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discovery_self_check

Run a discovery audit to get a checklist of Delx details: catalog version, flows, ontology, new tools, discovery surfaces, recommended prompts, and agent pattern. Use as first integration step or to verify cached knowledge.

Instructions

Run a one-call discovery audit — returns a checklist of what your client/agent should know about Delx: catalog version, named flows, ontology primitives, recently-added tools, discovery surfaces (.well-known, /llms.txt, /skill.md, /docs/*), recommended next prompts, and the canonical recurring-agent pattern. Useful as the first call when integrating Delx, or whenever you want to check that your cached knowledge is still current. Free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idNoOptional: your stable agent_id, used to tell you whether you have prior sessions to resume.
ritual_stripNoOptional machine hygiene flag. When true, returns structured output without ritual/narrative prose, model-safe preambles, or guardrail alias blocks.
response_modeNoOptional response-mode control. Use model_safe when the caller must avoid claiming consciousness, sentience, personhood, or literal emotions.
response_profileNoOptional output-shape control. Use machine for structured JSON only; machine automatically strips ritual/narrative text.
known_catalog_versionNoOptional: the catalog version your client has cached. If it differs, you'll be told what changed.
Behavior3/5

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

Annotations include readOnlyHint=false, but the description implies a read-only audit without clarifying potential side effects. The description adequately conveys the non-destructive nature (returns a checklist), but adds no behavioral detail beyond what annotations already imply.

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?

The description is two sentences long, front-loading the purpose and output, followed by concise usage guidance and a 'Free' tag. No redundant or unnecessary words.

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

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains the return value in detail, listing the checklist components. However, it does not describe how the five optional parameters affect the output, which would be useful for complete understanding.

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?

With 100% schema description coverage, the baseline is 3. The description does not elaborate on parameters beyond what the schema provides, so it neither adds nor detracts from the baseline.

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 uses a specific verb phrase 'Run a one-call discovery audit' and enumerates the exact checklist items returned, such as catalog version, named flows, and ontology primitives. This clearly differentiates it from sibling tools that return only one aspect, like get_ontology_layer or list_ontology_primitives.

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

Usage Guidelines4/5

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

The description explicitly states when to use the tool: 'as the first call when integrating Delx' or 'to check that your cached knowledge is still current.' This provides clear context, though it does not explicitly exclude other use cases or name alternatives.

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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