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Glama

Phishtank

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

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses parallel fan-out to multiple sources, the GDELT→GNews fallback on rate-limit/5xx, and the USPTO soft-fail due to API sunset. It also describes the return format, giving the agent a clear picture of performance and failure characteristics.

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 dense but well-structured, front-loaded with the core purpose, then sources, parameter details, return structure, and alternative tool. Every sentence provides necessary information with no fluff, making it an exemplar of concise yet comprehensive documentation.

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?

With no output schema, the description compensates by outlining the return structure (changes[], total_changes, citation URIs). It covers all necessary aspects: sources, fallbacks, parameter formats, and usage boundaries, making it fully complete for an agent to invoke correctly.

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%, so baseline is 3, but the description adds meaningful value by giving concrete examples for `since` ('2026-04-01', '7d', '30d', '3m', '1y') and a recommendation for typical monitoring. This helps the agent pick appropriate values beyond the schema's generic description.

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 as a change feed for a company over a time window, explicitly naming the data sources (SEC EDGAR, GDELT/GNews, USPTO) and listing the return structure. It distinguishes itself from sibling entity_profile by stating that entity_profile is for static profiles, making the purpose unmistakable.

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 explicitly tells when to use this tool (for 'what's new' queries) and when not to, directing to entity_profile for static profiles. It also provides practical parameter guidance ('Use 30d or 1m for typical monitoring'), leaving no ambiguity about selection.

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

All Phishtank toolsInspect
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TDQS

B3.4/5.0
Disambiguation1/5

The server is named 'Phishtank' but only one tool (check_url) relates to phishing. The remaining 31 tools cover a wide range of unrelated topics (data research, prediction markets, memory, etc.), many with overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research). This makes it extremely difficult for an agent to select the right tool.

Naming Consistency2/5

Tool names mix conventions inconsistently: some use underscores (ai_visibility_check, check_url), some are camelCase (ask_pipeworx, bet_research), and others are compound phrases. There is no predictable pattern across the set.

Tool Count1/5

With 32 tools, the count is high, but only one aligns with the server name 'Phishtank' (check_url). The vast majority belong to an entirely different domain (Pipeworx tools), making the tool count severely inappropriate for the server's stated purpose.

Completeness1/5

For a phishing detection server, the tool surface is severely incomplete. It lacks essential tools like report_phish, verify_phish, get_stats, etc. The single phishing tool (check_url) is insufficient, while the other 31 tools are completely out of scope.

Resources