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Glama

Startup Oracle

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
Behavior4/5

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

Annotations already cover safety and idempotency. The description adds valuable behavioral details: fan-outs to SEC EDGAR, GDELT→GNews fallback with rate-limit note, USPTO soft-fail, and structured return format. This goes beyond annotations without contradicting them.

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 thorough but slightly long; however, every sentence adds value. It is front-loaded with example use cases and structured logically. Could be trimmed slightly without loss, but overall 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, description adequately explains return structure (changes[], total_changes, citation URIs). The complex behavior (multiple sources, fallback, soft-fail) is well covered. Sibling differentiation is handled. Complete for the tool's complexity.

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

Parameters5/5

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

Schema coverage is 100%, but description adds meaning: explains 'since' accepts ISO or relative shorthand with examples, clarifies 'type' is limited to 'company', and notes 'value' accepts ticker or CIK. This provides practical guidance beyond schema descriptions.

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 returns a change feed for a company (SEC filings, news, patents) over a time window, and explicitly distinguishes itself from the sibling 'entity_profile' tool, making the purpose highly specific and differentiated.

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 example queries, explains multi-source aggregation, gives typical 'since' parameter values ('30d' or '1m'), and explicitly directs to 'entity_profile' for static profiles. This offers clear context on when and how to use the tool.

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

ask_pipeworx_beta is explicitly identical to ask_pipeworx today, and ask_pipeworx_grounded is another variant of the same router, so an agent can easily misselect. Additionally, bet_research, polymarket_edges, and polymarket_arbitrage all present as prediction-market opportunity finders with overlapping responsibilities.

Naming Consistency3/5

Most tools follow snake_case verb_noun (ask_pipeworx, list_subscriptions, resolve_entity, unsubscribe, validate_claim), but several break the pattern with noun phrases like polymarket_edges and pipeworx_trending, plus oddities like startup_oracle_evaluate. The mix is readable but not predictable.

Tool Count2/5

32 tools is well over the 25 threshold and the set is not tightly scoped: prediction markets alone account for six overlapping tools, and the server also bundles memory, subscriptions, npm dependency scanning, llms.txt generation, and AI visibility checks. ask_pipeworx_beta adds a duplicate that does not earn its slot.

Completeness3/5

As a read-heavy research gateway, the surface is broad: lookups, profiles, comparisons, verification, deep research, discovery, and citation handling are covered, and the subscription/memory helpers have their own lifecycle operations. But the 'Startup Oracle' mission is thin — startup-specific evaluation is a single joke tool, and there is no way to update subscriptions or act on research findings beyond saving memory.

Resources