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Crystallography

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 provide readOnlyHint, idempotentHint, etc. The description adds rich behavioral detail: fans out to SEC EDGAR, GDELT→GNews fallback (with conditions), USPTO patents (with known sunset). Also describes return structure (changes[], total_changes, citation URIs). 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.

Conciseness5/5

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

Front-loaded with examples and purpose; every sentence provides useful information. No fluff or repetition. Efficiently packs a lot of detail into a compact description.

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 no output schema, the description explains return format, data sources, fallback logic, and limitations. For a 3-param tool with moderate complexity, this is fully complete and leaves no major questions.

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 already covers all 3 parameters with descriptions (100% coverage). Description adds examples for `since` (ISO vs relative shorthand, recommended '30d'), clarifies `value` can be ticker or CIK, and gives typical monitoring recommendation. This adds value beyond 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 starts with example queries ('What's new with X') and defines the tool as a change feed for a company in a time window. It clearly distinguishes from sibling 'entity_profile' which provides static profile, so the agent knows when to use this tool 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?

Explicitly lists when to use via example queries and directly states: 'Use entity_profile instead when you want the static profile regardless of window.' This provides both use cases and alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Crystallography toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

There are three overlapping ask_pipeworx variants (stable, beta, grounded) plus a dense cluster of six polymarket trading/arbitrage tools, making misselection likely. Several other tools also blur together around research aggregation and entity lookup (entity_profile, compare_entities, recent_changes, validate_claim).

Naming Consistency3/5

All tool names are lowercase and snake_case, but the pattern is inconsistent: some are verb_noun (get_structure, resolve_entity), some are noun phrases (recent_changes, polymarket_edges), and a few are bare verbs (remember, forget, recall). The ask_pipeworx_beta/ask_pipeworx_grounded suffix pattern is readable but not mirrored across the rest of the set.

Tool Count2/5

34 tools is above the 25+ threshold for a server whose stated purpose is Crystallography, and only 3 of those tools actually serve that domain. The rest belong to Pipeworx data lookup, Polymarket betting, memory, research, subscriptions, and unrelated utilities, so the count feels excessive and the scope is unclear.

Completeness3/5

As a broad data-research toolset, there is decent lifecycle coverage: lookup, research, grounded verification, entity resolution, comparison, subscriptions, feedback, and memory all exist. However, for a server named Crystallography the domain surface is thin (search/get/get CIF only), and there is no general web-search fallback for topics not in the structured catalog.

Resources