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Glama

Microsoft Todo

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?

Beyond annotations, the description details internal fan-out to SEC EDGAR, GDELT/GNews fallback, and USPTO with soft-fail behavior, plus return structure (changes[], total_changes, citation URIs). No annotation contradictions.

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 well-structured with examples first, then source details, then parameters, and differentiation. It is slightly verbose for a tool description but every sentence contributes meaningful information.

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?

Comprehensive for a complex tool with multiple data sources and no output schema: describes return format, source behaviors, rate-limit fallbacks, and window formats. Fully covers what an agent needs to invoke correctly.

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 valuable context: relative date shorthand formats, example ticker/CIK inputs, and recommendation for typical monitoring window ('30d' or '1m').

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 returns a change feed for a company over a recent window, using specific verbs like 'What's new with X' 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?

The description provides explicit when-to-use guidance with example queries and directly tells the agent to use entity_profile for static profiles, offering clear context on tool selection.

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

All Microsoft Todo toolsInspect
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TDQS

B3.4/5.0
Disambiguation1/5

The set is dominated by overlapping Pipeworx and prediction-market tools, with ask_pipeworx and ask_pipeworx_beta explicitly described as currently identical, and ask_pipeworx_grounded, deep_research, and discover_tools serving heavily overlapping lookup purposes. The four Microsoft To Do tools are buried among 31 unrelated tools, making it very hard for an agent to select the right tool for the server's apparent domain.

Naming Consistency2/5

Most names are snake_case, but the naming conventions are otherwise mixed: there are verb_noun tools like list_tasks and get_task, bare verbs like remember/forget/recall, prefixed families like polymarket_*, and noun-phrase names like entity_profile, recent_changes, and pipeworx_trending. The lack of a consistent pattern across the set, especially relative to the Microsoft To Do server name, makes naming unpredictable.

Tool Count1/5

35 tools is already heavy, but the bigger problem is that only 4 of them are for Microsoft To Do, the server's stated name and purpose. The remaining 31 tools belong to unrelated Pipeworx data, prediction-market, and memory-management domains, which is an extreme scope mismatch.

Completeness1/5

For a Microsoft To Do server, the surface is severely incomplete: list_task_lists, list_tasks, get_task, and find_due_tasks cover reading and browsing only, with no create, update, complete, or delete operations. Even the broader Pipeworx functionality is scattered and redundant rather than forming a coherent domain surface.

Resources