Agent Toolbox

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

No annotations are provided, so the description carries the full burden. It mentions the output formats, which is useful, but omits any constraints, error behavior, input validation requirements, or return structure.

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?

One front-loaded sentence with zero filler. Every word contributes directly to the core purpose.

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 annotations, no output schema, and zero parameter descriptions, the agent lacks critical calling details (e.g., what 'name' and 'description' mean, what the return payload looks like). The single sentence covers only the core transformation.

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 description coverage is 0% for all three parameters. The description never mentions 'name', 'schema', or 'description', so their individual roles (e.g., what 'name' labels or what the 'description' field is for) remain completely undocumented.

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?

Specific verb 'Convert' and target resource 'JSON Schema' plus explicit output formats (OpenAI, Anthropic, MCP). The purpose is immediately clear and disambiguates from the unrelated sibling tools.

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 when-to-use, when-not, or alternative guidance is provided. The description only states what the transformation does, leaving the agent to infer the appropriate context on its own.

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?

With no annotations, the description carries the full behavioral burden. It does disclose a valuable trait — pricing list data 'verified 2026-10-16' — but says nothing about which specific models are covered, whether a network/model call is made, or whether results are cached or approximate.

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 front-loaded sentence with the core action first and the freshness caveat in a parenthetical. No wasted wording, though the date could be trimmed without losing much.

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?

No output schema and no annotations, so the description is the only spec. It establishes what is returned at a high level (token count and USD cost) but omits model coverage, the role of output_tokens, and precision/assumption details an agent would need to call it confidently.

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 description coverage is 0%, so the description must compensate for both parameters. It only implicitly covers 'text' (the thing being estimated) and never explains 'output_tokens', which is non-obvious and presumably drives the cost estimate. The gap is significant for the only non-self-evident parameter.

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 (estimate) and two concrete resources (tokens, USD cost) scoped to a given text and a set of frontier models. The sibling tools (convert_schema, mcp_config, search_directory) are in unrelated domains, so confusion is unlikely even without an explicit contrast.

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 mention of 'USD cost ... across current frontier models' implies the use case (cost/size budgeting before sending a prompt), but there is no explicit when-to-use, when-not-to-use, or prerequisite statement. Usage must be inferred from the resource description.

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?

No annotations are provided, so the description carries the full behavioral burden. It does not disclose whether the config is returned as text, written to disk, whether it merges with existing config, or any permission/side-effect profile — leaving ambiguous whether this mutates state.

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?

The single sentence is front-loaded and free of waste, which is good. However, for a 5-parameter tool with a nested env object it is undersized, so conciseness here shades into under-specification.

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?

This is a complex tool (5 params, a nested object, one required field, no output schema) and the description says almost nothing about its inputs or behavior. It is not remotely complete enough for an agent to call 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?

With 5 parameters at 0% schema description coverage, the description must compensate but adds nothing about name, command, args, url, or env, or which are required. It only weakly implies these are MCP server config fields.

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 states a specific verb (Generate) and a specific resource (an mcpServers config block), so an agent knows what the tool produces. It offers no differentiation from the sibling tools (convert_schema, estimate_tokens, search_directory), which keeps it below a 5.

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, no prerequisites, and no mention of alternatives. The description is purely a restatement of the action with no contextual cues.

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?

No annotations are provided, so the description carries the full disclosure burden. It implies a safe read operation, but does not describe matching behavior (keyword vs semantic), result limits, pagination, or return shape.

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 front-loaded sentence with no filler. It is efficient, though the brevity is partly under-specification rather than disciplined concision.

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 two-parameter tool with no annotations, no output schema and zero schema descriptions, the description is too thin. An agent cannot tell what fields are searched, how results are returned, or how category constrains the search.

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 description coverage is 0% and neither parameter is explained. The word "search" loosely hints that query is a search term, but category's accepted values and whether both params are optional are left entirely undocumented.

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 ("Search") and the resource ("the Agent Toolbox directory of AI agent tools"), which clearly separates it from siblings like convert_schema, estimate_tokens and mcp_config. It stops short of saying what a search returns or what fields are matched, so it is clear but not maximally precise.

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 statement of when to use this tool, no prerequisites, and no mention of alternatives for discovering tools. The agent must infer usage entirely from the name.

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