Agent Permissions Index

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

With no annotations, the description carries the full burden, and it does disclose one meaningful trait: data comes from a weekly snapshot, so results are not live. It says nothing about auth, rate limits, caching, or the granularity of the aggregates.

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 or redundancy. It is tight, though the brevity leaves the reader wanting more substance rather than being over-long.

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 exists, so the description is the only guide to return values, and 'headline aggregate numbers' does not say which metrics or dimensions come back. For a zero-parameter stats tool the gaps are modest but real.

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, so the baseline of 4 applies; there is nothing for the description to explain beyond the call semantics.

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 names a specific resource (headline aggregate numbers from the Agent Permissions Index) and the data source (public AI agent configs on GitHub). It implicitly separates itself from get_leaderboard by advertising aggregates rather than rankings, though the sibling differentiation is left to inference.

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 call this instead of get_leaderboard or grade_config, and no prerequisites or context for use. The reader must infer usage from the one-line purpose.

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 adds some context by noting rankings include sample sizes and method, which hints at a read-only, statistically transparent retrieval. However, it does not explicitly state read-only/no side effects, auth requirements, or pagination behavior, leaving gaps for a no-annotation tool.

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 description is a single, front-loaded sentence with no filler. Every phrase — ranked, safety of public configs, sample sizes, method — adds useful information for a tool with no parameters.

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 simple, zero-parameter tool with no output schema and no annotations, the description gives enough context to understand what is returned: ranked agent tools with sample sizes and method. It could be slightly more complete by clarifying what a 'leaderboard' entry contains or what 'public configs' means, but overall it is adequate.

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 input schema has zero parameters, so the baseline for parameter semantics is 4. There are no parameter names, types, or meanings needing description, and the description does not need to compensate for schema gaps.

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 clear resource: agent tools ranked by safety of public configs, with sample sizes and method. It distinguishes the tool from generic stats (get_index_stats) and grading (grade_config) by describing the ranked leaderboard concept, but does not explicitly name or differentiate from those siblings.

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 guidance is provided, nor are any alternatives mentioned. The description does not explain when an agent should choose this leaderboard over get_index_stats or grade_config; usage is only implied by the tool name and concept.

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 burden, and it does meaningful work: it discloses the return artifacts (letter grade, findings, a safer config, README badge Markdown) and a privacy behavior ('Inputs are not stored or logged'). It does not explicitly state there are no side effects on the caller's config or describe failure behavior on malformed JSON, but overall it exceeds the minimum for an unannotated tool.

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 short sentences, front-loaded with purpose, followed by return values and the privacy note. No filler; every sentence adds information.

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?

There is no output schema, so the description appropriately explains the return values and adds a privacy guarantee. It is nearly complete for a two-parameter analysis tool; only edge-case behavior (invalid JSON, grading criteria) is unaddressed, which is acceptable here.

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 100%, so both parameters are already documented, making 3 the baseline. The description adds some value by naming the config file formats, but it lists only three of the four enum values and omits cursor_mcp, so it does not fully clarify the 'tool' parameter's option set.

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 names a specific verb ('Grade'), a precise resource ('AI coding agent permissions config'), and lists concrete example files (Claude Code settings.json, Gemini CLI settings.json, mcp.json). An agent can immediately tell this is an analysis tool for agent configs, which is clearly distinct from the sibling tools get_index_stats and get_leaderboard.

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 gives clear context on what inputs are accepted and what the tool produces, so an agent knows when to reach for it. It stops short of explicit when-not conditions or naming alternatives, but the sibling tools are unrelated in domain, so this is a minor gap.

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