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Glama

Tvmaze

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 indicate readOnly, openWorld, idempotent, and non-destructive traits. The description adds significant behavioral detail: it fans out to multiple APIs in parallel, has fallback logic, accepts multiple date formats, and returns structured data grouped by source. No contradictions with 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 long but well-structured, front-loaded with example queries. Every sentence adds necessary context for a complex multi-source tool. A minor trim could be possible, but it remains efficient without redundancy.

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 data sources, fallback mechanisms, date formats) and the absence of an output schema, the description is remarkably complete. It covers the return structure, soft-failure behavior, and differentiates from a sibling tool. All essential context for an agent to use the tool correctly is provided.

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%, so baseline is 3. The description adds value beyond the schema: it explains that 'since' accepts ISO dates or relative shorthand with examples ('7d', '30d', '3m', '1y'), recommends '30d' or '1m' for monitoring, specifies that 'value' can be a ticker or CIK, and notes that 'type' only supports 'company'. This significantly enriches parameter understanding.

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 starts with concrete example queries and clearly states it returns a change feed for a company over a time window. It specifies the data sources (SEC EDGAR, GDELT→GNews, USPTO) and the return structure (changes[] grouped by source, total_changes, citation URIs). It also distinguishes itself from the sibling entity_profile for static data.

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/updates) and when to use entity_profile instead. It also explains fallback behavior between GDELT and GNews, and notes USPTO soft-failure. This provides clear guidance for tool selection.

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

A4.1/5.0
Disambiguation4/5

Tools cover a wide range of domains (TV, finance, betting, memory, etc.), each with detailed descriptions that help distinguish their purposes. However, some overlap exists between related tools like bet_research, polymarket_arbitrage, and polymarket_edges, though the descriptions clarify their distinct uses.

Naming Consistency4/5

Most tool names follow a consistent verb_noun pattern with underscores (e.g., get_show, list_episodes, resolve_entity). A few tools use single imperative verbs (remember, recall, forget), which is a minor deviation but still clear.

Tool Count3/5

The server has 31 tools, which is on the higher side. While each tool has a defined purpose, the scope seems too broad for a server named 'Tvmaze', as many tools are unrelated to TV (e.g., company analysis, betting, memory). A more focused set would improve coherence.

Completeness4/5

For a general-purpose data server, the tool set covers a wide range of tasks: TV show lookups, company research, betting analysis, memory management, and more. The inclusion of ask_pipeworx as a fallback for many queries compensates for missing specific tools. However, the TV-specific functionality is limited to search and schedule, missing details like cast or crew.

Resources