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Glama

Stat Ee

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

A4.6/5.0
Behavior5/5

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

Annotations already mark readOnly/openWorld/idempotent/non-destructive. The description adds multi-source fan-out, GDELT→GNews fallback on rate limit/5xx, and USPTO patent soft-fail due to API sunset. These are beyond annotation fields.

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?

Description is dense but well-structured; every sentence adds a function detail. Slightly longer than necessary, but not redundant.

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?

Even without output schema, it describes return shape (changes[] by source, total_changes, citation URIs), source behavior, and fallbacks. Covers all operational aspects needed for invocation.

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 100% of params with descriptions; description adds no new parameter details beyond restating the `since` format already present in schema. Baseline 3 for high coverage.

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?

Description clearly states it provides a 'change feed for a company in the last N days/weeks/months' and lists concrete natural-language triggers. It explicitly contrasts with entity_profile, distinguishing it from a key sibling.

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?

Description opens with example queries, specifies when to use (recent changes) and when not (entity_profile for static profile). It also notes 'in ONE parallel call' as an efficiency rationale. This is explicit 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

A3.9/5.0
Disambiguation3/5

Most tools have fairly distinct action/resource targets and the descriptions carefully separate entry points like ask_pipeworx, deep_research, and ask_pipeworx_grounded. However, ask_pipeworx_beta is explicitly an identical clone of ask_pipeworx right now, and a few related pairs (ai_visibility_check vs scan_competitor_ai_presence, stat_ee_find_table fetch_latest vs estonia_average_wage) add ambiguity.

Naming Consistency3/5

Names are consistently lowercase snake_case and verb-led names like resolve_entity, query_table, and suggest_questions are clear. But the set mixes conventions: bare nouns (subjects, recall, forget), adjective-noun phrases (recent_alerts, recent_changes), no-verb names (estonia_average_wage, table_meta), and multiple prefixes (pipeworx_*, polymarket_*, stat_ee_*). It is readable but not a single predictable pattern.

Tool Count2/5

36 tools is well above the 15-tool threshold for a well-scoped server, and the set spans many unrelated domains: Estonian statistics, Pipeworx research, Polymarket betting, AI visibility, npm scanning, memory, and subscriptions. There is also clear redundancy (ask_pipeworx_beta duplicates ask_pipeworx, ai_visibility_check could be folded into scan_competitor_ai_presence). This feels scattered for a server named 'Stat Ee'.

Completeness4/5

For the broad data-research/agent-assistant purpose, key workflows are well covered: discovery/query/grounded/deep research, entity resolution/profile/compare/validate/recent changes, complete memory CRUD, subscription CRUD with alert feeds, and a full Polymarket edge/arb/fill-risk suite. The main gap is not missing operations within these workflows but rather the overall scope being too broad and unfocused.

Resources