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Glama

flood

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. Added

TDQS

A4.7/5.0
Behavior5/5

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

Despite annotations already indicating read-only/idempotent safety, the description adds rich behavioral detail: fan-out to SEC/GDELT/USPTO, fallback and soft-fail behavior, return structure with changes[] grouped by source, and 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.

Conciseness5/5

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

Dense but highly efficient; every sentence delivers value: query examples, source fan-out, fallback logic, parameter format, return structure, and alternative tool. Front-loaded with common use cases.

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?

With no output schema, the description compensates by describing the return shape (changes[] grouped by source, total_changes, citation URIs). It also covers source-specific behavior, window semantics, and the key alternative tool, making the tool's operation fully understandable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all three parameters fully (100% coverage), so baseline is 3. The description reinforces the `since` shorthand and ticker/CIK formats but does not add substantive meaning beyond the schema; it contextualizes rather than extends parameter semantics.

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's function: a change feed for a company over a time window, fanning out to multiple sources. It distinguishes itself from sibling entity_profile by specifying static profile vs. change feed.

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 natural-language query examples and an explicit 'instead' alternative: 'Use entity_profile instead when you want the static profile'. This gives clear when-to-use guidance.

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

A3.6/5.0
Disambiguation2/5

The toolset contains several heavily overlapping families: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates (beta is currently identical), and polymarket_edges, polymarket_arbitrage, and bet_research cover adjacent purposes. Even though descriptions are detailed and attempt to differentiate, an agent can easily route a query to the wrong member of a cluster.

Naming Consistency2/5

Most names are readable snake_case, but the set does not follow a single convention: it mixes verb-first names (get_flood_forecast, subscribe), noun-phrase names (entity_profile, recent_changes, pipeworx_feedback), and product-prefixed families (polymarket_*). The verb style is also inconsistent across ask, get, list, scan, validate, generate, and discover, so names don't reliably predict what a tool does.

Tool Count2/5

33 tools is well above the 25-tool boundary for a coherent set, and the server's nominal 'flood' scope accounts for only two of them. The rest belong to unrelated domains like general data lookup, prediction markets, memory, and subscriptions, making the set feel like several MCP servers merged together.

Completeness3/5

For the broad data-agent scope, coverage is fairly strong: querying, entity resolution, memory lifecycle, subscription lifecycle, and prediction-market analysis all have their major operations represented. However, the flood domain implied by the server name is thin—only forecast and river discharge, with no historical series, flood-specific alerting, or location-focused risk tools—so the surface is not clearly complete for any single stated purpose.

Resources