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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?

Annotations include readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral details: fan-out to multiple sources, GDELT preferred with GNews fallback on rate limits, USPTO soft-fail due to API sunset, and return format with grouped changes and 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.

Conciseness4/5

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

The description is well-structured with examples, then behavioral details, parameter notes, return format, and sibling differentiation. It is informative but slightly verbose; however, every sentence adds value for a complex tool.

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 multiple data sources, fallback mechanisms, and lack of output schema, the description is exceptionally complete. It explains the return format, the soft-fail for USPTO, and the alternative tool entity_profile for static data.

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?

Schema covers all 3 parameters. The description adds significant meaning: `since` examples with ISO date and relative shorthand, suggestion for typical monitoring, `value` accepts ticker or CIK, `type` currently only supports 'company'. This provides actionable 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?

The description clearly states the tool provides a change feed for a company over a time window, listing example queries and distinguishing from entity_profile. It explicitly details the data sources (SEC EDGAR, GDELT/GNews, USPTO) and the parallel fan-out behavior.

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 includes explicit usage examples and directs users to use entity_profile for static profiles. It explains the fallback between GDELT and GNews, and provides guidance on the `since` parameter with typical values ("30d" or "1m").

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

B3.1/5.0
Disambiguation1/5

The tool set includes multiple near-identical tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and several overlapping entity/profile tools (entity_profile, compare_entities, resolve_entity, validate_claim) that make it hard for an agent to choose the right one. The Bitcoin-specific tools are isolated and don't integrate well with the rest, creating two separate domains.

Naming Consistency2/5

Naming conventions are mixed: some tools use snake_case (ask_pipeworx, fee_estimates), some use underscores (polymarket_edges), and others are standalone verbs (remember, recall, forget). No consistent pattern is followed across the tool set.

Tool Count2/5

With 39 tools, the server is overstuffed for its stated purpose ('Blockstream Info'). The Bitcoin blockchain tools are only 7, while the rest are a sprawling external data platform (Pipeworx) with many redundant tools, making the number feel excessive and unfocused.

Completeness2/5

For a Bitcoin-oriented server, the blockchain tools cover basic operations (address, block, transaction, fee_estimates) but miss essentials like UTXO queries or block details. Meanwhile, the Pipeworx tools dominate and are overcomplete for a server that should be lightweight. The mismatch leaves the Bitcoin part incomplete.