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Glama

Serper

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?

Goes beyond annotations by describing multi-source fan-out, fallback logic, soft-fail for USPTO, and return structure. Annotations already cover safety, and description adds operational details.

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?

Dense but well-organized: starts with examples, states core function, details sources and params, ends with alternative. Every sentence earns its place.

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, description clearly states return format (changes[] grouped, total_changes, citation URIs). Covers all sources, fallback, and error scenarios.

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 'since' formats (ISO vs relative), 'value' as ticker or CIK, and 'type' limitation 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 defines the tool as a 'change feed for a company' covering SEC, news, and patents. It distinguishes itself from 'entity_profile' (static profile) by contrasting use cases.

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 when-to-use: for 'what's new' queries. Directly recommends 'entity_profile' for static needs. Also details fallback behavior (GDELT→GNews) and parameter handling.

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

The ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) has blurry boundaries — ask_pipeworx_beta is explicitly identical to ask_pipeworx today — and the six Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, etc.) all operate in the opportunity-detection space. Extremely detailed descriptions help, but an agent could easily select the wrong variant.

Naming Consistency3/5

Snake_case is used throughout and the polymarket_* and pipeworx_* clusters are internally consistent, but the set mixes verb-first names (ask_pipeworx, resolve_entity, validate_claim) with noun-first names (entity_profile, recent_changes, news, places) roughly evenly. The 'beta' suffix on a stable production tool and the adjective-noun 'deep_research' add further inconsistency.

Tool Count2/5

At 34 tools this exceeds the 25+ threshold, and the count is padded with redundancy: three near-identical ask_pipeworx variants, ai_visibility_check wrapped by scan_competitor_ai_presence, and six overlapping Polymarket tools. The unusually broad multi-domain scope justifies more tools than a typical server, but several clusters could be consolidated.

Completeness4/5

The surface is thorough for a read-only data-access gateway: universal routing, grounded verification, entity resolution/profiles/comparisons, web/news/maps search, prediction-market analysis, memory CRUD, and a full subscription lifecycle. Minor gaps exist (no direct fetch tool for pipeworx:// citation URIs, no image/video Serper endpoints) but agents can work around them.

Resources