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Glama

Data Boston

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 details: parallel fan-out to SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail, and return structure (changes[], total_changes, 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.

Conciseness4/5

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

Front-loaded with example queries, then details sources, parameters, and return. Every sentence adds value. Slightly long but warranted given multiple data sources and fallback logic. Could be tightened slightly but still efficient.

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, the description explains return shape (changes grouped by source, total_changes count, pipeworx:// URIs). It covers all sources, fallback behavior, parameter formats, and explicitly contrasts with entity_profile. Completeness is high for a multi-source aggregation tool.

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 covers all parameters with descriptions. Description adds context: type is only 'company', value can be ticker or CIK, and since accepts both ISO date and relative shorthand with recommendation for typical monitoring ('30d' or '1m'). This adds practical usage hints beyond schema.

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 starts with concrete query examples ('What's new with X', 'latest on Y') and states it's a change feed for a company over a time window. It explicitly distinguishes from sibling tool 'entity_profile', which serves a different purpose (static profile) regardless of window.

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 guidance via example queries and recommends 'use entity_profile instead when you want the static profile'. Also explains fallback behavior between GDELT and GNews, and soft-fail for patents, giving clear context on when to expect results.

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

A4.1/5.0
Disambiguation3/5

Several tools occupy adjacent territory: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, ask_pipeworx_beta is currently an exact duplicate, and there are multiple polymarket-related tools. The descriptions are unusually explicit and cross-reference when to use which, which keeps this from scoring lower.

Naming Consistency4/5

All tool names use lowercase snake_case and group into recognizable families (boston_*, pipeworx_*, polymarket_*), giving the set a consistent feel. However, the set mixes verb_noun names, bare verbs like remember/forget, and adjective_noun phrases like recent_changes, so it is not a strict verb_noun pattern throughout.

Tool Count2/5

At 34 tools this exceeds the 25+ threshold and feels bloated for a server with the narrow name "Data Boston"—only three tools directly concern Boston data, while the rest cover general research, prediction markets, subscription management, memory, AI visibility, and npm auditing. Most tools have a purpose, but the overall surface is not well-scoped.

Completeness4/5

For the broad data-access purpose, coverage is strong: routing, grounded verification, entity profiles, comparisons, change tracking, entity resolution, discovery, monitoring, memory, and Boston datasets are all represented. Minor gaps exist, such as no subscription update operation, no explicit fetch-by-citation tool, and limited boston_recent coverage, but agents can work around them.

Resources