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Hilma

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").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations declare readOnly, idempotent, non-destructive. Description adds: parallel call, fan-out to multiple sources, fallback logic, relative date parsing, and soft-fail for USPTO. 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?

Well-structured: starts with purpose via example queries, then details sources, accepted formats, return structure, and ends with alternative tool. Every sentence adds value, no fluff.

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 complexity (multi-source, fallback, multiple date formats), description covers all aspects: sources, error handling, input formats, output structure. No output schema, but description sufficiently states return type (changes[] grouped by source, total count, URIs).

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?

All three parameters have schema descriptions (100% coverage). Description adds: 'type' only supports 'company', 'since' examples and relative formats ('7d', '30d', '3m', '1y'), 'value' accepts ticker or CIK. Adds meaningful usage context.

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 retrieves 'what's new' for a company, listing sources (SEC, GDELT/GNews, USPTO) and return format. It distinguishes from sibling entity_profile by contrasting change feed vs 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?

Explicit guidance: 'Use entity_profile instead when you want the static profile'. Also explains fallback behavior (GDELT→GNews) and soft-fail for USPTO. Recommends typical 'since' values ('30d' or '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

A3.9/5.0
Disambiguation3/5

Several natural-language query tools overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same underlying Pipeworx catalog. The descriptions do clarify the differences eventually, but the boundaries are subtle enough that an agent could easily pick the wrong one, and discover_tools/suggest_questions also serve a similar onboarding role.

Naming Consistency3/5

Almost all names are snake_case and readable, but they mix verb-first names (ask_, compare_, discover_, validate_) with noun-first names (entity_profile, recent_changes, polymarket_arbitrage, pipeworx_trending). The domain prefixes like hilma_, polymarket_, and pipeworx_ help navigation, but there is no single predictable verb_noun convention across the set.

Tool Count2/5

At 34 tools, the surface is too large for the amount of genuine functional diversity. Several tools are near-duplicates (ask_pipeworx_beta vs ask_pipeworx, ai_visibility_check vs scan_competitor_ai_presence, suggest_questions vs discover_tools), and the set would be noticeably tighter around 20-25 tools without losing coverage.

Completeness4/5

The major workflow clusters are well covered: research/query modes, entity resolution and comparison, prediction-market analysis, memory, and subscription lifecycle management all have their key operations present. Minor gaps exist — such as Hilma notices returning only index metadata rather than full notice text — but there are no critical dead ends for the server's apparent multi-domain research purpose.

Resources