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Glama

Cso Ie

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

A4.8/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, and open-world hints, but the description goes further by explaining the fan-out to SEC EDGAR, GDELT→GNews fallback, and the PatentsView API sunset soft-fail. This adds substantial behavioral context 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.

Conciseness4/5

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

The description is dense but organized: starts with query examples, then core behavior, data sources, parameter details, return format, and alternative tool. Every section earns its place, though it could be slightly more compact without losing value.

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 and lack of an output schema, the description covers input formats, return structure (changes[], total_changes, citation URIs), source grouping, fallback triggers, and failure modes. It is sufficiently complete for an agent to invoke the tool effectively.

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%, but the description adds meaningful detail: `since` accepts ISO dates or relative shorthands with examples, `value` can be a ticker or CIK, and `type` is restricted to 'company'. It also recommends typical monitoring values ('30d' or '1m').

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 over a time window, with multiple query examples ('What's new with X', 'latest on Y'). It also distinguishes from sibling entity_profile by explicitly directing users to that tool for static profiles, 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 openly provides usage triggers and explicitly names an alternative: 'Use entity_profile instead when you want the static profile...'. It also describes when the tool may soft-fail (patents) and the fallback to GNews, setting clear expectations for usage.

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
Disambiguation3/5

Several tools have overlapping purposes: the four ask_pipeworx variants all route to the same 5,581 tools (ask_pipeworx_beta is currently identical to stable), and the six polymarket_* tools have subtle boundaries between research, edges, and arbitrage that could cause misselection. That said, the descriptions are unusually detailed, and non-overlapping clusters (CSO table tools, memory tools, subscription tools) are clearly distinct.

Naming Consistency3/5

All names are snake_case and mostly verb-first (get_dataset, resolve_entity, validate_claim), but conventions are inconsistent: polymarket_* and pipeworx_* are brand/noun-first while bet_research and ask_pipeworx put the verb first for the same domains, and some tools are pure nouns (entity_profile, recent_alerts). The pattern is readable but far from predictable.

Tool Count2/5

At 35 tools the server is well past the 25+ 'too many' threshold for a single MCP. Several tools don't earn their place: ask_pipeworx_beta is functionally identical to ask_pipeworx right now, and the six polymarket tools plus four ask_pipeworx variants represent heavy redundancy for what are essentially two sub-domains.

Completeness4/5

The data-access core is covered end to end: discovery (discover_tools, list_datasets, suggest_questions), structured reads (ask_pipeworx, get_dataset, query_dataset), grounded verification (validate_claim, ask_pipeworx_grounded), comparison (compare_entities), change tracking (recent_changes), plus memory and subscription CRUD. Minor gaps: subscriptions can't be edited (only recreated) and unrelated utilities (generate_llms_txt, scan_dependency) dilute the focus rather than fill a real gap.

Resources