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Yc Rejection

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?

Describes fan-out to multiple sources (SEC, GDELT/GNews fallback, USPTO soft-fail), explains the return structure ('changes grouped by source + total_changes + pipeworx:// URIs'), and details the `since` parameter behavior. Adds significant context beyond the annotations (readOnlyHint, idempotentHint, etc.).

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 query examples and a clear overall purpose, but it is a single dense paragraph. Could be broken into sections for readability, though every sentence serves a function with minimal 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?

Without an output schema, the description still explains the return format (grouped changes, counts, URIs) and covers edge cases (GNews fallback, USPTO soft-fail). Combined with sibling distinction and parameter details, it provides all needed context for correct invocation.

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 basic descriptions, but the tool description enriches by explaining accepted formats for `since` (ISO date vs relative shorthand with examples) and clarifying that `value` can be a ticker or zero-padded CIK. Adds non-obvious nuance.

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 specifies a clear verb+resource: 'change feed for a company' with concrete query examples ('What's new with X', 'latest on Y'), and distinguishes itself from the sibling tool 'entity_profile' by stating when to use the other instead.

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?

Explicitly contrasts with 'entity_profile' for static profiles, and provides contextualized usage through examples like 'updates on Acme'. Gives clear direction on when to choose this tool over an alternative.

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

Many tools have overlapping purposes, e.g., multiple ask_pipeworx variants and several Polymarket analysis tools. The presence of meta-tools like discover_tools and suggest_questions adds confusion. Distinguishing between tools like entity_profile, compare_entities, and recent_changes requires careful reading of descriptions.

Naming Consistency2/5

Naming conventions are mixed: some use snake_case (ai_visibility_check, ask_pipeworx), others use underscores (compare_entities, deep_research). Prefixes like pipeworx_ and polymarket_ are inconsistently applied, and there is no clear verb_noun pattern across the set.

Tool Count2/5

With 32 tools, the server is heavily over-scoped for its name 'Yc Rejection'. Only one tool directly relates to that domain. The rest constitute a full data platform, making the count far too high for the implied narrow purpose.

Completeness1/5

For a server named 'Yc Rejection', the tool set is severely incomplete: only one tool generates rejection text. There are no tools for application management, review, or related tasks. The actual completeness of the underlying platform is irrelevant given the misleading name.

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