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Glama

Threatfox

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 are all safe (readOnly, idempotent, not destructive). The description adds behavioral context: fans out to multiple APIs in one parallel call, has explicit fallback logic (GDELT→GNews), soft-fails for USPTO due to API sunset, and explains the return structure (changes[] grouped by source). 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?

The description is a single paragraph but well-structured: it front-loads with example queries to quickly convey purpose, then explains data aggregation, parameter details, and sibling comparison. Every sentence adds useful information. 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?

Despite no output schema, the description explicitly states the return format: 'structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs.' It also covers error/fallback behavior and provides a clear alternative tool. With high parameter coverage and annotations, nothing essential is missing.

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 coverage is 100%, but the description adds significant value: it explains the `since` parameter syntax in detail (ISO date or relative shorthand like '7d', '30d', '3m', '1y'), recommends typical usage ('30d'), and clarifies that `type` only supports 'company' currently. It also explains `value` accepts ticker or CIK. This goes beyond the schema definitions.

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 clear example queries ('What's new with X', 'latest on Y') and explicitly states it returns a change feed for a company in a time window. It lists multiple data sources (SEC EDGAR, GDELT→GNews, USPTO) and distinguishes from sibling entity_profile, which provides static profiles. The verb 'returns' and resource 'change feed' are specific.

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 examples of when to use the tool ('What's new with X', 'updates on Acme') and when to use the alternative: 'Use entity_profile instead when you want the static profile.' It also explains fallback behavior (GDELT→GNews) and suggests a default `since` value ('Use "30d" or "1m" for typical monitoring').

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
Disambiguation2/5

Many tools have overlapping purposes, such as multiple 'ask_pipeworx' variants, 'deep_research', and various search tools (search_hash, search_ioc, search_malware, search_within). The similarities in descriptions confuse an agent's ability to select the correct tool.

Naming Consistency2/5

Tool names mix conventions: some use underscores (ask_pipeworx, deep_research), some use hyphenated or compound names (generate_llms_txt, pipeworx_feedback), and verbs are inconsistent (search vs. ask vs. validate). No clear pattern.

Tool Count2/5

With 35 tools, the server feels over-scoped, integrating many domains (financials, drugs, prediction markets, threat intel) into a single surface. This leads to redundancy and makes it hard for agents to navigate.

Completeness3/5

The tool set covers a wide range of data lookups and threat intel operations, but there are noticeable gaps: few update/delete/management tools (only subscribe/unsubscribe/forget) and no clear lifecycle for many resource types. Some domains appear incomplete.

Resources