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Glama

Owasp

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 already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds significant context: fans out to three sources, fallback logic, date format parsing, return structure with grouped changes and citation URIs. 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 a single paragraph but efficiently front-loads with user query examples and then details mechanics. Every sentence adds value: examples, sources, fallback, date formats, return format, and cross-reference to sibling. No wasted words.

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?

For a tool with no output schema, the description fully explains the return value: 'structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs'. It also covers complexity of multiple data sources, fallback, and date parsing.

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% with descriptions for each parameter. The description adds meaning: explains 'since' accepts ISO date or relative shorthand (with examples), 'value' can be ticker or CIK, and 'type' is limited to 'company'.

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 it provides a change feed for a company in the last N days/weeks/months, fanning out to SEC EDGAR, GDELT/GNews, and USPTO. It distinguishes from the sibling tool 'entity_profile' by noting when to use that instead.

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?

Provides explicit guidance on when to use this tool versus 'entity_profile', and gives specific query examples like 'What's new with X' and 'latest on Y'. It also describes fallback behavior (GDELT to GNews) and limitations (USPTO soft-fail).

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

Many tools overlap in purpose, especially the Pipeworx data query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and the Polymarket betting tools (bet_research, polymarket_arbitrage, etc.). Descriptions help differentiate, but an agent may still struggle to choose the correct one.

Naming Consistency4/5

Most tool names use snake_case and follow a verb_noun pattern (e.g., ask_pipeworx, resolve_entity, validate_claim). Some deviations exist (e.g., entity_profile, cheat_sheet) but overall the pattern is predictable.

Tool Count2/5

34 tools is excessive for a server named 'Owasp', as only 4 are directly OWASP-related (asvs_chapters, asvs_requirements, cheat_sheet, top10). The remaining 30 tools are for general data querying and betting, making the surface feel bloated and unfocused.

Completeness3/5

The OWASP domain is partially covered with ASVS requirements, cheat sheets, and Top 10 lists. However, notable gaps exist, such as the OWASP Testing Guide, Software Assurance Maturity Model (SAMM), or risk assessment tools. The Pipeworx tools are comprehensive but not relevant to OWASP.

Resources