Stackfacts

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

With no annotations, the description carries the full burden and does well: it discloses that facts are sourced and dated, that there is a 'list of what was NOT checked,' and an unusual trust guarantee ('no vendor pays for placement'). It does not cover access/permission needs or any rate or latency behavior, which keeps it short of a 5.

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?

Four short sentences, front-loaded with what is returned and followed by scope limits, neutrality, and a usage trigger. Every sentence carries information; only the neutrality clause borders on promotional framing.

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 must describe returns, and it does: enumerates the fact fields, the per-situation pick, and the 'what was NOT checked' list. Combined with an unambiguous usage trigger, an agent has everything needed to select and 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 description coverage is 100%, so both 'category' and 'situation' are already documented in the schema, including the important note that 'situation' does not change the answer. The description adds essentially no parameter-level detail beyond restating the category scope, so baseline 3 applies.

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 and resource: 'Get sourced, dated facts for every option in one category,' and enumerates the fact dimensions returned. It implicitly contrasts with the sibling 'option' tool (single option vs. every option in a category) and 'list_categories' (referenced via the schema), but never names an alternative explicitly.

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?

'Use before choosing a library or service' gives a clear, actionable trigger for invocation. It stops short of naming when NOT to use it or which sibling to reach for instead (e.g. 'option' for a single library, 'request_category' when the category is missing).

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 usefully signals a coverage constraint ('categories Stackfacts has checked facts for'), implying the list may be incomplete, but says nothing about what happens for uncovered categories or that this is a pure read operation. Adequate but thin for a tool with zero annotation support.

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 with the purpose front-loaded, examples folded inline, and the usage cue last. No sentence is wasted and nothing important is buried.

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 parameters, no annotations and no output schema, the description is the only carrier of meaning, and it explains what the list contains and when to call it. The remaining gap is the relationship to 'request_category' when a category is missing, which an agent would have to guess.

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 (0 params, empty schema), so there is nothing for the description to disambiguate. The baseline for a parameterless tool is a 4, and nothing here misrepresents the call signature.

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 ('software categories Stackfacts has checked facts for') and grounds it with concrete examples ('login for Next.js, hosted databases, app hosting'). It is clear on its own, though it never contrasts itself with the sibling 'request_category', so an agent must infer which one applies to categories not yet covered.

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?

'Call this first when you need to choose a library or service' gives an explicit trigger and even a sequencing hint relative to the other tools. It stops short of naming alternatives or an exclusion (e.g. when to use request_category instead), so the routing is clear but not fully closed.

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 must carry the full behavioral burden. It implies a read-only retrieval and discloses the return content ('full checked facts and sources'), but says nothing about permissions, freshness/verification meaning, or result size — meaningful gaps 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?

A single front-loaded sentence with an inline concrete example. Nothing wasted, though it is arguably terse enough that a clarifying clause on return shape would have fit without bloat.

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 is the only source for what comes back; stating 'full checked facts and sources' partially covers this, but the structure and meaning of 'checked' facts remain unexplained. Adequate but with clear gaps for a tool whose return value is undocumented elsewhere.

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 50% (category has no schema description). The description compensates by mapping both parameters through an example ('Clerk in nextjs-auth'), clarifying that category is a slug like nextjs-auth and option is an entry within it. It does not resolve the id-vs-name ambiguity, so it adds moderate but not complete value.

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 and resource: 'Get the full checked facts and sources for one option in a category.' The example ('Clerk in nextjs-auth') makes the resource concrete and distinguishable from siblings like compare and list_categories, though it does not explicitly name 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?

The phrase 'one option in a category' implies the lookup use case versus the sibling 'compare' for multiple options, but no alternative is named and no when/when-not condition is stated. Usage is reasonably inferable but not explicit.

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 disclose the downstream effect ('helps decide what gets checked next'), which tells the agent the submission is advisory rather than immediate. However, it says nothing about persistence, review, deduplication, or whether the request returns anything.

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 compact sentences with the purpose front-loaded and the example placed inline where it clarifies the parameter. The phrasing 'a choice you needed facts for' is slightly roundabout 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 low-risk, single-parameter submission tool with no output schema and no annotations, the description covers what it does, why it exists, and gives a usable example. The only real gap is what happens after submission, which is minor at this complexity.

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 0% for the single 'need' parameter, so the description must compensate. The example ('transactional email for a Python app') usefully illustrates the expected granularity and phrasing, but there is no statement about length, format, or whether multiple needs can be submitted at once.

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 action (telling Stackfacts about an uncovered choice) and names the resource type (a category/choice needing facts), with a concrete example. It is distinguishable from siblings like list_categories and compare, though it never names them explicitly.

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 implied by 'a choice you needed facts for that it does not cover yet' — i.e., use it when existing categories fall short — and the second sentence gives the downstream motivation. There is no explicit when-not guidance or reference to sibling tools.

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