Dikshant Rajput's portfolio agent

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

Annotations only declare readOnlyHint and openWorldHint=false; the description adds substantive behavior beyond them — responses are written in the first person as Dikshant, sourced only from his public record, come with relevant links attached, and each call is stateless. The statelessness and sourcing constraints are exactly the kind of traits an agent needs and cannot infer from the annotations.

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?

Three sentences, front-loaded with what the tool is and what it answers about, then the response style, then the calling constraints. No filler and nothing repeated from the schema beyond the necessary 500-character reminder.

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?

With a single required parameter, no output schema, and minimal annotations, the description carries the remaining burden well: it describes the response format (first-person, public-record-only, link-attached) and the per-call constraints. An agent has everything needed to invoke it correctly.

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% and the schema already documents the single question parameter with a maxLength of 500 and an example. The description adds meaning on top of that by requiring the question to be self-contained and by noting statelessness, which tells the caller how to phrase the parameter rather than just its type and length.

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 pairs a specific verb (ask) with a specific resource (Dikshant Rajput's AI agent) and enumerates the topic scope: work, projects, experience, skills, availability, hiring. It is unambiguous about what the tool returns, though it never contrasts itself with the topically overlapping siblings list_experience and list_projects, so an agent cannot tell from this text alone when to prefer freeform Q&A over a structured listing.

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?

It gives concrete operational guidance: one self-contained question per call, 500-character cap, and an explicit warning that calls are stateless so context must be included. That is clear usage context, but there is no routing guidance against the sibling tools (get_contact, list_experience, list_projects), which the description never mentions.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds essentially nothing behavioral beyond that — no indication of what fields are returned, freshness of the availability data, or any constraints.

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 short sentences with no filler, and the core purpose is front-loaded. The trailing channel preference is minor but not wasteful.

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?

For a trivial zero-param read with no output schema, the description is minimally sufficient — it names the resource returned, but does not sketch the shape of the response (e.g., which contact fields, how availability is expressed), which is the main gap given there is no output schema to fall back on.

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?

The tool takes no parameters and the schema is empty, so there is nothing for the description to disambiguate. Baseline 4 applies for a zero-parameter tool.

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?

States a specific verb ('Get') and resource ('contact details and hiring availability') for a named individual, so the agent knows exactly what it returns. It is distinguishable from siblings such as list_experience and list_projects, but doesn't explicitly contrast itself with them.

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?

There is no guidance on when to use this tool versus the siblings (ask_dikshant, list_experience, list_projects). The only usage-adjacent note is 'Email is the preferred channel,' which is a contact preference, not a selection criterion.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds genuinely new behavioral context: the output is structured (deterministic, no LLM) and it enumerates the exact returned fields, which matters since there is no output schema.

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?

A single sentence, front-loaded with verb and resource, then the returned fields. No filler; every clause 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?

With no input params and no output schema, the description compensates by listing the returned fields, which is enough for an agent to call and interpret the result. It could have added pagination or ordering behavior, but little else is missing.

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?

The tool takes no parameters, so per the rubric this is a baseline 4. There is nothing for the description to clarify on the input side.

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 gives a specific verb (List) plus the resource (Dikshant Rajput's roles) and enumerates the exact fields returned, so an agent knows precisely what this retrieves. It lacks an explicit contrast to siblings like ask_dikshant, though 'No LLM involved' implicitly separates it from that tool.

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?

Usage is only implied: 'No LLM involved' hints this is the deterministic retrieval path versus the LLM-backed ask_dikshant, but the description never states when to choose this over ask_dikshant or get_contact. No prerequisites or exclusions are given.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. With no output schema, the description earns credit by disclosing the returned fields (name, tagline, stack, highlights, traction, links) and clarifying the data is deterministic with no LLM. A bit more on return shape would push it higher.

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 tight sentences, front-loaded with the verb and resource, then the output fields. Every clause earns its place with no redundancy.

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?

For a no-param, read-only list tool with no output schema, the description covers purpose, returned fields, and the no-LLM behavioral note. It omits only minor details like the container structure (e.g., array of projects) and any ordering, which an agent could reasonably infer.

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?

The tool takes zero parameters and the schema is empty, so there is nothing to document; the baseline for a 0-param tool applies. The description correctly implies no filtering or input is needed.

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?

States a specific verb (List) and resource (Dikshant Rajput's projects) and enumerates the exact fields returned. An agent can distinguish it from siblings like list_experience or get_contact without opening any schema.

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?

"No LLM involved" implicitly signals this is the raw-data alternative to ask_dikshant, which is useful routing context. However, it never explicitly states when to prefer this over ask_dikshant or the other siblings, so the guidance remains inferred rather than stated.

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