No contradictions.

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 comprehensive but not overly long; front-loaded with examples. Could be slightly more concise, but every sentence adds value. Well-organized.

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 complexity (multiple data sources, fallback logic, flexible window parameter) and lack of output schema, the description adequately explains what the tool returns (grouped changes, count, URIs). References sibling tool for alternative use case.

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?

Schema covers all 3 parameters with descriptions. The description significantly enriches: explains since formats (ISO date and relative shorthand with examples), value as ticker or CIK, type as only 'company'. Adds typical usage hints.

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 starts with specific usage examples (e.g., "What's new with X", "latest on Y") and clearly states the tool's purpose: 'change feed for a company in the last N days/weeks/months'. It distinguishes from the sibling tool entity_profile by explicitly stating when to use that instead.

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?

Provides explicit when-to-use guidance via example queries and when-not-to-use with 'Use entity_profile instead when you want the static profile...'. Also includes parameter usage tips like using '30d' for typical monitoring.

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

A4.1/5.0
Disambiguation4/5

Most tools are distinct (event search, subscriptions, memory, Polymarket arbitrage, data lookups), but ai_visibility_check, scan_competitor_ai_presence, bet_research, polymarket_edges, and polymarket_arbitrage have overlapping purposes around competitive research and prediction-market edge-finding. The detailed descriptions disambiguate them, though an agent could confuse polymarket_edges with polymarket_arbitrage.

Naming Consistency4/5

Tool names are mostly descriptive and consistent: search_events, event, categories, tags are aligned; ask_pipeworx, compare_entities, entity_profile follow a similar pattern. However, polymarket_* tools have an odd mix of `polymarket_arbitrage`, `polymarket_fill_risk`, and `polymarket_edge_tracker`, and discover_tools/recent_alerts/recent_changes are consistent, mostly. Naming is quite consistent overall with minor deviations.

Tool Count3/5

At 35 tools, the count is heavier than typical MCP servers, but it reflects a broad service (Funcheap data + Pipeworx data platform + pred markets). Still, plenty of tools serve the same primary purpose (polymarket_, ask_pipeworx variants) so some pruning would improve the surface.

Completeness4/5

The surface appears complete for its domain: search/retrieve events, category/tag navigation, memory, subscriptions (create/list/cancel/pull), data lookups, entity resolution, comparisons, profiles, edge scanning, and feedback. Minor gaps include no direct 'update' on events (but events are static), and no pricing fetch tool separate from event text, though body text covers it.

Resources