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superhero

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

A4.6/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds substantial behavioral context: fan-out to SEC/GDELT/GNews/USPTO, GDELT-to-GNews fallback on rate limits/5xx, USPTO soft-fail due to PatentsView sunset, and the returned structure (changes[] grouped by source, total_changes, citation URIs).

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 query examples and packs a lot of necessary detail into a compact paragraph: sources, fallback behavior, date formats, output summary, and alternative tool. The list of six paraphrases is slightly repetitive, but every major clause contributes context, so the length is justified.

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?

For a multi-source tool with no output schema, the description provides sufficient operational detail: exact sources, fallback logic, date semantics, return value summary, and an explicit comparison to entity_profile. An agent can confidently invoke the tool and correctly interpret its result.

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?

The input schema already covers all three parameters with clear descriptions (type enum, since formats, ticker/CIK formats). The description reinforces the meaning of `since` and connects it to filing windows, but it does not add substantial meaning beyond the schema. Baseline 3 is appropriate given 100% schema 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 opens with concrete user intents ('What's new with X', 'latest on Y') and defines the tool as a 'change feed for a company in the last N days/weeks/months' with specific data sources (SEC, GDELT/GNews, USPTO). It clearly distinguishes itself from the sibling entity_profile by contrasting the dynamic feed with a static profile.

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 states when to use the tool via example queries and provides an explicit alternative: 'Use entity_profile instead when you want the static profile...'. It also adds practical guidance like choosing '30d' or '1m' for typical monitoring, and mentions the parallel single-call efficiency.

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

B3.3/5.0
Disambiguation1/5

The tool set is dominated by a huge number of unrelated tools for data lookup (Pipeworx, Polymarket, etc.), with only 4 superhero-specific tools. Many tools serve overlapping purposes (e.g., ask_pipeworx, deep_research, suggest_questions all handle general queries), making it very difficult for an agent to distinguish the right tool.

Naming Consistency2/5

Naming conventions are highly inconsistent: some tools use CamelCase (ask_pipeworx, discover_tools), others use snake_case (get_hero, list_all, compare_entities), and some use long descriptive phrases (polymarket_arbitrage, scan_competitor_ai_presence). This mixture makes it hard to predict tool names.

Tool Count1/5

With 34 tools, the count is far too large for a server named 'superhero'. Most tools are unrelated to superheroes, making the set seem bloated and misaligned with the server's stated purpose. A focused superhero server would need at most 10 tools.

Completeness1/5

For the superhero domain, the tool set is severely incomplete: only basic retrieval of heroes and powerstats, with no search, filtering, creation, comparison, or battle mechanisms. For the broader data access domain it is more complete, but that is not the server's name.

Resources