Skip to main content
Glama

Harrypotter

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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful context beyond these hints: it details the fan-out to multiple data sources (SEC, GDELT/GNews, USPTO), the fallback logic (rate-limited GDELT→GNews), and the soft-fail for patents after API sunset. This gives the agent a clear picture of the tool's runtime behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Despite being long, every sentence provides essential information: example queries, data sources, parameter formats, return structure, and an explicit alternative. The information is front-loaded and logically organized, making it efficient for an agent to parse.

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 that the tool has multiple external dependencies and no output schema, the description covers the key return structure (changes[] grouped by source + total_changes + citation URIs) and important edge cases (rate limits, API sunsets). It is comprehensive enough for an agent to know what to expect and how to invoke it correctly.

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?

The schema already provides 100% coverage with descriptions for all three parameters, so baseline is 3. The description adds extra value by specifying the accepted formats for 'since' (ISO date or relative shorthand like '7d', '30d', '3m', '1y') and for 'value' (ticker or zero-padded CIK). This is more detail than the schema provides, earning a 4.

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 with a specific verb + resource: 'change feed for a company in the last N days/weeks/months' and includes example natural-language queries. It distinguishes itself from the sibling tool entity_profile by explicitly contrasting the dynamic change feed with a 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?

The description provides explicit usage context ('What's new with X', 'latest on Y', etc.) and explicitly names the alternative tool for static profiles: 'Use entity_profile instead when you want the static profile...'. It also mentions fallback behaviors (GDELT→GNews, USPTO soft-fail) which help set expectations on when results may vary.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Harrypotter toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical, and ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap in data-query functionality. Prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) also have significant conceptual overlap.

Naming Consistency3/5

All names use snake_case, so there's no camelCase mixing, but conventions vary widely: some are verb_noun (list_characters, resolve_entity, validate_claim), some are noun phrases (entity_profile, recent_alerts, polymarket_edges), and some use a product prefix (ask_pipeworx, pipeworx_feedback). The Harry Potter subset (list_characters, list_spells, list_staff, list_students) is consistent, but the overall set lacks a unified pattern.

Tool Count2/5

35 tools is well above the 25+ threshold that feels heavy, and the server's stated name suggests a narrow Harry Potter scope, yet the vast majority of tools are unrelated Pipeworx data utilities. The count appears bloated and misaligned with the apparent purpose.

Completeness2/5

For a Harry Potter server, the four listing tools are thin (no detail lookups, no filtering by ID, no sort or random), leaving obvious gaps. For a Pipeworx data server, the set is broad but still lacks obvious additions like a generic list-sources tool. The mismatch between name and content makes it impossible to call the surface complete for any single purpose.

Resources