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Glama

Okx

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?

Discloses the multi-source fan-out to SEC EDGAR, GDELT→GNews with fallback conditions, and USPTO with a soft-fail due to API sunset. This adds value beyond the readOnly/idempotent annotations by explaining exactly what happens on each source and potential failure modes.

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 dense but every sentence serves a purpose: query examples, source fan-out, parameter formats, return structure, and alternative tool. It is well-structured and front-loaded with the core purpose.

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 lacking an output schema, the description clearly states the return format (changes[] by source, total_changes, citation URIs). It also covers fallback behavior and soft-fail conditions, making it complete for a tool of this complexity.

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 coverage is 100%, with detailed per-parameter descriptions already in the schema. The description largely repeats the parameter info, adding only minor context like 'one parallel call' and the typical monitoring window recommendation. Therefore the description does not add substantial semantic value 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?

Clearly defines the tool as a change feed for a company over a time window, with common query phrasings. Explicitly distinguishes from entity_profile by noting the static profile use case. The verb-phrase 'change feed' and resource 'company' are specific and accurate.

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?

Provides strong usage guidance: tells the user to use entity_profile when a static profile is needed regardless of window. The description also clarifies the tool's parallel call behavior and the kinds of questions it answers, making it easy to select among siblings.

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

B3.2/5.0
Disambiguation2/5

The tool set mixes OKX exchange tools with a large set of Pipeworx data query tools and prediction market tools. Many tools overlap in purpose, e.g., ask_pipeworx, deep_research, and ask_pipeworx_grounded all answer questions but with different modes. OKX tools like ticker and tickers are clear but the overall set is confusing.

Naming Consistency2/5

Naming is inconsistent: OKX tools use single nouns or underscores (ticker, order_book), Pipeworx tools use verb phrases (ask_pipeworx, validate_claim), and prediction market tools use prefixed names (polymarket_arbitrage, bet_research). No consistent pattern.

Tool Count2/5

43 tools is excessive for a coherent server. The scope is unclear—combining exchange, data lookup, and prediction market tools into one server results in a cluttered surface.

Completeness2/5

The server tries to cover too many domains. OKX coverage is decent, but the inclusion of many unrelated tools (e.g., generate_llms_txt, scan_dependency) makes the set feel incomplete for any single purpose. Gaps exist in each sub-domain due to the broad scope.

Resources