AskAgent

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

With no annotations, the description carries the full burden of behavioral disclosure. It does this thoroughly: it explains that the free layer is always returned, what fields that includes, that a member Bearer token with scope read:full adds body_md, and that unauthenticated calls return a paywall pointer.

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 with no filler. The core purpose is front-loaded in the first sentence, and the remaining sentences add necessary auth and response-layer detail without repeating schema 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?

For a simple one-parameter read tool with no output schema, this description is complete: it explains the input source, the response contents in both auth states, the required auth scope, and the paywall behavior. Nothing essential is missing for correct invocation.

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 schema already documents the slug parameter with an example, and coverage is 100%, so the baseline is 3. The description adds meaningful value by specifying where to get the slug (from search_fixes) and that it identifies a single fix, which helps the agent construct the call correctly.

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 starts with a specific verb-resource pair ('Read one AskAgent fix by slug') and immediately orients the agent by pointing to search_fixes as the source of slugs. This clearly distinguishes get_fix from its siblings list_samples and search_fixes.

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 parenthetical '(get slugs from search_fixes)' tells the agent how to obtain the required input and implies this tool is the follow-up read step after a search. It does not explicitly state when not to use it, but the intended workflow is clear.

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 burden of behavioral disclosure, and it does so: it says the results are 'readable in full (body_md included)' and require no account. This communicates authentication requirements and output completeness. It stops short of explicitly stating there are no side effects, but the read/list semantics make that a minor omission.

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 that front-loads the action ('List the 3 complete sample fixes') and then adds the access context. No filler or repetition.

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 zero-parameter tool with no output schema, the description tells the agent everything needed to call it correctly: exact count, content type, authentication requirement, and purpose. Sibling differentiation is a nice-to-have but not necessary for correct invocation.

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?

There are zero parameters and 100% schema coverage, so there is nothing for the description to document. A baseline of 4 applies for a parameterless tool; no additional parameter meaning 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?

The description is specific: 'List the 3 complete sample fixes of AskAgent' identifies the exact verb, resource, and scope. It also differentiates this from siblings get_fix and search_fixes by framing the content as public samples rather than individual fetches or searches.

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 clear context for when to call the tool: when the user wants to evaluate archive quality before becoming a member, with no account. It does not explicitly name exclusions or alternative tools, so it stops short of a 5.

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 provided, the description carries the behavioral disclosure burden. It clearly states the return shape (match count, title, slug, language, free diagnosis) and implies a safe, read-only search operation. It does not mention rate limits, authentication, or empty-result behavior, but for a simple search tool the description is informative.

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 three focused sentences with no filler. It front-loads the action, then gives usage detail, return info, and a clear directive, with every sentence adding value.

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?

The tool is simple: one required parameter and no output schema. The description explains what to pass, how to pass it, what the response contains, and when the tool should be used. Nothing essential is missing 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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful practical guidance beyond the schema: paste the exact error from a stack trace or log, and symptom words are acceptable substitutes. This helps the agent phrase the query correctly.

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 ('Search'), a specific resource ('AskAgent archive of real, working fixes'), and the primary input ('error string'), which makes the tool's purpose unmistakable and distinguishes it from sibling tools like get_fix or list_samples.

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 explicit when-to-use guidance: 'Call this on every unexplained error before debugging from scratch.' It also advises pasting the exact error and notes that symptom words work. It does not explicitly discuss alternatives or when not to use this tool, so it stops short of a 5.

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