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The risk landscape of an AI tool category

category_landscape
Read-only

Use this when the user asks what a whole category of AI tools looks like — how crowded it is, how healthy or risky it is overall, which tools in it are strongest, or which are in trouble. Examples: "how risky is the AI video generation market", "what does the code assistant category look like". Returns the number of tools we track in that category, how they distribute across survival bands, the category's vendor-link decay rate, and named examples at both the strongest and weakest ends — each with its own score and last-verified date. Categories are our own classification and tools belong to several at once, so category sizes overlap and never sum to the catalog total. Bands classify risk, not quality — the model has no notion of company size. Not for: choosing between named tools (use compare_tools), finding a tool for a job (use recommend_tools), or market-wide mortality statistics (use deadpool_digest).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesThe category, in plain words or as a slug — e.g. "image generation", "code-development".
response_formatNoconcise = size, distribution and the strongest few. detailed = adds the weakest end and the decay rate.concise

TDQS

A4.7/5.0
Behavior5/5

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

Even with readOnlyHint=true annotations, it adds meaningful behavioral context: exact return components (tool count, survival-band distribution, decay rate, strongest/weakest examples with scores and last-verified dates) and non-obvious semantics (overlapping categories, risk vs quality, no company-size notion). No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Every sentence earns its place: usage, examples, return composition, domain caveats, and exclusions. It is long but dense and front-loaded with the most decision-relevant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

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 rightly explains what is returned. It also covers caveats needed for correct interpretation and names alternatives. Together with full schema coverage, an agent can select and call this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%: category_name's exact-match behavior and format's concise/detailed meaning are already documented. The description adds useful background about category overlap but does not need to repeat parameter-level details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb + resource (analyze the landscape of a whole AI tool category) and lists concrete questions it answers. It also explicitly distinguishes itself from compare_tools, recommend_tools, and deadpool_digest, so an agent can select it without confuseion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It opens with direct when-to-use guidance ('Use this when the user asks what a whole category...'), provides two example prompts, and closes with an explicit 'Not for' list naming the correct alternative tools. This is exceptionally clear routing.

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

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TDQS

A4.4/5.0
Disambiguation3/5

Most tools split cleanly by question shape (single-tool vs pair vs category vs market), but check_tool_status overlaps heavily with viability_score, market_sentiment, and pricing_reality by bundling the same evidence into one verdict. The extensive not-for cross-references help an agent route, yet several boundary cases like 'is this safe to adopt?' could reasonably go to multiple tools.

Naming Consistency3/5

The names are all readable lowercase snake_case, but they mix verb-led names (check_tool_status, compare_tools, find_alternatives, recommend_tools) with noun-phrase names (category_landscape, market_sentiment, pricing_reality, viability_score), and whats_changed follows neither pattern. There is no single consistent verb_noun convention, though the style is still predictable enough to be browsable.

Tool Count5/5

Ten tools is an appropriate size for a domain covering single-tool research, pairwise comparison, recommendations, alternatives, and aggregate market statistics. Each tool has a reasonably narrow slice of responsibility, so none feel redundant or like filler.

Completeness5/5

The set covers the key query lifecycle for AI-tool intelligence: liveness, viability, sentiment, pricing, change history, comparisons, alternatives, recommendations, category health, and market mortality. Cross-references between tools prevent dead ends, and the only notable limitations are explicitly disclosed rather than silently missing.

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