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Glama

Hibp

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. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, etc.), the description discloses multi-source fan-out, fallback from GDELT to GNews, soft-failure for USPTO due to API sunset, and the return structure with citations. This adds substantial behavioral context.

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 use examples and structured logically, but it is somewhat lengthy. Every sentence contributes meaning, though minor trimming could improve conciseness.

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?

Despite lacking an output schema, the description sufficiently explains return format (grouped changes, total count, citations) and sources. It covers all key aspects for a multi-source aggregation tool.

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 coverage is 100%, but the description adds value by explaining the since parameter formats (ISO date vs relative shorthand) and recommending typical values, and clarifying value accepts ticker or CIK. It reinforces the enum for type.

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 defines the tool's purpose: fetching recent changes (filings, news, patents) for a company within a time window. It uses specific verbs like 'change feed' and distinguishes itself from the sibling tool entity_profile by explicitly stating when to use that alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides strong guidance with example queries and explicitly directs users to entity_profile for static profiles. It explains the parallel call nature but lacks explicit 'when not to use' or restrictions beyond the alternative suggestion.

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

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, and the long, detailed descriptions make it easy for an agent to pick the right one. However, there is some functional overlap between research-oriented tools (e.g., ask_pipeworx vs deep_research vs bet_research) and between visibility-checking tools (ai_visibility_check vs scan_competitor_ai_presence), which could potentially confuse an agent without careful reading.

Naming Consistency4/5

Tool names are uniformly lowercase with underscores, and related tools share consistent prefixes (e.g., polymarket_*, ask_pipeworx, check_*). While most follow a verb_noun pattern, some are noun-first (entity_profile, polymarket_arbitrage) or compound (scan_competitor_ai_presence), creating minor inconsistency, but overall the naming is readable and not chaotic.

Tool Count4/5

With 37 tools, the count is on the higher end but appropriate for a broad data/research platform that covers HIBP breaches, multiple data-pull/research modes, Polymarket analytics, memory, subscriptions, and meta-tools. The number is justified by the diversity of capabilities, though it exceeds the typical 3-15 range for a narrowly-scoped server.

Completeness5/5

The tool set appears thorough for its intended scope: HIBP breach lookup is fully covered (check_account, check_password, get_breach, list_breaches, list_data_classes), research and entity resolution are extensive (entity_profile, compare_entities, deep_research, validate_claim, etc.), Polymarket has dedicated arbitrage/edge/fill-risk tools, and memory/subscription/meta capabilities are present. No obvious gaps for the stated functionality.

Resources