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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

A4.5/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description details internal behavior: fans out to multiple sources (SEC, GDELT→GNews fallback, USPTO), soft-fail scenarios, and return format with 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 somewhat lengthy but front-loaded with example queries. Every sentence adds value, though it could be more structured (e.g., bullet points for fallback behavior). No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 3 required parameters, full schema coverage, and no output schema, the description explains return structure (grouped changes, total count, citations) and handles edge cases like USPTO soft-fail. Slight miss on pagination or error handling, but adequate for 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 already has 100% coverage, but the description adds practical meaning: explains ISO vs relative shorthand for 'since', recommends defaults ('30d'), clarifies ticker/CIK for 'value', and notes only 'company' for 'type'. This goes beyond basic 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' with specific verbs like 'What's new with X' and lists example queries. It distinguishes from the sibling 'entity_profile' by noting the latter is 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use this tool (for recent changes/news/patents) and directs users to 'entity_profile' instead for static profiles. However, it lacks explicit exclusion scenarios or when-not-to-use guidance.

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

A3.5/5.0
Disambiguation2/5

The four spaceflight news tools (get_article, search_articles, list_sources, latest_reports) are clearly distinct, but the remaining 31 tools create heavy overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research are all query entry points into the same data catalog, and six Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) all serve prediction-market analysis with fuzzy boundaries between them.

Naming Consistency2/5

Naming is inconsistent across the set. Spaceflight tools use verb_noun snake_case (get_article, search_articles, list_sources), but Pipeworx tools mix bare nouns (bet_research, compare_entities), prefixed verbs (ask_pipeworx, discover_tools, validate_claim), and a proliferation of polymarket_* names. There is no single predictable pattern the agent can rely on.

Tool Count1/5

35 tools for a server named 'Spaceflight News' is an extreme mismatch. Only 4 tools actually serve the spaceflight news domain; the other 31 are unrelated Pipeworx data, prediction-market, memory, and feedback tools that belong in a different server entirely.

Completeness2/5

The spaceflight news surface is thin: search, get-by-id, list sources, and latest reports cover basic reading but lack obvious features like listing all launches/events, filtering by launch, or pagination. More importantly, the set is dominated by tools unrelated to spaceflight news, so the tool surface is severely misaligned with the server's apparent purpose.