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Glama

W3c

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

A4.9/5.0
Behavior5/5

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

Annotations already declare it as read-only and idempotent. The description adds valuable behavioral details: fan-out logic, fallback from GDELT to GNews, USPTO soft-fail due to sunset, and return structure.

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 thorough but well-structured with front-loaded examples. Every sentence provides useful information, though it could be slightly trimmed 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 (multiple sources, fallbacks, parameter formats) and no output schema, the description is comprehensive: it explains all key behaviors, inputs, and outputs (grouped changes, total count, URIs).

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 coverage is 100%, but the description adds significant value beyond the schema: explains 'since' format with examples, clarifies 'value' accepts ticker or CIK, and notes 'type' only supports 'company'.

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 it provides a change feed for a company, fanning out to multiple sources (SEC, GDELT/GNews, USPTO). It distinguishes itself from sibling tool 'entity_profile' by specifying what each tool covers.

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 explicitly tells when to use this tool (for recent changes) and when to use the alternative 'entity_profile' (for static profile). It also gives example queries to guide 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

A3.9/5.0
Disambiguation3/5

The three ask_pipeworx variants plus deep_research and validate_claim overlap heavily on the same routing capability — ask_pipeworx_beta is even explicitly identical to ask_pipeworx right now. The polymarket tools (arbitrage/edges/edge_tracker/fill_risk/kalshi_spread) form a second cluster with fuzzy boundaries, and ai_visibility_check vs scan_competitor_ai_presence overlap. However, most other tools (w3c_search, spec, remember/recall/forget, subscribe/unsubscribe) have clear distinct roles.

Naming Consistency3/5

Names are generally descriptive snake_case with recognizable prefix families (ask_pipeworx_*, polymarket_*, w3c_*), but verb usage is inconsistent: passive labels like ai_visibility_check and entity_profile sit alongside imperatives like recall, forget, subscribe, and resolve_. There's no uniform verb_noun or noun_pattern convention across the set.

Tool Count2/5

33 tools is over the heavy threshold, and the count is wildly mismatched to the server's claimed identity: the server is named 'W3c' yet only 2 of 33 tools (w3c_search, w3c_spec) relate to W3C. The remaining 31 form a sprawling Pipeworx data/prediction-market service that would justify its own server, making this aggregation incoherent.

Completeness3/5

For its actual purpose (broad data research + prediction markets), the surface is fairly complete: search, grounded answers, deep research, comparison, entity profiles, entity resolution, memory, subscriptions, and feedback cover the main workflows. But for the server's stated W3C purpose, only search and spec-detail exist with no broader standards tooling. The domain mismatch makes completeness hard to credit.

Resources