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Glama

Opensea

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, openWorldHint, idempotentHint, destructiveHint=false. The description adds behavioral details: fans out to multiple sources, fallback logic (GDELT→GNews), USPTO soft-fail, returns structured changes grouped by source with citation URIs. No contradictions.

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 a single paragraph but dense with information, front-loaded with examples. Every sentence adds value, though it could be more structured (e.g., bullet points). Still very concise and effective.

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 no output schema, the description explains the return format: structured changes grouped by source, total_changes count, and citation URIs. It also covers fallback and soft-fail. However, it omits mention of potential rate limits or pagination. For a complex tool, this is fairly complete.

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% with descriptions for all 3 parameters. The description adds further meaning: explains type is only 'company', since accepts ISO or relative formats, value is ticker or CIK. It also provides typical usage suggestion for since.

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 states the tool provides a 'change feed for a company in the last N days/weeks/months' and lists specific sources (SEC EDGAR, GDELT→GNews, USPTO). It clearly distinguishes from sibling tool 'entity_profile' which is 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?

Explicitly provides example user queries like 'What's new with X' and 'latest on Y'. It tells when to use this tool vs entity_profile. It also explains the GDELT/GNews fallback and parameter usage guidance (e.g., 'Use 30d or 1m for typical monitoring').

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

Tools cluster into overlapping groups: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates distinguished only by mode; bet_research, polymarket_edges, and polymarket_arbitrage all target prediction-market opportunities. The five OpenSea read tools are distinct, but they are buried among several unrelated domains, making misselection likely.

Naming Consistency4/5

Names are overwhelmingly snake_case with a verb_noun structure (get_collection, list_owned_nfts, validate_claim, create nothing but still remember/unsubscribe). Pipelined families like ask_pipeworx_* and polymarket_* are consistent, with only minor deviations such as pipworx_trending or bet_research not following a clear verb-object pattern.

Tool Count2/5

36 tools is too many for a coherent server, and the count is inflated by at least four unrelated domains: OpenSea NFT reads, Pipeworx data lookup/research, Polymarket betting, and memory/subscription utilities. Only five tools actually relate to the server's stated OpenSea purpose, so the surface is heavily bloated with off-scope functionality.

Completeness2/5

The OpenSea-relevant tools cover basic read operations—collections, stats, single NFT, collection NFTs, and owned NFTs—but omit search, events, offers/listings, order book data, and account/contract details. The many unrelated Pipeworx tools do not fill these gaps, so an agent needing real marketplace behavior would hit dead ends.

Resources