Skip to main content
Glama

genderize

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?

It discloses fan-out to SEC EDGAR, GDELT→GNews fallback rules, USPTO soft-fail due to API sunset, and return shape (changes[], total_changes, citation URIs) — all beyond the read-only/idempotent annotations. 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 but every element earns its place: examples, purpose, sources, fallback, date formats, return structure, alternative pointer. It's front-loaded and not padded, 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?

With no output schema, the description still explains the return structure (changes[] grouped by source + total_changes + citation URIs) and covers edge behavior (GDELT preferred, GNews fallback, soft-fail). For a multi-source tool with 3 params, this is complete.

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 baseline is 3. The description adds a typical-monitoring recommendation ('Use "30d" or "1m"') and clarifies `since` semantics in context, but most parameter details duplicate the schema. This modest extra guidance merits a 4.

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 opens with concrete natural-language examples then immediately states 'change feed for a company in the last N days/weeks/months' — a specific verb+resource+scope. It distinguishes from the sibling by explicitly pointing to entity_profile for static profiles, making purpose unmistakable.

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?

Beyond the purpose, it specifies when to prefer this tool over an alternative: 'Use entity_profile instead when you want the static profile ... regardless of window.' It also states this works in 'ONE parallel call' and details source fallback behavior, giving clear usage context. It doesn't list exclusions for every sibling but provides enough for the most relevant alternative.

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

All genderize toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Several tools route the same style of query to the same Pipeworx catalog: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap in purpose, and ask_pipeworx_beta is explicitly identical to ask_pipeworx. The prediction-market tools also blur together, with bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk all covering overlapping analysis territory.

Naming Consistency3/5

The tools are consistently lowercase snake_case, but the naming convention is mixed: some are verb_noun (predict_gender, generate_llms_txt), some are noun phrases (entity_profile, recent_alerts), some are bare verbs (remember, forget), and many share domain prefixes like ask_pipeworx or polymarket_. It is readable, but there is no single predictable pattern across the set.

Tool Count2/5

At 33 tools, this exceeds the 25+ threshold where the surface becomes hard to navigate. More importantly, the count does not match the server's apparent genderize identity: the vast majority of tools are unrelated Pipeworx research, prediction-market, memory, and subscription utilities bolted onto a two-tool gender-prediction core.

Completeness3/5

As a broad research assistant, the set is substantial: it covers question routing, grounded verification, deep research, entity profiles, comparisons, change feeds, memory, and subscriptions. However, the actual genderize domain is thin—just two prediction tools with no batch, supported-country, or accuracy endpoints—and several unrelated capabilities feel bolted on, making coverage uneven.

Resources