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Glama

Geohash

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?

The description discloses behavioral details beyond annotations: it fans out to multiple sources (SEC EDGAR, GDELT→GNews fallback, USPTO), explains fallback logic, and mentions potential issues like rate limiting and API sunset. Annotations confirm read-only and idempotent behavior, with no contradiction.

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 fairly long but front-loaded with concrete examples. Every sentence adds value, though some repetition could be trimmed. It is well-structured for an AI agent to grasp quickly.

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 lacking an output schema, the description explains the return format (changes grouped by source, total_changes count, citation URIs). It also covers fallback behavior and soft-failure for USPTO, making it complete for a complex multi-source 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?

The input schema already has 100% description coverage for all three parameters. The description adds further context, such as accepted formats for `since` (ISO date or relative shorthand) and the fallback behavior for news sources, enhancing understanding 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's purpose: providing a change feed for a company in a recent time window. It includes example queries and explicitly distinguishes it 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?

The description provides explicit guidance on when to use this tool (e.g., 'What's new with X') and when not to use it, directing users to entity_profile for static profiles. It also gives example values for the `since` parameter.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Geohash toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Many tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, with beta explicitly documented as currently identical, while discover_tools/suggest_questions and the several Polymarket analysis tools also blur together. The detailed descriptions help only after careful reading; an agent can easily misselect.

Naming Consistency2/5

Names are all snake_case, but the conventions are mixed: verb_noun (encode_geohash, generate_llms_txt, scan_dependency) coexists with bare verbs (remember, forget), noun phrases (entity_profile, polymarket_arbitrage), and adjective-noun forms (recent_alerts, recent_changes). The Pipeworx family has ask_pipeworx variants but no predictable pattern across the set.

Tool Count2/5

33 tools is well over the useful focused-server range, and the mismatch with the server name is stark: only 2 of 33 tools actually relate to geohashing. The remaining 31 form a sprawling data-research, prediction-market, memory, and subscription toolkit that would be heavy even as a standalone Pipeworx server.

Completeness2/5

For the geohash core, encode/decode covers the basic operation but lacks obvious utilities like neighbors, distance, or batch decoding. For the broader implicit Pipeworx surface, there is no coherent lifecycle tying the research, prediction-market, and subscription features together, and several workflows dead-end at analysis.

Resources