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Glama

Tavily

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?

The description reveals significant behavioral details beyond the annotations: it fans out to SEC EDGAR, GDELT→GNews (with fallback logic), and USPTO (with a noted API sunset causing soft-fail). This is valuable operational context that annotations do not provide.

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?

Despite its length, the description is dense and well-structured: it opens with query examples, defines the core behavior, explains internal source fan-out and fallbacks, details the `since` parameter, and closes with a sibling contrast. Every sentence contributes necessary information.

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?

The description covers the return format (changes[], total_changes, citation URIs), source behavior, parameter formats, and alternative tool usage. Given the tool's complexity and lack of an output schema, this is a complete and self-sufficient description.

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. The description adds concrete examples for `since` ('7d', '30d', '3m', '1y') and a usage recommendation, which slightly enhances understanding beyond the schema's minimal definition.

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 is a 'change feed for a company' and provides multiple example query forms ('What's new with X', 'updates on Acme') that make its purpose immediately recognizable. It also explicitly contrasts with entity_profile, distinguishing it from sibling tools.

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 gives explicit when-to-use guidance via the query examples and directly names an alternative: 'Use entity_profile instead when you want the static profile... regardless of window.' This covers both when to use and when not to use this tool.

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
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same 5,529 tools, and deep_research overlaps for broad questions. The six prediction-market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also have heavily overlapping purposes, making misselection likely.

Naming Consistency4/5

All names use snake_case, which is consistent, and most are verb-first (search, extract, remember, resolve_entity, validate_claim). However, several are noun-phrases (entity_profile, polymarket_arbitrage, recent_alerts, pipeworx_feedback), breaking the verb_noun pattern. The deviations are minor but present.

Tool Count2/5

At 33 tools, the set is well above the 25-tool threshold for 'too many'. The server also spans several unrelated domains—web search, Pipeworx structured data, prediction markets, memory, subscriptions, AI visibility—making the count feel excessive for a coherent purpose.

Completeness4/5

Each sub-domain is well covered: search has search/extract/search_within, prediction markets have research/arbitrage/edges/fill-risk/tracking, subscriptions have subscribe/unsubscribe/list/recent_alerts, and memory has remember/recall/forget. Minor gaps exist (e.g., no way to edit a subscription's parameters, no direct SEC filing content viewer), but agents can work around them via ask_pipeworx.

Resources