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Glama

Mbta

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context: it fans out to multiple sources, describes fallback logic, the structure of the return (changes[] grouped by source, total_changes, citation URIs), and potential failures. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is detailed but well-organized, front-loading common query patterns and then systematically covering sources, parameters, and return format. While a bit long, every sentence adds value and there is no redundancy.

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 having no output schema, the description clearly states the return structure (changes[] grouped by source, total_changes, citation URIs). It covers error handling (USPTO soft-fail, GDELT→GNews fallback) and explains all three required parameters. The tool is complex, but the description is fully self-contained.

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 substantial meaning: explains `type` enums, provides examples for `since` (ISO and relative), and specifies `value` as ticker or CIK. It also gives typical usage recommendations like '30d' or '1m'.

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 explicitly states the tool provides a change feed for a company combining SEC filings, news, and patents, and contrasts with entity_profile. It includes concrete example queries that clarify the purpose.

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 example use cases, explains when to use entity_profile instead, details the `since` parameter format, and notes fallback behavior and limitations (USPTO soft-fail, GDELT→GNews). This provides clear guidance on appropriate usage.

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

ask_pipeworx_beta is explicitly stated to be currently identical to ask_pipeworx, which is a direct duplication. The polymarket cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) has heavily overlapping purposes around finding and validating betting edges, and the ask_pipeworx / ask_pipeworx_grounded / deep_research / validate_claim tools all handle natural-language 'look up X' queries, making misselection likely without reading lengthy descriptions.

Naming Consistency4/5

snake_case is uniform and helpful prefixes (mbta_, polymarket_, pipeworx_, ask_pipeworx) create recognizable families. However, verb style is inconsistent — imperative verbs like ask/compare/discover/validate mix with noun-first names like bet_research, entity_profile, and search_within, and the memory trio (remember/recall/forget) doesn't share a common prefix.

Tool Count2/5

At 35 tools this exceeds the comfortable range, and the count is inflated by near-duplicates (ask_pipeworx_beta) and a dense 6-tool polymarket family. The server also mixes unrelated domains — only 4 of 35 tools are MBTA transit tools while the rest are Pipeworx data research, prediction markets, memory, and subscriptions — making it a kitchen sink rather than a well-scoped set.

Completeness4/5

The Pipeworx research surface is thorough: query, grounded verification, deep research, entity resolution, profiles, comparisons, change feeds, claim validation, and subscriptions are all covered with few dead ends. Minor gaps exist — the MBTA portion lacks schedule/line-detail tools beyond departures and alerts, and the AI-visibility feature feels bolted on without deeper integration.

Resources