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Glama

Shodan

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?

Discloses behavior beyond annotations: fans out to multiple sources in one parallel call, mentions soft-fail for USPTO due to sunset, and explains fallback logic. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with example queries, every sentence adds value, and it is efficiently packed with information while remaining readable.

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 the complexity of multiple sources and fallback, the description is complete. It describes the return structure (changes[] grouped by source, total_changes, citation URIs) despite no output schema.

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%, baseline 3. Description adds value by explaining the 'since' format with examples, recommending typical values, and clarifying that 'type' only supports 'company' and 'value' can be ticker or CIK.

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 provides a change feed for a company over a recent window, aggregating from multiple sources. It uses example queries like 'What's new with X' and distinguishes itself from sibling 'entity_profile' which is for static profiles.

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?

Explicitly tells when to use this tool vs alternatives: 'Use entity_profile instead when you want the static profile'. Describes fallback behavior between GDELT and GNews.

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

A3.6/5.0
Disambiguation2/5

Several tool families have unclear boundaries: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, ask_pipeworx_grounded and validate_claim both verify claims against sources, and polymarket_edges/polymarket_arbitrage/bet_research all surface prediction-market opportunities. Agents would need to read very long descriptions to distinguish overlapping intents, and would likely misroute requests.

Naming Consistency3/5

All names use snake_case, which is a consistent base, and subgroups (shodan_host*, ask_pipeworx*, polymarket_*) follow internal patterns. However, conventions mix verb-first (ask_pipeworx, validate_claim, resolve_entity) with noun-first (entity_profile, bet_research, recent_alerts), and parallel functionality is named with inverted ordering (ai_visibility_check vs scan_competitor_ai_presence).

Tool Count2/5

34 tools is above the comfortable range for a single server and the breadth feels bloated, especially with five highly niche Polymarket tools. More critically, the server is named 'Shodan' but only 3 of 34 tools are Shodan-related, so the count is badly mismatched with the stated identity.

Completeness2/5

For a server claiming to be Shodan, the surface is severely incomplete — only host lookup, search, and count are present, with no DNS, alerting, or other standard Shodan operations. For the actual Pipeworx/Polymarket domain implied by most tools, coverage is fuller but still has gaps such as no direct tool to fetch a pipeworx:// citation URI, and the memory/subscription features feel bolted on rather than integral.

Resources