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Glama

Digitalnz

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

A5/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: it fans out to multiple sources, explains fallback order (GDELT→GNews on rate limit), notes USPTO API sunset, and specifies return structure (changes[], total_changes, citation URIs). No contradiction 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 a single paragraph that front-loads purpose with example queries, then covers sources, parameters, return format, and sibling guidance efficiently. Every sentence adds value, with no redundancy.

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 the tool's complexity (multiple sources, fallback, soft-fail, parameter formatting), the description covers all necessary aspects: parallel call, data sources, parameter details, return structure, and citation URIs. It is fully 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?

Although schema coverage is 100%, the description adds meaning by explaining accepted formats for `since` (ISO date or relative shorthand with examples) and clarifying `value` accepts ticker or CIK. It also restrains `type` to only "company".

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 starts with explicit user queries ("What's new with X") and clearly defines the tool as a change feed for a company in a time window, listing data sources. It distinguishes from the sibling entity_profile, making the purpose 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 clear when-to-use guidance (recent changes queries) and explicitly advises against using it for static profiles (use entity_profile instead). It also offers parameter tips ("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

A3.7/5.0
Disambiguation2/5

The tool set contains multiple clusters with heavy overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying data sources, making it ambiguous which to choose. Similarly, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and bet_research all target prediction-market opportunities with fuzzy boundaries between them. The three DigitalNZ tools (search, record, search_within) are distinct, but the rest of the set obscures their purpose.

Naming Consistency3/5

Most tools use snake_case with descriptive names, and the polymarket_* cluster is consistent among itself. However, conventions are mixed: some are verb-first (validate_claim, discover_tools, remember), some are noun-first (entity_profile, recent_changes), and ask_pipeworx_beta breaks the pattern with a suffix variant. The naming is readable overall but not uniform.

Tool Count2/5

At 33 tools, the server exceeds the 25-tool threshold for a 'too heavy' count. The vast majority of tools belong to the Pipeworx data-query and prediction-market domains rather than DigitalNZ, which is the server's stated name. A focused DigitalNZ server would need closer to 5-10 tools; a Pipeworx server would still be over-packed at 33 given the functional overlap.

Completeness2/5

For the DigitalNZ domain, the surface is severely incomplete: only search, record, and search_within exist, with no browse, filter, facet, or contribution capabilities. The Pipeworx side is more complete but still has gaps (e.g., no direct per-source query tools, and several tools soft-fail on sunset APIs). The server tries to cover two unrelated domains and satisfies neither fully.

Resources