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Glama

Wolfram Alpha

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

A4.9/5.0
Behavior5/5

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

The description richly discloses behavior beyond annotations: multiple API fan-outs, GDELT→GNews fallback on rate limits/5xx, USPTO soft-fail due to API sunset, and 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.

Conciseness5/5

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

The description is dense yet every sentence earns its place: examples, source behavior, parameter formats, output shape, and an alternative. It's well-structured with a clear lead and supporting details, appropriate for the tool's complexity.

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?

Given the tool's multi-source complexity and absence of an output schema, the description fully explains the return format and failure modes. It also covers fallback logic and the entity_profile alternative, making it self-contained for an agent.

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%, so the baseline is 3. The description adds value with explicit examples for `since` (ISO/relative shorthand) and a recommendation for typical monitoring, plus clarification that `value` can be a ticker or CIK. This goes beyond the 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 identifies this as a change feed for a company over a time window, listing example queries and detailing specific data sources (SEC, GDELT/GNews, USPTO). It distinguishes itself from entity_profile by explicitly stating the alternative 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?

Provides explicit guidance on when to use this tool versus entity_profile, and clarifies the parallel call efficiency. It also gives parameter usage examples like '7d', '30d', and recommends '30d' or '1m' for typical monitoring, which helps agents choose appropriate inputs.

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

A3.7/5.0
Disambiguation2/5

Several tools are near-indistinguishable: ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx/ask_pipeworx_grounded/deep_research have overlapping routing behavior. discover_tools vs suggest_questions and ai_visibility_check vs scan_competitor_ai_presence also create boundary ambiguity, making misselection likely for agents.

Naming Consistency2/5

Names mix bare verbs (recall, remember, forget), brand-prefixed nouns (pipeworx_trending, polymarket_edges), and descriptive phrases (generate_llms_txt, scan_competitor_ai_presence). There is no consistent verb_noun or prefix convention, and the server name 'Wolfram Alpha' does not match the dominant pipeworx_/polymarket_ naming.

Tool Count2/5

At 34 tools, the set exceeds the 25+ threshold and feels heavy even for a broad data platform. Several unrelated add-ons (memory trio, ai_visibility, generate_llms_txt, scan_dependency) could live in separate servers, contributing to bloat and diluting the core purpose.

Completeness3/5

The data-research and prediction-market surfaces are fairly thorough (routing, grounded verification, deep research, entity resolution, comparisons, subscriptions), but the Wolfram Alpha core is thin—only short_answer, full_query, and wolfram_compute—with no step-by-step solutions, units catalog, or history. The mismatched server name and unrelated tools indicate an incoherent scope with notable gaps.

Resources