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Glama

Nbp Pl

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?

Description adds significant behavioral context beyond the annotations: it reveals fan-out to SEC EDGAR, GDELT/GNews fallback (with rate-limit handling), USPTO soft-failure, and the return structure. Annotations already mark it read-only and idempotent, and the description complements this without contradiction.

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?

Description is well-structured with front-loaded examples, clear explanation of sources, parameter details, return format, and alternative tool. Each sentence adds value, and length is appropriate for the 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?

Despite having no output schema, the description adequately describes the return (changes grouped by source, total_changes count, citation URIs). It covers fallback behavior, parameter formatting, and alternative tool. No major gaps given the tool's complexity.

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. Description enhances by providing recommended default for 'since' ('30d' or '1m'), explaining 'value' accepts ticker or CIK, and noting 'type' only supports 'company'. This adds useful guidance beyond the 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?

Description clearly states the tool provides a change feed for a company over a time window, using examples like 'What's new with X'. It explicitly distinguishes from the sibling tool 'entity_profile', which provides static profiles instead.

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 gives clear use case examples (e.g., 'latest on Y', 'what happened to Z this week') and explicitly advises to use 'entity_profile' for static profiles. It also explains the tool fans out to multiple sources, setting appropriate expectations.

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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed routing guidance, but the three ask_pipeworx variants and overlapping company-focused tools (entity_profile vs compare_entities vs recent_changes) create some ambiguity. The descriptions are thorough enough that an agent can usually select correctly.

Naming Consistency4/5

All names are snake_case and mostly descriptive, but they mix verb-first (ask_pipeworx, discover_tools), noun-first (entity_profile, gold_price), and domain-prefixed (polymarket_*, pipeworx_*) patterns. The style is consistent enough to be predictable, though not uniform verb_noun.

Tool Count2/5

34 tools is well past the 25-tool threshold for "too many," and the server name (Nbp Pl) suggests a narrow Polish-bank scope while most tools cover unrelated domains like prediction markets, dependency scanning, and AI visibility. The breadth makes the set feel over-stuffed and unfocused.

Completeness4/5

The toolset covers a remarkably complete data-research workflow: general querying, grounded answers, deep research, entity resolution, profiles, comparisons, claim validation, change feeds, discovery, subscriptions, and memory. Minor gaps like subscription updating or a direct source catalog exist, but agents can work around them.

Resources