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Glama

Merriam Webster

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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. The description goes beyond by detailing the multi-source fan-out (SEC EDGAR, GDELT→GNews, USPTO), parallel execution, soft-failure for USPTO, and the structured return format. No contradictions 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 concisely structured: it opens with user-facing query examples, then explains the behavior, parameters, and alternatives. Every sentence adds necessary information without redundancy. Appropriate length for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity and lack of output schema, the description covers return fields (changes[], total_changes, pipeworx:// URIs), the parallel call nature, and source-specific details. Minor gaps: no mention of result limits or pagination, but overall sufficiently complete for agent invocation.

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 value by explaining the `since` parameter formats with examples and a recommended default, clarifying that `type` only supports 'company', and detailing acceptable `value` formats (ticker or zero-padded CIK). This practical guidance raises the score above baseline.

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 the tool as a change feed for a company over a time window, with specific example queries. It distinguishes itself from the sibling 'entity_profile' tool by contrasting temporal vs. static profiles. The verb-resource relation is explicit and unambiguous.

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 when-to-use guidance, including when to prefer alternatives ('Use entity_profile instead when you want the static profile'). It also gives practical advice for parameters (e.g., 'Use "30d" or "1m" for typical monitoring') and explains the fallback behavior between GDELT and GNews.

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

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers, and the Polymarket cluster (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) all target the same opportunity-finding use case. The detailed descriptions help, but an agent selecting among 6+ prediction-market tools or 4+ general-data entry points will frequently pick the wrong one.

Naming Consistency2/5

Naming is a mix of snake_case verb_noun patterns (ask_pipeworx, discover_tools, scan_competitor_ai_presence), bare dictionary verbs (define, thesaurus), bare memory verbs (remember, forget, recall), and inconsistent version/feature suffixes (ask_pipeworx_beta, ask_pipeworx_grounded, polymarket_kalshi_spread). There is no consistent convention across the set.

Tool Count1/5

A server named 'Merriam Webster' exposes 33 tools, but only 2 (define, thesaurus) are dictionary-related — the other 31 are Pipeworx data-research, prediction-market, and memory tools. This is an extreme mismatch between the server's apparent scope and its tool count; the dictionary surface could be served with 3-5 tools.

Completeness2/5

For the stated dictionary domain, the surface is thin: define and thesaurus cover word lookup, but miss audio pronunciation, example sentences, usage notes, word history, and word-of-the-day — common dictionary features. Meanwhile, the large Pipeworx cluster includes meta-tools, memory, subscriptions, and feedback, making the overall surface cluttered and leaving no single domain comprehensively covered.

Resources