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Glama

Septa

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

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool as read-only and idempotent. The description adds substantial behavioral context: it fans out to multiple sources in parallel, handles rate limiting with a fallback, and documents the return structure (changes[] grouped by source, 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?

The description is a single paragraph that front-loads example queries and packs in many details. While dense, it is efficient and avoids redundancy. A slight trim or structuring into bullet points could improve readability, but it remains concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is fairly complete given the tool's complexity. It explains the return structure and sources. However, it lacks details on error handling or behavior when all sources fail. With no output schema, the description carries the burden, and it mostly succeeds.

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% with descriptions for all three parameters. The description adds extra value by providing examples for 'since' (ISO date and relative shorthand, with default recommendation '30d' or '1m') and clarifying input formats for 'value' (ticker or CIK). This enriches the parameter meaning beyond the 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 clearly states the tool provides a 'change feed for a company in the last N days/weeks/months' and lists the data sources (SEC EDGAR, GDELT→GNews, USPTO). It distinguishes from the sibling tool 'entity_profile' which is for static profiles.

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?

Explicit when-to-use guidance is given: 'Use entity_profile instead when you want the static profile...' The description also provides example queries and explains fallback behavior (GDELT preferred, GNews on rate limit, 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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap among the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and among the Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread). However, detailed descriptions help differentiate them.

Naming Consistency3/5

Tool names mix conventions: snake_case is predominant, but there is no consistent verb_noun pattern. Some tools have 'septa_' prefix, but the majority do not follow a predictable structure. Naming is inconsistent across the set, though subgroups have some consistency.

Tool Count3/5

With 36 tools, the count is high. The server name 'Septa' suggests a focused transit server, but the tool set covers transit, data access, and prediction markets, making it overly broad. The number is borderline appropriate for a general-purpose server but misaligned with the name.

Completeness4/5

The transit domain is well-covered with alerts, positions, schedules, and train views. The data access and prediction market tools also cover their domains comprehensively. Minor gaps exist (e.g., no dedicated weather tool), but overall the tool set provides a wide range of capabilities.

Resources