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Yc Rejection

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

TDQS

A4.5/5.0
Behavior4/5

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

Builds on annotations by detailing return format (per-model scores, combined view) and BYO key behavior for Anthropic. Adds value beyond the readOnlyHint and idempotentHint annotations.

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?

Four concise sentences with front-loaded main action. Every sentence adds value: purpose, default behavior, extended usage, and return format. No fluff.

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 sufficiently describes return structure. Covers all parameters and use cases. No gaps given the tool's complexity and rich annotations.

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 covers all 4 parameters (100%). Description adds clarity on default model, _apiKey pass-through, and context disambiguation, complementing the schema effectively.

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?

Description uses a specific verb (probe) and resource (LLMs for business/brand/product/topic visibility), clearly differentiating from sibling tools like ask_pipeworx (Q&A) or scan_competitor_ai_presence. Includes default model and optional Anthropic integration.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

States clear use cases (marketing audits, pre-launch checks, monitoring) and explains when _apiKey is needed. Could be improved by explicitly mentioning when not to use or comparing with siblings like scan_competitor_ai_presence.

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

All Yc Rejection toolsInspect
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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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