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Imei

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?

Although annotations declare readOnly, idempotent, etc., description adds crucial behavior: parallel call, fan-out to three sources, error handling (GNews fallback, patent soft-fail), and return format (grouped changes, URIs). 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?

Description is long but justified by complexity. Front-loaded with query examples, but some details (e.g., patent API sunset) could be compressed. No wasted sentences.

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?

Without output schema, description adequately explains return: structured changes grouped by source, total_changes count, pipeworx:// citation URIs. Covers all necessary aspects for correct agent invocation.

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 has 100% coverage, but description enriches each parameter: 'since' accepts ISO dates and relative shorthand with examples; 'value' can be ticker or CIK; 'type' only 'company' today. Provides guidance like 'Use "30d" for typical monitoring'.

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?

Clear verb (change feed), concrete resource (company), and actionable query examples: "What's new with X". Explicitly distinguishes from sibling 'entity_profile' by stating use case difference.

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 patterns and when-not-to (static profile via entity_profile). Details fallback behavior (GDELT→GNews), soft-fail for patents, and typical 'since' values ('30d', '1m').

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

A3.8/5.0
Disambiguation2/5

Several tools have unclear boundaries: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, and deep_research, validate_claim, discover_tools, and suggest_questions all overlap with the ask_pipeworx family. The prediction-market cluster also has six tools whose distinctions require careful reading, making mis-selection likely.

Naming Consistency3/5

Most names are lowercase snake_case and reasonably descriptive, but no consistent verb_noun pattern holds across the set. entity_profile, recent_alerts, and pipeworx_trending are noun phrases, while compare_entities, resolve_entity, and validate_imei are verbs, and the useful ask_pipeworx_* and polymarket_* prefixes are not applied server-wide.

Tool Count2/5

33 tools is too many for an agent to navigate efficiently, especially since the underlying data surface is already hidden behind ask_pipeworx and dozens more tools. The set spans data research, prediction markets, memory, subscriptions, IMEI validation, dependency scanning, and llms.txt generation, making it feel like a grab bag rather than a focused server.

Completeness3/5

The main query/verify/research/monitor workflow is covered well: ask, grounded, deep research, claim validation, entity profiles, comparisons, subscriptions, and memory all exist, so common paths have few dead ends. However, the set is not a single coherent domain, and there is no direct fetch/read-record tool or prediction-market execution tool, leaving some reasonable follow-up actions implicit.

Resources