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Glama

Httpstatus

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?

Discloses fan-out behavior across multiple APIs, fallback logic (GDELT to GNews on rate limits), and soft-fail for USPTO. Describes return structure (grouped changes with counts and URIs). Adds context beyond 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 information-dense and front-loads with intuitive examples. While relatively long, every sentence contributes essential detail. Minor redundancy could be trimmed.

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?

Covers all key aspects: data sources, parameter meaning, return structure, fallback behavior, and relationship to sibling tool. No output schema exists, but the description adequately explains return format.

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% with existing descriptions. The description adds value by explaining relative shorthand for 'since', acceptable formats for 'value' (ticker or CIK), and that 'type' is currently limited to '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 the tool's purpose: providing a change feed for a company in a specified time window. It gives example queries and explicitly distinguishes from the sibling tool 'entity_profile' 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 Guidelines5/5

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

Provides explicit when-to-use guidance with example queries, explains data sources, and advises when to use the sibling 'entity_profile' instead. No ambiguity about 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

A3.7/5.0
Disambiguation4/5

Most tools have distinct purposes, but ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are very similar, causing potential confusion. The memory tools (remember/recall/forget) are separate, but the overall set is diverse enough that agents can typically distinguish them.

Naming Consistency2/5

Tool names use a mix of camelCase (ask_pipeworx, entity_profile, generate_llms_txt) and snake_case (http_status, list_http_statuses, list_subscriptions), with no consistent verb_noun pattern. Some names are descriptive, but the lack of a unified convention hurts predictability.

Tool Count2/5

33 tools is high for a server named 'Httpstatus' that only has two HTTP-related tools. The large number spans data retrieval, memory, subscriptions, prediction markets, and more, making the server feel bloated and unfocused.

Completeness3/5

The server covers many domains but each is incomplete. HTTP status has lookup and list but no lifecycle. Memory tools are basic. Data retrieval is extensive but not exhaustive for any single domain. It's a broad but shallow collection.

Resources