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Glama

Lobsters

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?

Annotations already indicate readOnly, idempotent, etc. The description adds rich behavioral detail: fans out to SEC EDGAR, GDELT→GNews, USPTO; describes fallback logic; mentions output structure with changes[], total_changes, citation URIs. 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.

Conciseness4/5

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

The description is a single dense paragraph that front-loads example queries. Every sentence adds value, but it could be slightly more structured (e.g., bullet points for sources). Still concise and informative for its 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 three parameters, no output schema, the description covers return structure, fan-out logic, fallback, and limitations (USPTO soft-fail). It provides enough context for an agent to understand when to call and what to expect without needing additional documentation.

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. The description adds further context: examples for 'since' (ISO date, relative shorthand), recommended default, and explanation for 'value' (ticker or CIK). For 'type', it notes only 'company' supported. Adds significant value beyond schema.

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 uses concrete example queries and explicitly states the purpose: a change feed for a company over a time window. It distinguishes from sibling 'entity_profile' for static profiles, making the tool's unique purpose very clear.

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 (recent changes over a window) and when-not-to-use (use entity_profile for static profiles). It also explains fallback behavior and soft-fail for USPTO, guiding correct invocation.

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

Many tools have overlapping purposes, e.g., three tools for querying Pipeworx data (ask_pipeworx, ask_pipeworx_grounded, deep_research), and six tools for Polymarket prediction markets. The boundaries between them are unclear, causing potential for misselection.

Naming Consistency3/5

Tool names are all snake_case but follow inconsistent patterns: some use verb_noun (get_hottest, get_newest), some are single verbs (remember, forget), and others are noun_noun (bet_research, polymarket_arbitrage). While readable, the lack of a consistent pattern adds confusion.

Tool Count2/5

With 34 tools, the server is overloaded, especially given its name 'Lobsters' suggests a focus on that site, yet only 4 tools are Lobsters-related. The majority belong to Pipeworx, Polymarket, and generic utilities, making the scope extremely broad and unfocused.

Completeness2/5

For the implied domain of Lobsters, the tool set is incomplete (missing create/update/delete). For the broader domains (Pipeworx, Polymarket), the set is extensive but doesn't align with the server's name. The lack of a coherent domain leaves obvious gaps relative to any single purpose.

Resources