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Glama

Snb Ch

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

A4.8/5.0
Behavior5/5

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

Annotations already declare read-only, open-world, idempotent, and non-destructive, but the description adds essential runtime behavior: fan-out to multiple sources, GDELT→GNews fallback triggers, and PatentsView sunset soft-fail. This goes well beyond the annotations and clarifies what the agent should expect.

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 dense but each clause contributes: triggers, sources, fallback, time handling, return shape, and alternative tool. It could be better structured with bullets, but it's not bloated; the length is justified by inherent complexity.

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?

For a multi-source aggregation tool with no output schema, the description covers trigger phrases, sources, fallback logic, soft-fail behavior, `since` formats, return structure (changes[], total_changes, citation URIs), and the sibling alternative. This is comprehensive.

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 covers all three parameters with descriptions, but the description enriches `since` with concrete formats ('2026-04-01', '7d', '3m') and a recommended value ('30d'/'1m'). It also clarifies `value` accepts ticker or zero-padded CIK, adding practical examples beyond 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 opens with natural-language query examples, then states the precise action: a change feed for a company over a time window in one parallel call, fanning out to SEC, GDELT/GNews, and USPTO. It clearly differentiates from entity_profile, which serves the static profile.

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?

Explicitly names entity_profile as the alternative when a static profile is desired, and advises '30d' or '1m' for typical monitoring. It also explains fallback conditions (GNews on rate-limit/5xx) and soft-fail for patents, giving concrete use context.

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

Several tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and suggest_questions all handle question-answering, with beta currently identical to the main router. The Polymarket family has six distinct but related tools, and descriptions are detailed enough to differentiate, but an agent could still misselect when the boundaries are subtle.

Naming Consistency3/5

All tool names use snake_case, but the pattern is mixed: some start with verbs (resolve_entity, validate_claim), while many are noun phrases (entity_profile, bet_research, polymarket_edges). This is readable but not a consistent verb_noun convention, and the repeated ask_pipeworx_* and polymarket_* prefixes add some structure.

Tool Count2/5

At 33 tools, the surface is heavy for an agent to navigate. While the server covers a broad data-research and prediction-market domain, the count exceeds what is typically manageable, and several tools (ask_pipeworx_beta, discover_tools) could be collapsed or merged without losing core capability.

Completeness4/5

The tool set covers the domain well: querying, grounded verification, entity profiles, comparisons, prediction-market analysis, subscriptions, memory, and onboarding. Minor gaps exist (e.g., no tool to fetch a full SEC filing body directly, relying on ask_pipeworx routing), but there are no obvious dead ends for the stated purposes.

Resources