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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

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint), the description reveals internal fan-out behavior across SEC EDGAR, GDELT/GNews fallback, and USPTO with soft-fail. It also notes rate-limiting behavior and returns structured data with citation URIs, providing rich operational insights.

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 a dense paragraph but front-loads the core purpose with examples. It could be more structured (e.g., bullet points for sources) but remains effective without excessive verbosity.

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 no output schema, the description fully specifies the return format (structured changes grouped by source, total_changes count, citation URIs). It covers all aspects: purpose, sources, parameters, fallback behaviors, and alternative tool usage, making it complete for a complex tool.

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%, so baseline is 3. The description adds value by explaining the `since` parameter's acceptable formats (ISO date or relative shorthand like "7d") and the `value` parameter's accepted formats (ticker or CIK). It also recommends typical values like "30d".

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's purpose: a change feed for a company aggregating recent updates from multiple sources. It uses specific verbs like "What's new", "latest", and distinguishes itself from the sibling tool entity_profile, which provides 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 provides explicit guidance on when to use this tool (for recent changes over a window) and directs users to entity_profile for static profiles. However, it could more explicitly state when not to use it, though the context is clear.

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

A3.9/5.0
Disambiguation2/5

Several tool families have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates (the beta is currently identical), and the six polymarket_* tools plus bet_research heavily overlap in prediction-market analysis. ai_visibility_check and scan_competitor_ai_presence also serve the same core function. An agent would frequently need to read lengthy descriptions to pick the right one, and could easily misselect.

Naming Consistency3/5

Most tools follow a readable snake_case pattern, but the style is mixed: some are verb-first (ask_pipeworx, search_datasets, resolve_entity), some are domain-prefixed nouns (polymarket_edges, pipeworx_feedback), and a few are bare nouns or adjective-noun phrases (dataset, entity_profile, recent_alerts). It is not chaotic, but there is no single predictable verb_noun convention across the set.

Tool Count2/5

With 34 tools, this is above the 25+ threshold considered too many for a coherent toolset. The count is inflated by near-duplicate families (three ask_pipeworx variants, six polymarket tools) that could reasonably be consolidated. While the server covers a broad domain, the number of top-level choices creates unnecessary selection burden for agents.

Completeness4/5

For a read-focused data/research gateway, the surface is quite complete: general lookup, grounded verification, deep research, entity resolution, comparisons, change tracking, memory, subscriptions, and feedback are all present. Minor gaps exist, such as no direct fetch-by-URI tool for the pipeworx:// citations that other tools return, and the Dutch open-data tools are strictly read-only. These are workarounds rather than dead ends.