Use this when ChatGPT needs the full public text for a specific EntangleEQ page returned by search. Never returns private company or employee data.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Public page id returned by search. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that it returns only public text and never private data, but does not mention read-only nature, side effects, authentication, or error handling. Adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no redundant information. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool (1 param, no output schema, no annotations), the description is mostly complete. It tells when to use, what it returns, and what it excludes. Lacks specifics about return format but sufficient for a fetch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter 'id' with description. The tool description adds minimal extra meaning beyond 'Public page id returned by search.' Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'fetch', the resource 'full public text for a specific EntangleEQ page', and distinguishes from sibling tools like search. It also notes what it does not return.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly specifies when to use ('when ChatGPT needs the full public text...') and what it does not return (private data). It implies usage after search but lacks explicit alternatives or when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: readiness assessment, page fetching, demo request, search, and index simulation. No ambiguity or overlap.
Naming is mixed: some use snake_case (assess_readiness, simulate_index_2027), others are single words (fetch, search) or verb_noun (request_demo_lead). While readable, the pattern is inconsistent.
Five tools is well-scoped for the domain of pay transparency and demo requests. Each tool earns its place without redundancy.
The tool surface covers the key workflows: readiness check, public information retrieval (search + fetch), demo request, and index simulation. No obvious gaps for the stated purpose.
