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Glama

Cbs Nl

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent, and the description enriches this with concrete behavioral details: fan-out to specific sources (SEC EDGAR, GDELT→GNews fallback, USPTO), soft-failure behavior for PatentsView, date parsing formats, and the return structure including citation URIs. This goes well beyond the 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?

Though the description is long, every sentence earns its place: usage examples, source list, fallback logic, date parsing, output summary, and alternative tool. It is front-loaded with intuitive questions and structured with clear separators. No fluff.

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?

The description is thorough for a complex tool with no output schema. It explains the source fan-out, fallback behavior, output shape (changes[] + total_changes + citation URIs), date formats, and when to use a sibling tool. It even flags a known downstream limitation (USPTO soft-fail).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description does not add meaningful parameter semantics beyond the schema; it merely repeats the `since` format examples already present in the schema. No new meaning is provided for `type` or `value`.

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 a clear verb+resource: "change feed for a company in the last N days/weeks/months" with a wealth of natural-language examples ("What's new with X", "updates on Acme"). It explicitly distinguishes itself from sibling tools by suggesting entity_profile 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?

The description provides explicit usage context with sample queries and clearly names an alternative: "Use entity_profile instead when you want the static profile ... regardless of window." This gives the agent direct guidance on when to pick this tool over a sibling.

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

A3.8/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions with subtle differences that are hard to distinguish (beta is currently identical to the stable version). Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_fill_risk) similarly overlap in opportunity-finding. Entity_profile, compare_entities, and recent_changes also share company-research territory.

Naming Consistency3/5

All tool names use snake_case, which is consistent, but the structural pattern varies widely: some are verb_noun (get_data, search_tables), some are noun_phrase (table_dimensions, entity_profile), some are brand-prefixed (pipeworx_trending, polymarket_edges), and the memory tools (remember, recall, forget) break the pattern entirely. Mixed conventions make the set feel less coherent.

Tool Count2/5

With 36 tools, the set is heavy, and the server name 'Cbs Nl' implies a focused CBS statistics dataset, yet most tools cover unrelated domains (Polymarket, AI visibility, npm dependencies). Even as a general data-research platform, the count exceeds the 25-tool threshold for 'heavy', and many tools could be consolidated (e.g., the three ask_pipeworx variants).

Completeness4/5

As a general data-research platform, the tool surface is fairly complete: discovery (discover_tools, search_tables, suggest_questions), metadata (table_info, table_dimensions), retrieval (get_data, ask_pipeworx, deep_research), validation (validate_claim, compare_entities), and supporting features (memory, subscriptions, feedback). Minor gaps include no explicit tool to manipulate data or manage sources, but for a read-heavy research assistant, the coverage is strong.

Resources