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Asana

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").

TDQS

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, etc.), the description details multi-source fan-out, fallback mechanisms, and soft-fail behavior (USPTO PatentsView API sunset). This gives the agent comprehensive understanding of the tool's internal behavior and failure modes.

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 front-loaded with example queries and organized into sources, fallback, and return format. While comprehensive, it is slightly verbose but still efficient given the 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?

The tool has no output schema, but the description explicitly mentions return structure ('structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs'). Combined with fallback and error behavior, the description is thorough and self-sufficient.

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%, but the description adds practical usage guidance: e.g., 'Use "30d" or "1m" for typical monitoring' for 'since', and explains 'value' can be ticker or CIK. This adds semantic value beyond the schema's brief descriptions.

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 explicitly states it provides a 'change feed for a company in the last N days/weeks/months in ONE parallel call' and clarifies it aggregates SEC, news, and patents. It distinguishes from the sibling tool 'entity_profile' which provides static profiles, fulfilling the need for differentiation.

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?

Description offers clear guidance on when to use this tool vs. alternatives: 'Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.' It also explains fallback behavior (GDELT→GNews) and provides parameter usage examples.

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

B3.3/5.0
Disambiguation2/5

The tool set mixes Asana project management tools with a large number of Pipeworx data retrieval tools. Within the Pipeworx subset, tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded have overlapping purposes, and multiple prediction market tools exist (e.g., polymarket_arbitrage, polymarket_edges). This creates ambiguity and potential for misselection.

Naming Consistency2/5

Tool names lack a consistent pattern. Some use 'asana_' prefix, others use descriptive phrases (e.g., 'compare_entities', 'generate_llms_txt'), and some are single verbs (e.g., 'forget', 'remember'). Mix of different conventions leads to unpredictability.

Tool Count2/5

37 tools is high for a server named 'Asana', yet only 6 tools are Asana-specific. The majority are Pipeworx tools unrelated to Asana. This overloading makes the server feel bloated and off-purpose.

Completeness2/5

For Asana functionality, the set is incomplete: missing update/delete task, project management features, etc. The Pipeworx tools are extensive but irrelevant to the server's stated purpose, leaving the Asana workflow with notable gaps.

Resources