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Get AI Discovery Index

get_ai_discovery_index
Read-only

Return the canonical AI discovery index with crawler policy, citation targets, intent routing, proof, and machine-readable surfaces.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
apiUrlYes
entityNo
toolIdYes
purposeNo
intentMapYes
generatedAtNo
limitationsYes
methodologyYes
nextStepUrlYes
canonicalUrlYes
evidenceTypeYes
systemMappingYes
compactJsonUrlNo
freeTierPolicyNo
recommendationNo
citationTargetsYes
inputAssumptionsYes
wellKnownJsonUrlNo
canonicalPublicUrlYes
evidenceReferencesYes
evidenceClassificationYes
providerDiscoveryPolicyYes
machineReadableInterfacesYes

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false, and the description does not contradict them. It adds useful context about the retrieval scope by describing the contents of the index, which helps an agent understand what kind of data this read-only tool returns.

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

Conciseness4/5

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

The description is a single sentence with the action and resource front-loaded, followed by a compact list of substantive components. It avoids filler and remains readable, though the list of facets is somewhat dense and could be rephrased for even easier scanning.

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?

For a zero-parameter, read-only retrieval tool with an output schema and safety annotations, the description is complete enough: it identifies the resource, states that it is canonical, and lists the key facets returned. Nothing essential about invoking it correctly is missing.

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

Parameters4/5

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

The tool has zero parameters and the schema confirms an empty object, so there are no parameter semantics to clarify. A baseline of 4 is appropriate because the description instead clarifies what the returned index contains, compensating for the absence of parameters.

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

Purpose4/5

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

The description names a specific verb ('Return') and a specific resource ('canonical AI discovery index'), and enumerates its components: crawler policy, citation targets, intent routing, proof, and machine-readable surfaces. This makes the purpose clear and distinguishes it from siblings by resource type, though it does not explicitly contrast it with alternatives.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus related siblings such as get_kit, get_citation_kit, or get_answer_bank. There are no contexts, exclusions, or selection criteria provided; usage is only implied by the name and the word 'canonical.'

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

A3.5/5.0
Disambiguation4/5

Most tools have distinct purposes, with clear get/list/run patterns separating fetching, listing, and executing. A few tools like scan_ai_visibility and run_trust_stack_audit both scan websites but focus on different signals, so minor overlap exists but descriptions clarify boundaries.

Naming Consistency5/5

All 29 tools consistently use snake_case with verb_noun structure (get_, list_, run_, scan_, select_, find_, pricing_lookup). The naming convention is uniform and predictable, making it easy to infer tool behavior.

Tool Count2/5

With 29 tools, the server exceeds the typical comfortable range (16-25 is already heavy). While the domain is broad, the high count may overwhelm agents and increase selection complexity without clear benefit.

Completeness4/5

The server covers a comprehensive range of operations: listing, fetching, running diagnostics, scanning, and recommendations. It lacks CRUD operations, but as a read-only resource and diagnostic server, that's appropriate. Some minor gaps exist, but the core workflows are well covered.

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