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Glama

Ebird

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: it fans out to multiple sources (SEC, GDELT/GNews, USPTO) with fallback behavior, mentions soft-fail for USPTO, and describes return structure. 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 front-loaded with query examples and efficiently packs information about sources, fallback, and return values. Slightly dense but not verbose; could be broken into bullet points for better readability.

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?

Despite no output schema, the description clearly explains the return format (structured changes grouped by source, total count, citation URIs). It also covers the parallel call behavior and source fallback logic, making it complete for a 3-parameter tool.

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%, but the description adds meaningful guidance beyond schema: for 'since' it gives examples and recommends '30d' or '1m', for 'type' it clarifies only 'company' is supported, for 'value' it explains ticker or CIK formats.

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 states the tool provides a change feed for a company, with specific query examples like 'What's new with X'. It explicitly distinguishes from sibling tool 'entity_profile' by stating when to use each.

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 explicitly tells when to use this tool and when to use 'entity_profile' instead, providing clear context for AI agent decision-making.

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
Disambiguation3/5

Several tools are close cousins: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying data catalog, and bet_research/polymarket_edges/polymarket_arbitrage share a betting-research niche. The eBird tools are clearly a different cluster, but the server carries so many unrelated domains that an agent may struggle to pick the right category member (e.g., stable router vs beta router vs grounded router).

Naming Consistency3/5

Almost everything is snake_case, but the pattern is not uniform: there are plenty of verb_noun names (find_species, list_subregions, scan_competitor_ai_presence) mixed with bare consumer-style names (ask_pipeworx, bet_research, entity_profile, deep_research) and short helpers (recall, forget, remember). No mixed scripting-case chaos, but no consistent verb_noun or noun_verb system either.

Tool Count2/5

36 tools is heavy for a server that calls itself Ebird: only ~5 tools actually relate to bird observation, while the rest span Pipeworx data lookups, Polymarket betting, legal/regulatory analyzers, memory, subscriptions, competency scanning, npm package checking, and llms.txt generation. The count would be reasonable for a broad data platform, but the server's stated identity and the bundled tool set do not match, making the scope feel bloated and incoherent.

Completeness2/5

For a bird-centric server, the surface is thin: you can find a species, list subregions, and pull recent/notable observations, but you cannot get coordinated eBird atlases, hotspot details, species life-history stats, or region-based species lists. For the larger set of unrelated tools, completeness is impossible to gauge about a missing domain; the eBird purpose feels unfinished even though the miscellaneous tools are overloaded.

Resources