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Mast Nasa

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

A4.7/5.0
Behavior5/5

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

Despite having readOnlyHint and idempotentHint annotations, the description adds valuable behavioral context: it details fan-out to multiple sources, fallback logic (GDELT→GNews), soft-failure for patents, and accepted date formats. It also discloses the return structure, going beyond the 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 dense but every sentence earns its place: examples front-load purpose, technical details explain sources and fallbacks, and the alternative tool is clearly pointed out. It is well-structured and avoids 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?

There is no output schema, so the description appropriately explains the return format (changes[], total_changes, citation URIs). It also covers source behaviors, edge cases (patent soft-fail), and provides a comparison to an alternative tool, making it complete for a complex operation.

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%, and the description largely repeats parameter details already in the schema (e.g., since accepts ISO or relative shorthand, value is ticker/CIK). The description provides examples but adds minimal new parameter-level meaning 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?

The description clearly states the tool's function with a specific verb and resource: "change feed for a company in the last N days/weeks/months." It also differentiates from sibling tools by explicitly recommending entity_profile for static profiles, making the purpose unambiguous.

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 provides explicit usage context with example questions like "What's new with X" and "latest on Y." It also names an alternative tool (entity_profile) and defines when to use it, giving clear when/when-not guidance.

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

A3.5/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap among ask_pipeworx, ask_pipeworx_grounded, and deep_research, all of which query the Pipeworx database with different levels of structure. The detailed descriptions help differentiate them, but the overlap is notable.

Naming Consistency4/5

All tool names use snake_case consistently, which is good. However, the naming conventions vary: some are descriptive phrases (e.g., ai_visibility_check), others are verb_noun (e.g., list_subscriptions), and some are compound nouns (e.g., entity_profile). Lack of a single pattern reduces consistency slightly.

Tool Count3/5

34 tools is on the higher side for a single server, but it may be justified given the broad scope of Pipeworx data sources. However, the server name 'Mast Nasa' implies a focus on astronomy, yet only a few tools relate to that domain, making the count feel inflated and unfocused.

Completeness3/5

The tool set is comprehensive for the Pipeworx data platform, covering querying, grounding, entity resolution, comparison, subscriptions, and more. However, for the implied NASA/Mast domain, the surface is severely incomplete with only four astronomy-specific tools, leaving obvious gaps.

Resources